survey paper static output feedback a survey this paper reviews the static output feedback problem in the control of linear time invariant lti systems it includes analytical and computational methods and presents in a unified fashion the knowledge gained in the decades of research into this important open problem the paper shows that although many approaches and techniques exist to approach different versions of the problem no efficient algorithmic solutions are available

multiple output regression with latent noise in high dimensional data structured noise caused by observed and unobserved factors affecting multiple target variables simultaneously imposes a serious challenge for modeling by masking the often weak signal therefore 1 explaining away the structured noise in multiple output regression is of paramount importance additionally 2 assumptions about the correlation structure of the regression weights are needed we note that both can be formulated in a natural way in a latent variable model in which both the interesting signal and the noise are mediated through the same latent factors under this assumption the signal model then borrows strength from the noise model by encouraging similar effects on correlated targets we introduce a hyperparameter for the latent signal to noise ratio which turns out to be important for modelling weak signals and an ordered infinite dimensional shrinkage prior that resolves the rotational unidentifiability in reduced rank regression models simulations and prediction experiments with metabolite gene expression fmri measurement and macroeconomic time series data show that our model equals or exceeds the state of the art performance and in particular outperforms the standard approach of assuming independent noise and signal models

ridge regression biased estimation for nonorthogonal problems in multiple regression it is shown that parameter estimates based on minimum residual sum of squares have a high probability of being unsatisfactory if not incorrect if the prediction vectors are not orthogonal proposed is an estimation procedure based on adding small positive quantities to the diagonal of x x introduced is the ridge trace a method for showing in two dimensions the effects of nonorthogonality it is then shown how to augment x x to obtain biased estimates with smaller mean square error

predicting multivariate responses in multiple linear regression we look at the problem of predicting several response variables from the same set of explanatory variables the question is how to take advantage of correlations between the response variables to improve predictive accuracy compared with the usual procedure of doing individual regressions of each response variable on the common set of predictor variables a new procedure is introduced called the curds and whey method its use can substantially reduce prediction errors when there are correlations between responses while maintaining accuracy even if the responses are uncorrelated in extensive simulations the new procedure is compared with several previously proposed methods for predicting multiple responses including partial least squares and exhibits superior accuracy one version can be easily implemented in the context of standard statistical packages

output feedback multivariable global variable gain super twisting algorithm a novel output feedback super twisting algorithm design is proposed for uncertain multivariable plants in order to allow global finite time exact tracking in the presence of a rather general class of matched nonlinear disturbances which may depend on measured and or unmeasured states the algorithm is designed with variable gains this represents a significant extension of a recently proposed nondecoupled multivariable super twisting algorithm based on state feedback with fixed gains to the case of output feedback with variable gains

smooth dynamic output feedback control for multiple time delay systems with nonlinear uncertainties the dynamic output feedback control problem is considered for a class of systems with multiple time delays and nonlinear uncertainties based on the control input matrix and output matrix we decompose the system into two subsystems the dynamic compensator is designed for the first subsystem and then the output feedback controller is constructed based on the compensator and the second subsystem by using the introduced new lyapunov krasovskii functional we show that the solution of the resultant closed loop system converges exponentially to an adjustable bounded region compared with the previous works the developed controller in this paper is memoryless and smooth which only uses the system output the control design conditions are relaxed because of the developed dynamic compensator the result is further extended to the general nonlinear case the corresponding dynamic nonlinear output feedback control method is proposed finally simulations are performed to show the potential of the proposed methods

on observation of time delay systems with unknown inputs causal and non causal observability are discussed in this technical note for nonlinear time delay systems with unknown inputs using the theory of non commutative rings and the algebraic framework introduced by xia the nonlinear time delay system is transformed into a suitable canonical form to solve the problem a necessary and sufficient condition is given to guarantee the existence of a change of coordinates leading to such a form

bayesian reduced rank regression in econometrics abstract the reduced rank regression model arises repeatedly in theoretical and applied econometrics to date the only general treatment of this model have been frequentist this paper develops general methods for bayesian inference with noninformative reference priors in this model based on a markov chain sampling algorithm and procedures for obtaining predictive odds ratios for regression models with different ranks these methods are used to obtain evidence on the number of factors in a capital asset pricing model

multi target regression via robust low rank learning multi target regression has recently regained great popularity due to its capability of simultaneously learning multiple relevant regression tasks and its wide applications in data mining computer vision and medical image analysis while great challenges arise from jointly handling inter target correlations and input output relationships in this paper we propose multi layer multi target regression mmr which enables simultaneously modeling intrinsic inter target correlations and nonlinear input output relationships in a general framework via robust low rank learning specifically the mmr can explicitly encode inter target correlations in a structure matrix by matrix elastic nets men the mmr can work in conjunction with the kernel trick to effectively disentangle highly complex nonlinear input output relationships the mmr can be efficiently solved by a new alternating optimization algorithm with guaranteed convergence the mmr leverages the strength of kernel methods for nonlinear feature learning and the structural advantage of multi layer learning architectures for inter target correlation modeling more importantly it offers a new multi layer learning paradigm for multi target regression which is endowed with high generality flexibility and expressive ability extensive experimental evaluation on 18 diverse real world datasets demonstrates that our mmr can achieve consistently high performance and outperforms representative state of the art algorithms which shows its great effectiveness and generality for multivariate prediction

