complexity of stability and controllability of elementary hybrid systems in this paper we consider simple classes of nonlinear systems and prove that basic questions related to their stability and controllability are either undecidable or computationally intractable np hard as a special case we consider a class of hybrid systems in which the state space is partitioned into two halfspaces and the dynamics in each halfspace correspond to a different linear system

controllability and stabilizability of a class of continuous piecewise affine dynamical systems this paper studies controllability and stabilizability of continuous piecewise affine dynamical systems which can be considered as a collection of ordinary finite dimensional linear input state output systems together with a partition of the product of the state space and input space into full dimensional polyhedral regions each of these regions is associated with one particular linear system from the collection the main results of the paper are popov belevitch hautus type necessary and sufficient conditions for both controllability and stabilizability of such systems

optimization of lyapunov invariants in verification of software systems the paper proposes a control theoretic framework for verification of numerical software systems and puts forward software verification as an important application of control and systems theory the idea is to transfer lyapunov functions and the associated computational techniques from control systems analysis and convex optimization to verification of various software safety and performance specifications these include but are not limited to absence of overflow absence of division by zero termination in finite time absence of dead code and certain user specified assertions central to this framework are lyapunov invariants these are properly constructed functions of the program variables and satisfy certain properties analogous to those of lyapunov functions along the execution trace the search for the invariants can be formulated as a convex optimization problem if the associated optimization problem is feasible the result is a certificate for the specification

brief equivalence of hybrid dynamical models this paper establishes equivalences among five classes of hybrid systems mixed logical dynamical mld systems linear complementarity lc systems extended linear complementarity elc systems piecewise affine pwa systems and max min plus scaling mmps systems some of the equivalences are established under rather mild additional assumptions these results are of paramount importance for transferring theoretical properties and tools from one class to another with the consequence that for the study of a particular hybrid system that belongs to any of these classes one can choose the most convenient hybrid modeling framework

continuous piecewise affine dynamical systems do not exhibit zeno behavior in the context of continuous piecewise affine dynamical systems we study the zeno behavior i e infinite number of mode transitions in finite time interval in this note the main result reveals that piecewise affine dynamical systems do not exhibit zeno behavior a direct benefit of the main result is that one can apply smooth ordinary differential equations theory in a local manner for the analysis of piecewise affine systems

kernel methods in machine learning we review machine learning methods employing positive definite kernels these methods formulate learning and estimation problems in a reproducing kernel hilbert space rkhs of functions defined on the data domain expanded in terms of a kernel working in linear spaces of function has the benefit of facilitating the construction and analysis of learning algorithms while at the same time allowing large classes of functions the latter include nonlinear functions as well as functions defined on nonvectorial data we cover a wide range of methods ranging from binary classifiers to sophisticated methods for estimation with structured data

learning prototype hyperplanes for face verification in the wild in this paper we propose a new scheme called prototype hyperplane learning phl for face verification in the wild using only weakly labeled training samples i e we only know whether each pair of samples are from the same class or different classes without knowing the class label of each sample by leveraging a large number of unlabeled samples in a generic data set our scheme represents each sample in the weakly labeled data set as a mid level feature with each entry as the corresponding decision value from the classification hyperplane referred to as the prototype hyperplane of one support vector machine svm model in which a sparse set of support vectors is selected from the unlabeled generic data set based on the learnt combination coefficients to learn the optimal prototype hyperplanes for the extraction of mid level features we propose a fisher s linear discriminant like fld like objective function by maximizing the discriminability on the weakly labeled data set with a constraint enforcing sparsity on the combination coefficients of each svm model which is solved by using an alternating optimization method then we use the recent work called side information based linear discriminant sild analysis for dimensionality reduction and a cosine similarity measure for final face verification comprehensive experiments on two data sets labeled faces in the wild lfw and youtube faces demonstrate the effectiveness of our scheme

dead beat control of simple hammerstein models dead beat controllers for simple hammerstein systems are investigated several designs for nonminimum time state dead beat controllers are given for certain classes of simple hammerstein systems a general minimum time state dead beat controller is presented for a class of simple hammerstein systems a design for a family of minimum time control laws is provided this enables to a certain extent shaping of transient response via choosing an appropriate control law finally the authors design an output dead beat controller for a class of hammerstein systems that are not necessarily state dead beat controllable

the landau hall problem on canal surfaces abstract in this note we study the landau hall problem on a generalized canal surface and classify all uniform magnetic trajectories of a charged particle moving on such a surface under the action of a uniform magnetic field

