bluuebunny/crossref_metadata_2025 huggingface dataset:
Robust Adaptive Control of Robots Using Neural Network: Global Stability
Authors: Kwan C. , Dawson D.M. , Lewis F.L.
Published: September 2010

A desired compensation adaptive law‐based neural network (DCAL‐NN) controller is proposed for the robust position control of rigid‐link robots. The NN is used to approximate a highly nonlinear function. The controller can guarantee theglobalasymptotic stability of tracking errors and boundedness of NN weights. In addition, the NN weights here are tuned on‐line, withno offline learning phase required.When compared with standard adaptive robot controllers, we do not require linearity in the parameters, or lengthy and tedious preliminary analysis to determine a regression matrix. The controller can be regarded asa universal reusable controllerbecause the same controller can be applied to any type of rigid robots without any modifications. A comparative simulation study with different robust and adaptive controllers is included.
Adaptive Neural Network Control For Smart Materials Robots Using Singular Perturbation Technique
Authors: Ge S.S. , Lee T.H. , Wang Z.P.
Published: September 2010

In this paper, an adaptive neural network controller is presented for smart materials robots using Singular Perturbation techniques by modeling the flexible modes and their derivatives as the fast variables and link variables as slow variables. The neural network (NN) controller is to control the slow dynamics in order to eliminate the need tor the tedious dynamic modeling and the error prone process in obtaining the regressor matrix. In addition, inverse dynamic model evaluation is not required and the time‐consuming training process is avoided except for initializing the NNs based on the approximate function values at the initial posture at timet=0. The smart materials bonded along the links are used to active suppress the residue vibration. Simulation results have shown that the controller can control the system successfully and effectively.
Nonlinear Fly‐By‐Throttle H∞ Control Using Neural Networks
Authors: Yu Gwo‐Ruey
Published: September 2010

A neural network approach to gain scheduling H∞ controllers for propulsion controlled aircraft (PCA) systems is introduced. The PCA system is applied to backup control of aircraft experiencing control surface failure. The H∞ technology is applied to the problem of matching the crippled aircraft and the nominal model. Various H∞ controllers at various flight conditions are used to train radial basis function networks (RBFN), which can then be used as the nonlinear controller. Simulation on an L‐1011 under fly‐by‐throttle control demonstrates that the RBFN controller can stabilize the crippled airplane to obtain the desired model and possesses robustness against the engine delay.
An Lmi Approach To Non‐Fragile Guaranteed Cost Control Of Uncertain Discrete Time‐Delay Systems
Authors: Yee Jiong‐Sang , Wang Jian Liang , Yang Guang‐Hong
Published: September 2010

This paper studies the non‐fragile Guaranteed Cost Control (GCC) problem via memoryless state‐feedback controllers for a class of uncertain discrete time‐delay linear systems. The systems are assumed to have norm‐bounded, time‐varying parameter uncertainties in the state, delay‐state, input, delay‐input and state‐feedback gain matrices. Existence of the guaranteed cost controllers are related to solutions of some linear matrix inequalities (LMIs). The non‐fragile GCC state‐feedback controllers are designed based on a convex optimization problem with LMI constraints to minimize the guaranteed cost of the resultant closed‐loop systems. Numerical examples are given to illustrate the design methods.
Using the Sliding‐Mode PWM Method in an Anti‐Lock Braking System
Authors: Wu Ming‐Chin , Shih Ming‐Chang
Published: September 2010

This study attempted to integrate the Pulse Width Modulation (PWM) method and sliding mode control theory to develop quasi‐continuous control for an automobile anti‐lock braking system. Two controllers are designed in this study. One applies directly by applying quasi‐continuous control to achieve ABS slip control. In addition, the quasi‐continuous control method was applied to develop pressure tracking control, and then this pressure tracking controller and the acceleration signal of the tire were implemented together to construct an anti‐lock braking controller. Both controllers were investigated on a dynamic test stand. Wet road braking was simulated by spraying water on the contact surface between the tire and the flywheel. Excellent braking results not only verify the performance of the sliding PWM method but also provide an alternative to an ABS controller without slip feedback.
Input Disturbance Estimation For Linear Systems in The Bounded Noise Context
Authors: Grobov Igor D.
Published: September 2010