memoryless static output feedback sliding mode control for nonlinear systems with delayed disturbances in this technical note a local stabilization problem for nonlinear systems with disturbances which are dependent on time varying delay phenomena is studied an appropriate transformation is introduced to express the system in regular form which facilitates both design and analysis then sufficient conditions are developed based on a lyapunov razumikhin approach such that the sliding motion is uniformly asymptotically or exponentially stable a static output feedback control law independent of the time delay is synthesized to guarantee reachability of the sliding mode the accessible bounds on the disturbances are fully used in the control design to reduce the conservatism a simulation example shows the effectiveness of the approach

sliding mode control for singular stochastic markovian jump systems with uncertainties this paper considers sliding mode control design for singular stochastic markovian jump systems with uncertainties a suitable integral sliding function is proposed and the resulting sliding mode dynamics is an uncertain singular stochastic markovian jump system a set of new sufficient conditions is developed which not only guarantees the stochastic admissibility of the sliding mode dynamics but also determines all the parameter matrices in the integral sliding function then a sliding mode control law is synthesized such that reachability of the specified sliding surface can be ensured finally three examples are given to demonstrate the effectiveness of the results

memoryless state feedback controller design for time delay systems with matched uncertain nonlinearities the robust control problem for a class of uncertain time delay systems is investigated the systems include multiple time delays and uncertain nonlinear functions to our best knowledge no controller design method for the systems is reported in the literature except for sliding mode control smc however the sliding mode controller must use the delayed state of the system so that the precise time delays have to be known and also a large memory is needed for controller implementation to overcome the shortcomings we employ the backstepping method to design a memoryless state feedback controller by constructing a novel lyapunov krasovskii functional we show that the resulting closed loop system is asymptotically stable simulations are done to verify the effectiveness of the proposed method

input output linearization approach to state observer design for nonlinear system presents a state observer for a class of nonlinear systems based on the input output linearization while the previous result presented state observers for nonlinear systems of full relative degree we proposed a procedure fur the design of nonlinear state observers which do not require the hypothesis of full relative degree assuming that there exists a global state observer for internal dynamics and that some functions are globally lipschitz we can design a globally convergent state observer it is also shown that if the zero dynamics are locally exponentially stable then there exists a local state observer an example is given to illustrate the proposed design of nonlinear state observers

bifurcations in persistence theory for models of population growth represented by systems of autonomous ordinary differential equations the question of whether persistence outcomes may be changed due to violation of one of the standard assumptions by means of a perturbation leading to bifurcations is investigated the results are illustrated by several examples

lie algebras and the stability of nonlinear systems lie algebras and the cartan decomposition are used to study the stability of pseudo linear systems of differential equations

uniform persistence and chain recurrence soit 7b e d un espace metrique localement compact et e un sous ensemble clos de 7b e soit un systeme dynamique sur e on reconsidere la notion de persistance uniforme dans le cadre de la theorie de conley des ensembles invariants

integral sliding mode control for nonlinear systems with matched and unmatched perturbations we consider the problem of designing an integral sliding mode controller to reduce the disturbance terms that act on nonlinear systems with state dependent drift and input matrix the general case of both matched and unmatched disturbances affecting the system is addressed it is proved that the definition of a suitable sliding manifold and the generation of sliding modes upon it guarantees the minimization of the effect of the disturbance terms which takes place when the matched disturbances are completely rejected and the unmatched ones are not amplified a simulation of the proposed technique applied to a dynamically feedback linearized unicycle illustrates its effectiveness even in presence of nonholonomic constraints

brief paper wirtinger s inequality and lyapunov based sampled data stabilization discontinuous lyapunov functionals appeared to be very efficient for sampled data systems fridman 2010 naghshtabrizi hespanha teel 2008 in the present paper new discontinuous lyapunov functionals are introduced for sampled data control in the presence of a constant input delay the construction of these functionals is based on the vector extension of wirtinger s inequality these functionals lead to simplified and efficient stability conditions in terms of linear matrix inequalities lmis the new stability analysis is applied to sampled data state feedback stabilization and to a novel sampled data static output feedback problem where the delayed measurements are used for stabilization

on saturation discontinuities and delays in iiss and iss feedback control redesign this paper addresses input to state stability iss and integral iss iiss redesign to enhance robustness of feedback control systems in the face of actuator disturbances the idea of the redesign is to modify control laws available for disturbance free systems a priori in order to achieve robustness against the disturbance in practice control system design often encounters time delays discontinuities and actuator limitations at the same time the purpose of this paper is to propose a unified treatment of these issues in the redesign for actuator disturbances for the new class of systems we pursue lgv type formulas which are widely used for ordinary differential equations in addition we incorporate saturation into the lgv type control for implementation in the presence of actuator limitations to derive such a redesign formula we develop a framework of iss iiss lyapunov krasovskii functionals allowing for the krasovskii solutions this paper demonstrates that the notion of invariantly differentiable functionals is useful for deriving lgv type control laws and dealing with discontinuities directly the iiss formulation which includes the iss as a special case allows us to address actuator limitations in the redesign problem in a flexible manner