survey constrained model predictive control stability and optimality model predictive control is a form of control in which the current control action is obtained by solving at each sampling instant a finite horizon open loop optimal control problem using the current state of the plant as the initial state the optimization yields an optimal control sequence and the first control in this sequence is applied to the plant an important advantage of this type of control is its ability to cope with hard constraints on controls and states it has therefore been widely applied in petro chemical and related industries where satisfaction of constraints is particularly important because efficiency demands operating points on or close to the boundary of the set of admissible states and controls in this review we focus on model predictive control of constrained systems both linear and nonlinear and discuss only briefly model predictive control of unconstrained nonlinear and or time varying systems we concentrate our attention on research dealing with stability and optimality in these areas the subject has developed in our opinion to a stage where it has achieved sufficient maturity to warrant the active interest of researchers in nonlinear control we distill from an extensive literature essential principles that ensure stability and use these to present a concise characterization of most of the model predictive controllers that have been proposed in the literature in some cases the finite horizon optimal control problem solved on line is exactly equivalent to the same problem with an infinite horizon in other cases it is equivalent to a modified infinite horizon optimal control problem in both situations known advantages of infinite horizon optimal control accrue

brief paper a full characterization of stabilizability of bimodal piecewise linear systems with scalar inputs this paper studies open loop stabilization problem for bimodal systems with continuous vector field it is based on the earlier work of the authors on the controllability problem for the same class of systems a full characterization of stabilizability is established by presenting algebraic necessary and sufficient conditions it is also shown that controllability implies stabilizability for these systems in a very similar fashion to the linear case

valid inequalities for mixed integer linear programs this tutorial presents a theory of valid inequalities for mixed integer linear sets it introduces the necessary tools from polyhedral theory and gives a geometric understanding of several classical families of valid inequalities such as lift and project cuts gomory mixed integer cuts mixed integer rounding cuts split cuts and intersection cuts and it reveals the relationships between these families the tutorial also discusses computational aspects of generating the cuts and their strength

improved algorithm for mixed integer quadratic programs and a computational study in a recent paper 6 we suggested an algorithm for solving complicated mixed integer quadratic programs based on an equivalent formulation that employs a nonsingular transformation of variables the objectives of the present paper are two first to present an improved version of this algorithm which reduces substantially its computational requirements second to report on the results of a computational study with the revised algorithm

controllability of piecewise linear systems abstract the paper deals with a discontinuous control system that consists of two finite dimensional linear systems the first describes the motion in a given half space the second in the complementary half space necessary and sufficient conditions for local controllability are obtained

normalized cuts and image segmentation we propose a novel approach for solving the perceptual grouping problem in vision rather than focusing on local features and their consistencies in the image data our approach aims at extracting the global impression of an image we treat image segmentation as a graph partitioning problem and propose a novel global criterion the normalized cut for segmenting the graph the normalized cut criterion measures both the total dissimilarity between the different groups as well as the total similarity within the groups we show that an efficient computational technique based on a generalized eigenvalue problem can be used to optimize this criterion we applied this approach to segmenting static images as well as motion sequences and found the results to be very encouraging

switching stabilizability for continuous time uncertain switched linear systems this paper investigates the switching stabilizability problem for a class of continuous time switched linear systems with time variant parametric uncertainties first a necessary and sufficient condition for the asymptotic stabilizability of such uncertain switched linear system is derived under the assumption that the closed loop switched system does not generate sliding motions then an additional condition is introduced to exclude the possibility of unstable sliding motions finally a necessary and sufficient for the asymptotic stabilizability of such continuous time uncertain switched linear systems is presented this result improves upon conditions found in the literature which are either sufficient only or necessary only

output dead beat control for a class of planar polynomial systems output dead beat control for a class of nonlinear discrete time systems which are described by a single input output i o polynomial difference equation is considered the class of systems considered is restricted to systems with a two dimensional state space description it is assumed that the highest degree with which the present input appears in the equation is odd necessary and sufficient conditions for the existence of output dead beat control and for the stability of the zero output constrained dynamics are presented we also design a minimum time output dead beat control algorithm feedback controller which yields stable zero dynamics whenever this is feasible a number of interesting phenomena are discussed and illustrated with examples

cardinality constrained linear quadratic optimal control as control implementation often incurs not only a variable cost associated with the magnitude or energy of the control but also a setup cost we consider a discrete time linear quadratic lq optimal control problem with a limited number of control implementations termed in this technical note the cardinality constrained linear quadratic optimal control cclq we first derive a semi analytical feedback policy for cclq problems using dynamic programming dp due to the exponential growth of the complexity in calculating the action regions however dp procedure is only efficient for cclq problems with a scalar state space recognizing this fact we develop then two lower bounding schemes and integrate them into a branch and bound bnb solution framework to offer an efficient algorithm in solving general cclq problems adopting the devised solution algorithm for cclq problems we can solve efficiently the linear quadratic optimal control problem with setup costs

constrained stabilization problems for linear plants linear systems with magnitude and rate constraints on both the state and control variables are considered for such systems semi global and global constrained stabilization problems are formulated when state feedback controllers are used necessary and sufficient conditions for the solvability of the formulated problems are developed moreover design methodologies for such constrained stabilization problems are presented an important aspect of our development here is a taxonomy of constraints to show clearly for what type of constraints what can or cannot be achieved

enumeration based approach to solving parametric linear complementarity problems this paper presents a solution method for parametric linear complementarity problems plcp that relies on an enumeration technique to discover all feasible bases the enumeration procedure is based on evaluating all possible combinations of active constraints and testing for feasibility although the enumeration approach is known to grow exponentially in the number of constraints the formulation of the plcp allows incorporation of cheap rank tests to quickly prune the infeasible directions in the exploration the motivation for the development of the enumeration based plcp solver is that it represents a direct method to solve parametric linear and quadratic optimization problems as well as their mixed integer counterparts these types of problems often arise in the field of model predictive control for linear and hybrid systems the enumeration based plcp solver offers another alternative to compute explicit solutions in the field of hybrid model predictive control that can be extremely effective in some important cases

a fast and accurate matrix completion method based on qr decomposition and l_ 2 1 norm minimization low rank matrix completion aims to recover matrices with missing entries and has attracted considerable attention from machine learning researchers most of the existing methods such as weighted nuclear norm minimization based methods and qatar riyal qr decomposition based methods cannot provide both convergence accuracy and convergence speed to investigate a fast and accurate completion method an iterative qr decomposition based method is proposed for computing an approximate singular value decomposition this method can compute the largest r r 0 singular values of a matrix by iterative qr decomposition then under the framework of matrix trifactorization a method for computing an approximate svd based on qr decomposition csvd qr based l 2 1 norm minimization method lnm qr is proposed for fast matrix completion theoretical analysis shows that this qr decomposition based method can obtain the same optimal solution as a nuclear norm minimization method i e the l 2 1 norm of a submatrix can converge to its nuclear norm consequently an lnm qr based iteratively reweighted l 2 1 norm minimization method irlnm qr is proposed to improve the accuracy of lnm qr theoretical analysis shows that irlnm qr is as accurate as an iteratively reweighted nuclear norm minimization method which is much more accurate than the traditional qr decomposition based matrix completion methods experimental results obtained on both synthetic and real world visual data sets show that our methods are much faster and more accurate than the state of the art methods

observability and controllability of piecewise affine and hybrid systems we prove in a constructive way the equivalence between piecewise affine systems and a broad class of hybrid systems described by interacting linear dynamics automata and propositional logic by focusing our investigation on the former class we show through counterexamples that observability and controllability properties cannot be easily deduced from those of the component linear subsystems instead we propose practical numerical tests based on mixed integer linear programming

some results on the controllability of planar variational inequalities abstract this note deals with the controllability of a class of planar complementarity dynamical systems which can also be viewed as planar evolution variational inequalities it is shown that the complementarity conditions influence the controllability of the system a lot

magnetic curves corresponding to killing magnetic fields in e 3 we explicitly determine all magnetic curves corresponding to the killing magnetic fields on the 3 dimensional euclidean space

algebraic necessary and sufficient conditions for the controllability of conewise linear systems the problem of checking certain controllability properties of even very simple piecewise linear systems is known to be undecidable this paper focuses on conewise linear systems i e systems for which the state space is partitioned into conical regions and a linear dynamics is active on each of these regions for this class of systems we present algebraic necessary and sufficient conditions for controllability we also show that the classical results of controllability of linear systems and input constrained linear systems can be recovered from our main result our treatment employs tools both from geometric control theory and mathematical programming

numerical experience with lower bounds for miqp branch and bound the solution of convex mixed integer quadratic programming miqp problems with a general branch and bound framework is considered it is shown how lower bounds can be computed efficiently during the branch and bound process improved lower bounds such as the ones derived in this paper can reduce the number of quadratic programming qp problems that have to be solved the branch and bound approach is also shown to be superior to other approaches in solving miqp problems numerical experience is presented which supports these conclusions

brief paper tracking and synchronisation for a class of pwa systems in this paper the tracking problem for a class of discontinuous piecewise affine pwa systems is addressed we propose an observer based output feedback control design consisting of a feedforward a piecewise affine feedback law and a model based observer solving the tracking problem these synthesis results can also be employed to tackle the master slave synchronisation problem for pwa systems it is shown that for certain classes of pwa systems the design is characterised in terms of linear matrix inequalities the results are illustrated by application to mechanical systems with discontinuous friction characteristics

the contact magnetic flow in 3d sasakian manifolds we first present a geometrical approach to magnetic fields in three dimensional riemannian manifolds because this particular dimension allows one to easily tie vector fields and 2 forms when the vector field is divergence free it defines a magnetic field on the manifold whose lorentz force equation presents a simple and useful form in particular for any three dimensional sasakian manifold the contact magnetic field is studied and the normal magnetic trajectories are determined as an application we consider the three dimensional unit sphere where we prove the existence of closed magnetic trajectories of the contact magnetic field and that this magnetic flow is quantized in the set of rational numbers

stabilizing polynomial approximation of explicit mpc a given explicit piecewise affine representation of an mpc feedback law is approximated by a single polynomial computed using linear programming this polynomial state feedback control law guarantees closed loop stability and constraint satisfaction the polynomial feedback can be implemented in real time even on very simple devices with severe limitations on memory storage