adaptive observers for single output nonlinear systems an adaptive version of the nonlinear observer obtained by a j krener et al 1983 is presented this version involves the cancellation of nonlinear terms by output injection as an intermediate step necessary and sufficient conditions are given for transforming a nonlinear system by state space change of coordinates into the special adaptive observer form that was used by y bastin et al 1988 to design adaptive observers

nonlinear sliding mode control design an lmi approach abstract this paper presents a novel nonlinear sliding mode control methodology for systems with both matched and unmatched perturbations including parametric uncertainties instead of traditional approaches where uncertainties and nonlinearities are coped with via linear nominal models and linear sliding surfaces the proposed approach incorporates exact convex expressions to represent both the nonlinear surface and the system thus allowing a significant chattering reduction moreover thanks to the convex form of the nonlinear nominal model when combined with the direct lyapunov method it leads to linear matrix inequalities which are efficiently solved via convex optimization techniques illustrative examples are provided

robust integral sliding mode control for uncertain stochastic systems with time varying delay this paper is concerned with sliding mode control for uncertain stochastic systems with time varying delay both time varying parameter uncertainties and an unknown nonlinear function may appear in the controlled system an integral sliding surface is first constructed then by means of linear matrix inequalities lmis a sufficient condition is derived to guarantee the global stochastic stability of the stochastic dynamics in the specified switching surface for all admissible uncertainties the synthesized sliding mode controller guarantees the reachability of the specified sliding surface finally a simulation example is presented to illustrate the proposed method

sparse bayesian infinite factor models we focus on sparse modelling of high dimensional covariance matrices using bayesian latent factor models we propose a multiplicative gamma process shrinkage prior on the factor loadings which allows introduction of infinitely many factors with the loadings increasingly shrunk towards zero as the column index increases we use our prior on a parameter expanded loading matrix to avoid the order dependence typical in factor analysis models and develop an efficient gibbs sampler that scales well as data dimensionality increases the gain in efficiency is achieved by the joint conjugacy property of the proposed prior which allows block updating of the loadings matrix we propose an adaptive gibbs sampler for automatically truncating the infinite loading matrix through selection of the number of important factors theoretical results are provided on the support of the prior and truncation approximation bounds a fast algorithm is proposed to produce approximate bayes estimates latent factor regression methods are developed for prediction and variable selection in applications with high dimensional correlated predictors operating characteristics are assessed through simulation studies and the approach is applied to predict survival times from gene expression data

bayesian canonical correlation analysis canonical correlation analysis cca is a classical method for seeking correlations between two multivariate data sets during the last ten years it has received more and more attention in the machine learning community in the form of novel computational formulations and a plethora of applications we review recent developments in bayesian models and inference methods for cca which are attractive for their potential in hierarchical extensions and for coping with the combination of large dimensionalities and small sample sizes the existing methods have not been particularly successful in fulfilling the promise yet we introduce a novel efficient solution that imposes group wise sparsity to estimate the posterior of an extended model which not only extracts the statistical dependencies correlations between data sets but also decomposes the data into shared and data set specific components in statistics literature the model is known as inter battery factor analysis ibfa for which we now provide a bayesian treatment

smc design for robust stabilization of nonlinear markovian jump singular systems in this technical note the sliding mode control smc problem is investigated for t s fuzzy model based nonlinear markovian jump singular systems subject to matched unmatched uncertainties to accommodate the model characteristics of such a hybrid system a novel integral type fuzzy sliding surface is put forward by taking the singular matrix and state dependent projection matrix into account simultaneously which is the key contribution of the note the designed surface contains two important features 1 local input matrices for different subsystems in the same system mode are allowed to be different and 2 the matched uncertainties are completely compensated and the unmatched ones are not amplified during sliding motion sufficient conditions for the stochastic admissibility of the corresponding sliding mode dynamics are presented and a fuzzy smc law is constructed to ensure the reaching condition despite uncertainties the applicability and effectiveness of our approach are verified by simulations on an inverted pendulum system

persistence in dynamical systems soit un espace metrique localement compact de metrique d et soit e un sous ensemble clos de a frontiere e et interieur e on suppose que l on a un flot continu 7b f defini sur e tel que e est invariant par 7b f on considere la notion de persistance et on donne un critere de persistance

delay estimation based adaptive fuzzy memory control for a class of uncertain nonlinear time delay systems abstract this paper is concerned with the problem of adaptive fuzzy memory control for a class of uncertain nonlinear systems with unknown time delays compared with the existing results the restrictions on the time delayed functions are overcome and the unknown delay parameters are estimated by establishing the piecewise adaptive laws based on the delay estimations and the backstepping technique a novel adaptive fuzzy memory output feedback controller is designed for the analysis of stability the cubic lyapunov functions with absolute value are constructed it is proved that the proposed control scheme can guarantee that all the signals in the closed loop system are bounded and the tracking error converges to a small neighborhood of the origin two practical simulation examples are given to demonstrate the effectiveness of the proposed control scheme