impulsive and hybrid dynamical systems stability dissipativity and control s book review w m haddad v s chellaboina and s g nersesov

optimal control of continuous time switched affine systems this paper deals with optimal control of switched piecewise affine autonomous systems where the objective is to minimize a performance index over an infinite time horizon we assume that the switching sequence has a finite length and that the decision variables are the switching instants and the sequence of operating modes we present two different approaches for solving such an optimal control problem the first approach iterates between a procedure that finds an optimal switching sequence of modes and a procedure that finds the optimal switching instants the second approach is inspired by dynamic programming and identifies the regions of the state space where an optimal mode switch should occur therefore providing a state feedback control law

alternating direction method with gaussian back substitution for separable convex programming we consider the linearly constrained separable convex minimization problem whose objective function is separable into m individual convex functions with nonoverlapping variables a douglas rachford alternating direction method of multipliers adm has been well studied in the literature for the special case of m 2 but the convergence of extending adm to the general case of m ge 3 is still open in this paper we show that the straightforward extension of adm is valid for the general case of m ge 3 if it is combined with a gaussian back substitution procedure the resulting adm with gaussian back substitution is a novel approach towards the extension of adm from m 2 to m ge 3 and its algorithmic framework is new in the literature for the adm with gaussian back substitution we prove its convergence via the analytic framework of contractive type methods and we show its numerical efficiency by some application problems

on controllability of discrete time bilinear systems by near controllability abstract in this paper controllability of discrete time bilinear systems is studied by applying a recent result on near controllability a new sufficient condition for controllability of the systems is presented where controllability is proved by approximation with near controllability the new condition is algebraically verifiable and is hence easy to apply compared with a classical result on controllability of discrete time bilinear systems which can be effective even when the classical result does not work furthermore the control inputs to achieve the transition of the systems between any given pair of states are approximately computable according to near controllability therefore near controllability can be used to not only better characterize the system properties but also prove controllability with computable control inputs the new condition is then generalized to derive similar results on controllability and near controllability of the systems finally examples are given to illustrate the results of this paper

control of systems integrating logic dynamics and constraints this paper proposes a framework for modeling and controlling systems described by interdependent physical laws logic rules and operating constraints denoted as mixed logical dynamical mld systems these are described by linear dynamic equations subject to linear inequalities involving real and integer variables mld systems include linear hybrid systems finite state machines some classes of discrete event systems constrained linear systems and nonlinear systems which can be approximated by piecewise linear functions a predictive control scheme is proposed which is able to stabilize mld systems on desired reference trajectories while fulfilling operating constraints and possibly take into account previous qualitative knowledge in the form of heuristic rules due to the presence of integer variables the resulting on line optimization procedures are solved through mixed integer quadratic programming miqp for which efficient solvers have been recently developed some examples and a simulation case study on a complex gas supply system are reported

human motion segmentation via robust kernel sparse subspace clustering studies on human motion have attracted a lot of attentions human motion capture data which much more precisely records human motion than videos do has been widely used in many areas motion segmentation is an indispensable step for many related applications but current segmentation methods for motion capture data do not effectively model some important characteristics of motion capture data such as riemannian manifold structure and containing non gaussian noise in this paper we convert the segmentation of motion capture data into a temporal subspace clustering problem under the framework of sparse subspace clustering we propose to use the geodesic exponential kernel to model the riemannian manifold structure use correntropy to measure the reconstruction error use the triangle constraint to guarantee temporal continuity in each cluster and use multi view reconstruction to extract the relations between different joints therefore exploiting some special characteristics of motion capture data we propose a new segmentation method which is robust to non gaussian noise since correntropy is a localized similarity measure we also develop an efficient optimization algorithm based on block coordinate descent method to solve the proposed model our optimization algorithm has a linear complexity while sparse subspace clustering is originally a quadratic problem extensive experiment results both on simulated noisy data set and real noisy data set demonstrate the advantage of the proposed method

implicit block diagonal low rank representation while current block diagonal constrained subspace clustering methods are performed explicitly on the original data space in practice it is often more desirable to embed the block diagonal prior into the reproducing kernel hilbert feature space by kernelization techniques as the underlying data structure in reality is usually nonlinear however it is still unknown how to carry out the embedding and kernelization in the models with block diagonal constraints in this paper we shall take a step in this direction first we establish a novel model termed implicit block diagonal low rank representation ibdlr by incorporating the implicit feature representation and block diagonal prior into the prevalent low rank representation method second mostly important we show that the model in ibdlr could be kernelized by making use of a smoothed dual representation and the specifics of a proximal gradient based optimization algorithm finally we provide some theoretical analyses for the convergence of our optimization algorithm comprehensive experiments on synthetic and real world data sets demonstrate the superiorities of our ibdlr over state of the art methods

robust recovery of subspace structures by low rank representation in this paper we address the subspace clustering problem given a set of data samples vectors approximately drawn from a union of multiple subspaces our goal is to cluster the samples into their respective subspaces and remove possible outliers as well to this end we propose a novel objective function named low rank representation lrr which seeks the lowest rank representation among all the candidates that can represent the data samples as linear combinations of the bases in a given dictionary it is shown that the convex program associated with lrr solves the subspace clustering problem in the following sense when the data is clean we prove that lrr exactly recovers the true subspace structures when the data are contaminated by outliers we prove that under certain conditions lrr can exactly recover the row space of the original data and detect the outlier as well for data corrupted by arbitrary sparse errors lrr can also approximately recover the row space with theoretical guarantees since the subspace membership is provably determined by the row space these further imply that lrr can perform robust subspace clustering and error correction in an efficient and effective way

generalized principal component analysis gpca this paper presents an algebro geometric solution to the problem of segmenting an unknown number of subspaces of unknown and varying dimensions from sample data points we represent the subspaces with a set of homogeneous polynomials whose degree is the number of subspaces and whose derivatives at a data point give normal vectors to the subspace passing through the point when the number of subspaces is known we show that these polynomials can be estimated linearly from data hence subspace segmentation is reduced to classifying one point per subspace we select these points optimally from the data set by minimizing certain distance function thus dealing automatically with moderate noise in the data a basis for the complement of each subspace is then recovered by applying standard pca to the collection of derivatives normal vectors extensions of gpca that deal with data in a high dimensional space and with an unknown number of subspaces are also presented our experiments on low dimensional data show that gpca outperforms existing algebraic algorithms based on polynomial factorization and provides a good initialization to iterative techniques such as k subspaces and expectation maximization we also present applications of gpca to computer vision problems such as face clustering temporal video segmentation and 3d motion segmentation from point correspondences in multiple affine views

on small controllability and controllability of a class of nonlinear systems in this paper small controllability and controllability of a class of nonlinear systems are studied both the discrete time case and continuous time case are considered by proposing an implicit function approach sufficient conditions for the systems respectively to be small controllable and controllable are obtained examples are also provided to illustrate the results of the paper

stabilizability and dead beat controllers for two classes of wiener hammerstein models two classes of block oriented models of the wiener hammerstein type are considered we prove that a generic condition is sufficient for a null controllable discrete time system of this form to have a stabilizing minimum time dead beat controller when the condition is violated we show how to design a nonminimum time stabilizing dynamic dead beat controller the result is used to obtain stabilizability conditions for these systems

propositional logic in control and monitoring problems by using linear constraints on integer variables logical inference can be incorporated within model predictive control and model based detection problems resulting in mixed integer quadratic or mixed integer linear programming problems qualitative knowledge can be used to prioritize control objectives or to improve performance of detection schemes this article demonstrates some capabilities of combining logic using integer variables with quantitative models

robust kernel low rank representation recently low rank representation lrr has shown promising performance in many real world applications such as face clustering however lrr may not achieve satisfactory results when dealing with the data from nonlinear subspaces since it is originally designed to handle the data from linear subspaces in the input space meanwhile the kernel based methods deal with the nonlinear data by mapping it from the original input space to a new feature space through a kernel induced mapping to effectively cope with the nonlinear data we first propose the kernelized version of lrr in the clean data case we also present a closed form solution for the resultant optimization problem moreover to handle corrupted data we propose the robust kernel lrr rklrr approach and develop an efficient optimization algorithm to solve it based on the alternating direction method in particular we show that both the subproblems in our optimization algorithm can be efficiently and exactly solved and it is guaranteed to obtain a globally optimal solution besides our proposed algorithm can also solve the original lrr problem which is a special case of our rklrr when using the linear kernel in addition based on our new optimization technique the kernelization of some variants of lrr can be similarly achieved comprehensive experiments on synthetic data sets and real world data sets clearly demonstrate the efficiency of our algorithm as well as the effectiveness of rklrr and the kernelization of two variants of lrr

controllability is harder to decide than accessibility this article compares the difficulties of deciding controllability and accessibility these are standard properties of control systems but complete algebraic characterizations of controllability have proved elusive the article shows in particular that for subsystems of bilinear systems accessibility can be decided in polynomial time but controllability is np hard

normal cr structures on compact 3 manifolds we study normal cr compact manifolds in dimension 3 for a choice of a cr reeb vector field we associate a sasakian metric on them and we classify those metrics as a consequence the underlying manifolds are topologically finite quotients of s 3 or of a non flat circle bundle over a riemann surface of positive genus in the latter case we prove that their cr automorphisms group is a finite extension of s 1 and we classify the normal cr structures on these manifolds

spectral curvature clustering scc this paper presents novel techniques for improving the performance of a multi way spectral clustering framework govindu in proceedings of the 2005 ieee computer society conference on computer vision and pattern recognition cvpr 05 vol 1 pp 1150 1157 2005 chen and lerman 2007 preprint in the supplementary webpage for segmenting affine subspaces specifically it suggests an iterative sampling procedure to improve the uniform sampling strategy an automatic scheme of inferring the tuning parameter from the data a precise initialization procedure for k means as well as a simple strategy for isolating outliers the resulting algorithm spectral curvature clustering scc requires only linear storage and takes linear running time in the size of the data it is supported by theory which both justifies its successful performance and guides our practical choices we compare it with other existing methods on a few artificial instances of affine subspaces application of the algorithm to several real world problems is also discussed

a nine fold canonical decomposition for linear systems the zero structure for non minimal proper systems in state space form is investigated the approach is geometric and a complete characterization in geometric terms is given of the invariant decoupling system and transmission zeros as defined by rosenbrock the first main result is a formula for the transmission zeros second a canonical lattice diagram is presented of a decomposition of the state space which can be viewed as the product of the kalman canonical decomposition and the morse canonical decomposition this decomposition gives a straightforward characterization of all zeros just mentioned in terms of spectral properties of subspaces under a certain class of feedback and injection mappings via this diagram a number of equivalent formulae for the transmission zeros are derived the freedom in pole assignment leads to new characterizations for the invariant and system zeros in terms of greatest common divisors of characteristic polynomials finally the relation is demonstrated be

output feedback stabilization of a class of wiener systems a globally stabilizing output feedback controller is designed for a class of continuous time wiener systems the wiener systems we consider consist of a linear dynamical block and an output polynomial nonlinearity connected in series the hybrid controller consists of three modes of operation which are periodically applied to the system the controller achieves a dead beat response of the closed loop system

structure and control of piecewise linear systems elementary algebraic topology is used in the study of the structure and optimal control of piecewise linear and non linear systems

on the controllability of piecewise linear hypersurface systems this paper investigates the global controllability of piecewise linear hypersurface systems which are defined as control systems that are subject to affine dynamics on each of the components of a finite polyhedral partition various new tools are developed for the study of the problem including a classification of the facets of the polyhedra in the partition necessary and sufficient conditions for complete controllability are obtained via the study of a suitably defined controllability connection matrix of polyhedra

discriminative block diagonal representation learning for image recognition existing block diagonal representation studies mainly focuses on casting block diagonal regularization on training data while only little attention is dedicated to concurrently learning both block diagonal representations of training and test data in this paper we propose a discriminative block diagonal low rank representation bdlrr method for recognition in particular the elaborate bdlrr is formulated as a joint optimization problem of shrinking the unfavorable representation from off block diagonal elements and strengthening the compact block diagonal representation under the semisupervised framework of lrr to this end we first impose penalty constraints on the negative representation to eliminate the correlation between different classes such that the incoherence criterion of the extra class representation is boosted moreover a constructed subspace model is developed to enhance the self expressive power of training samples and further build the representation bridge between the training and test samples such that the coherence of the learned intraclass representation is consistently heightened finally the resulting optimization problem is solved elegantly by employing an alternative optimization strategy and a simple recognition algorithm on the learned representation is utilized for final prediction extensive experimental results demonstrate that the proposed method achieves superb recognition results on four face image data sets three character data sets and the 15 scene multicategories data set it not only shows superior potential on image recognition but also outperforms the state of the art methods

a note on observability tests for general polynomial and simple wiener hammerstein systems we propose an algorithm based on symbolic computation packages for testing observability conditions of general polynomial systems which were formulated in sontag siam j control optim 17 1979 139 151 computational complexity of the observability test can be reduced and the test simplified for classes of polynomial systems we illustrate this by considering the class of simple wiener hammerstein systems which consist of a series of two linear dynamic blocks between which a static nonlinearity is sandwiched we consider the case when the nonlinearity is a monomial n q q 1 simple necessary and sufficient conditions for observability are given and they resemble but are subtly different from the well known result on observability for the series connection of two linear systems c 1998 elsevier science b v all rights reserved

nonlinear regulation the piecewise linear approach this paper approaches nonlinear control problems through the use of discrete time piecewise linear systems these are systems whose next state and output maps are both described by pl maps i e by maps which are affine on each of the components of a finite polyhedral partition various results on state and output feedback observers and inverses standard for linear systems are proved for pl systems many of these results are then used in the study of more general both discrete and continuous time systems using suitable approximations

hybrid linear modeling via local best fit flats we present a simple and fast geometric method for modeling data by a union of affine subspaces the method begins by forming a collection of local best fit affine subspaces i e subspaces approximating the data in local neighborhoods the correct sizes of the local neighborhoods are determined automatically by the jones 2 numbers we prove under certain geometric conditions that our method finds the optimal local neighborhoods the collection of subspaces is further processed by a greedy selection procedure or a spectral method to generate the final model we discuss applications to tracking based motion segmentation and clustering of faces under different illuminating conditions we give extensive experimental evidence demonstrating the state of the art accuracy and speed of the suggested algorithms on these problems and also on synthetic hybrid linear data as well as the mnist handwritten digits data and we demonstrate how to use our algorithms for fast determination of the number of affine subspaces

controllability for a class of simple wiener hammerstein systems abstract controllability for a class of simple wiener hammerstein systems is considered necessary and sufficient conditions for dead beat and complete controllability for these systems are presented the controllability tests consist of two easy to check tests for the subsystems

a unified framework for hybrid control model and optimal control theory we propose a very general framework that systematizes the notion of a hybrid system combining differential equations and automata governed by a hybrid controller that issues continuous variable commands and makes logical decisions we first identify the phenomena that arise in real world hybrid systems then we introduce a mathematical model of hybrid systems as interacting collections of dynamical systems evolving on continuous variable state spaces and subject to continuous controls and discrete transitions the model captures the identified phenomena subsumes previous models yet retains enough structure to pose and solve meaningful control problems we develop a theory for synthesizing hybrid controllers for hybrid plants in all optimal control framework in particular we demonstrate the existence of optimal relaxed and near optimal precise controls and derive generalized quasi variational inequalities that the associated value function satisfies we summarize algorithms for solving these inequalities based on a generalized bellman equation impulse control and linear programming

data based identification and control of nonlinear systems via piecewise affine approximation the piecewise affine pwa model represents an attractive model structure for approximating nonlinear systems in this paper a procedure for obtaining the pwa autoregressive exogenous arx autoregressive systems with exogenous inputs models of nonlinear systems is proposed two key parameters defining a pwarx model namely the parameters of locally affine subsystems and the partition of the regressor space are estimated the former through a least squares based identification method using multiple models and the latter using standard procedures such as neural network classifier or support vector machine classifier having obtained the pwarx model of the nonlinear system a controller is then derived to control the system for reference tracking both simulation and experimental studies show that the proposed algorithm can indeed provide accurate pwa approximation of nonlinear systems and the designed controller provides good tracking performance

state deadbeat control of nonlinear systems construction via sets a geometric generalization of the discrete time linear deadbeat control problem is studied the proposed method to generate a deadbeat tracker for a given nonlinear system is constructive and makes use of sets that can be computed iteratively for demonstration derivations of the deadbeat feedback law and tracker dynamics are provided for an example system based on the method a simple algorithm that computes the deadbeat gain for a linear system with scalar input is given

popov belevitch hautus type controllability tests for linear complementarity systems it is well known that checking certain controllability properties of very simple piecewise linear systems are undecidable problems this paper deals with the controllability problem of a class of piecewise linear systems known as linear complementarity systems by exploiting the underlying structure and employing the results on the controllability of the so called conewise linear systems we present a set of inequalitytype conditions as necessary and sufficient conditions for controllability of linear complementarity systems the presented conditions are of popov belevitch hautus type in nature 2006 elsevier b v all rights reserved

further results on controllability of recurrent neural networks this paper studies controllability properties of recurrent neural networks the new contributions are 1 an extension of a previous result to a slightly different model 2 a formulation and proof of a necessary and sufficient condition and 3 an analysis of a low dimensional case for which the hypotheses made in previous work do not apply

a note on dead beat controllability of generalised hammerstein systems necessary and sufficient conditions for dead beat and complete controllability for a class of generalised hammerstein systems are presented since the system s structure is very close to linear only linear algebra is used for the controllability test the test is very simple and easy to use a closed loop minimum time dead beat controller can be designed for a subclass of generalised hammerstein systems using the grobner basis method

output regulation of linear plants subject to constraints output regulation problems for continuous time linear systems with state and or input constraints are studied the problems are formulated in global and semi global setting by using state or full information feedback the goal of this paper is to develop solvability conditions for the posed problems moreover appropriate regulators are constructed under the solvability conditions to state the solvability conditions clearly a taxonomy of constraints is introduced which delineates the constraints into several categories such a taxonomy of constraints provides a classification of linear plants with constraints and identifies what types of output regulation problems are solvable results developed here include as a special case the results obtained in the literature for systems with only input constraints the constraint taxonomy also identifies some intrinsically hard constraints non right invertible constraints for which the solvability conditions of global semi global output regulation problems are not clear yet as a special case of output regulation we also consider tracking problems with constraints it is shown that if there exists a state feedback controller with a stabilizing domain of attraction then one can find a regulator with a tracking domain of attraction arbitrarily close to the stabilizing domain

sparse subspace clustering algorithm theory and applications many real world problems deal with collections of high dimensional data such as images videos text and web documents dna microarray data and more often such high dimensional data lie close to low dimensional structures corresponding to several classes or categories to which the data belong in this paper we propose and study an algorithm called sparse subspace clustering to cluster data points that lie in a union of low dimensional subspaces the key idea is that among the infinitely many possible representations of a data point in terms of other points a sparse representation corresponds to selecting a few points from the same subspace this motivates solving a sparse optimization program whose solution is used in a spectral clustering framework to infer the clustering of the data into subspaces since solving the sparse optimization program is in general np hard we consider a convex relaxation and show that under appropriate conditions on the arrangement of the subspaces and the distribution of the data the proposed minimization program succeeds in recovering the desired sparse representations the proposed algorithm is efficient and can handle data points near the intersections of subspaces another key advantage of the proposed algorithm with respect to the state of the art is that it can deal directly with data nuisances such as noise sparse outlying entries and missing entries by incorporating the model of the data into the sparse optimization program we demonstrate the effectiveness of the proposed algorithm through experiments on synthetic data as well as the two real world problems of motion segmentation and face clustering

structure identification of nonlinear dynamic systems a survey on input output approaches abstract in the past several methods have been elaborated for the identification of nonlinear dynamic systems most of the methods assume that the structure of the system is given a priori therefore they are in reality parameter estimation algorithms and structure identification is thus usually performed by repeated parameter estimation however in nonlinear system theory several methods are known to determine the structure of a system in this paper structure identification of block oriented especially cascade models of semi linear dynamic models with signal dependent parameters and of nonlinear dynamic models being linear in the parameters will be considered different structure selection methods are summarized based on step and impulse tests frequency response measurements correlation analysis repeated reproducible tests and normal operating data

lyapunov stability of a class of discrete event systems discrete event systems des are dynamical systems which evolve in time by the occurrence of events at possibly irregular time intervals logical des are a class of discrete time des with equations of motion that are most often nonlinear and discontinuous with respect to event occurrences recently there has been much interest in studying the stability properties of logical des and several definitions for stability and methods for stability analysis have been proposed here the authors introduce a logical des model and define stability in the sense of lyapunov and asymptotic stability for logical des then the authors show that more conventional analysis of stability which employs appropriate lyapunov functions can be used for logical des the authors provide a general characterization of the stability properties of automata theoretic des models petri nets and finite state systems furthermore the lyapunov stability analysis approach is illustrated on a manufacturing system that processes batches of n different types of parts according to a priority scheme to prove properties related to the machine s ability to reorient itself to achieve safe operation and a load balancing problem in computer networks to study the ability of the system to achieve a balanced load to minimize underutilization

control to facet by piecewise affine output feedback the control to facet problem plays an important role in the design of feedback controllers for piecewise affine hybrid systems on polytopes in the literature necessary conditions and sufficient conditions for solvability by static state feedback exist in this paper we extend these results to the case of continuous piecewise affine static output feedback for the construction of a controller a triangulation of the output polytope is made which satisfies additional conditions to guarantee compatibility with the induced subdivision of the state polytope in the state feedback case the use of this special type of triangulation is not required

stability and controllability of planar conewise linear systems abstract this paper presents a fairly complete treatment of stability and controllability of piecewise linear systems defined on a conic partition of r 2 this includes necessary and sufficient conditions for stability and controllability as well as establishing that controllability implies stabilizability by piecewise linear state feedback a key tool in the approach is the study of the poincare map

soft margin multiple kernel learning multiple kernel learning mkl has been proposed for kernel methods by learning the optimal kernel from a set of predefined base kernels however the traditional l 1 rm mkl method often achieves worse results than the simplest method using the average of base kernels i e average kernel in some practical applications in order to improve the effectiveness of mkl this paper presents a novel soft margin perspective for mkl specifically we introduce an additional slack variable called kernel slack variable to each quadratic constraint of mkl which corresponds to one support vector machine model using a single base kernel we first show that l 1 rm mkl can be deemed as hard margin mkl and then we propose a novel soft margin framework for mkl three commonly used loss functions including the hinge loss the square hinge loss and the square loss can be readily incorporated into this framework leading to the new soft margin mkl objective functions many existing mkl methods can be shown as special cases under our soft margin framework for example the hinge loss soft margin mkl leads to a new box constraint for kernel combination coefficients using different hyper parameter values for this formulation we can inherently bridge the method using average kernel l 1 rm mkl and the hinge loss soft margin mkl the square hinge loss soft margin mkl unifies the family of elastic net constraint regularizer based approaches and the square loss soft margin mkl incorporates l 2 rm mkl naturally moreover we also develop efficient algorithms for solving both the hinge loss and square hinge loss soft margin mkl comprehensive experimental studies for various mkl algorithms on several benchmark data sets and two real world applications including video action recognition and event recognition demonstrate that our proposed algorithms can efficiently achieve an effective yet sparse solution for mkl

on the necessity of the invariance conditions for reach control on polytopes abstract we study the reach control problem rcp to make the solutions of an affine system defined on a polytopic state space reach and exit a prescribed facet of the polytope in finite time without first leaving the polytope so called invariance conditions are used to prevent solutions from leaving the polytope through facets which are not designated as the exit facet these conditions are known to be necessary for solvability of the rcp on polytopes by continuous state feedback we study whether the invariance conditions are also necessary for solvability of the rcp on polytopes by open loop controls we show by way of a counterexample that surprisingly the answer is negative we identify a suitable class of polytopes for which the invariance conditions remain necessary conditions