A method is presented for constructing input disturbance estimates for a linear system using noisy observations. The input disturbance of the dynamic system and the observation noise are assumed to be unknown but bounded. In addition, the structural characteristics of the input disturbance are given in the form of the maximum possible change of its magnitude per sampling time. The input disturbance represents a wide category of system uncertainties. A recursive procedure for obtaining disturbance set estimates is derived. The procedure is easy to implement and is a competitive technique comparative to the classical estimation schemes. Simulation results demonstrate the performance of the given techniques.
A Robust Speed Controller With VSS Tuning Of Disturbance Cancellation For An Induction Motor Drive
Authors: Liaw C.M. , Chao K.H. , Guo R.S.
Published: September 2010

A robust speed controller for an induction motor drive that achieves quantitative control and considers the effects of nonlinearities is presented in this paper. First, a two‐degree‐of‐freedom controller (2DOFC) is designed according to the estimated model for the nominal case and given speed specifications. Then, the control characteristics and limitations of the conventional robust controller are investigated. Accordingly, a modified robust speed controller based on disturbance cancellation is developed, wherein tuning of disturbance cancellation is performed via a variable structure system (VSS) controller. The estimated model mismatched error and its derivative are used as the driven inputs of the VSS controller. Greater robustness and flexibility in achieving a compromise between control effort and control performance can be achieved using the proposed controller. The effectiveness of the proposed controller is demonstrated by simulation and measured results.
Disturbance Attenuation for Beam Equations Using Stiffness Control
Authors: Guo Bao‐Zhu , Luo Zheng‐Hua
Published: September 2010

The paper presents a control method for disturbance attenuation of a rotating flexible beam using strain feedback. It is shown that the strain feedback, if appropriately used, can increase the stiffness of the flexible beam and, thus, attenuate external disturbances. The disturbance responses of the feedback controlled closed‐loop equation are also derived, and simulation results are provided to demonstrate the effect of the proposed control law.
A Novel Robust Pid Controller Design By Fuzzy Neural Network
Authors: Lee Ching‐Hung , Lee Yi‐Hsiung , Teng Ching‐Cheng
Published: September 2010

In this paper, we propose a robust PID controller tuning method for parametric uncertainty systems (or interval plant family) using fuzzy neural networks (FNNs). This robust controller is based on robust gain and phase margin (GM/PM) specifications that satisfy user requirements. Here, the FNN system is used to identify the relation between the PID controller parameters and robust GM/PM. We can use the trained FNN system to determine the parameters of the PID controllers in order to satisfy robust GM/PM specifications that guarantee robustness and performance. Simulation results are shown to illustrate the effectiveness of the robust controller scheme.
Robust Controller Design And Pid Tuning For Multivariable Processes
Authors: Tan Wen , Chen Tongwen , Marquez Horacio J.
Published: September 2010

In this paper, a robust controller design method is first formulated to deal with both performance and robust stability specifications for multivariable processes. The optimum problem is then dealt with using a loop‐shaping H∞ approach, which gives a sub‐optimal solution. Then a PID approximation method is proposed to reduce a high‐order controller. The whole procedure involves selecting several parameters and the computation is simple, so it serves as a PID tuning method for multivariable processes. Examples show that the method is easy to use and the resulting PID settings have good time‐domain performance and robustness.
The Mimo Predictive Pid Controller Design
Authors: Moradi M.H. , Katebi M.R. , Johnson M.A.
Published: September 2010

This paper is concerned with the design of Multi‐Inputs and Multi‐Outputs (MIMO) predictive PID controllers, which have similar performance to that obtainable from model‐based predictive controllers. A new PID control structure is defined which incorporates the prediction of future outputs and uses future set point. A method is proposed to calculate the optimal values of the PID gains from generalised predictive control results. A decentralized version of the predictive PID controllers is presented and the stability of the closed loop system is studied. Simulation studies demonstrate the superior performance of the proposed controller compared with a conventional PID controller. The results are also compared with generalised predictive control solutions.
A Design Of Multiloop Predictive Self‐Tuning Pid Controllers
Authors: Katayama Masaru , Yamamoto Toru , Mada Yasuhiro
Published: September 2010

In this paper, a new design scheme of multiloop predictive self‐tuning PID controllers is proposed for multivariable systems. The proposed scheme firstly uses a static pre‐compensator as an approximately decoupling device, in order to roughly reduced the interaction terms of the controlled object. The static matrix pre‐compensator is adjusted by an on‐line estimator. Furthermore, by regarding the approximately decoupled system as a series of single‐input single‐output subsystems, a single‐input single‐output PID controller is designed for each subsystem. The PID parameters are calculated on‐line based on the relationship between the PID control and the generalized predictive control laws. The proposed scheme is numerically evaluated on a simulation example.
Pd Control For Active Vibration Damping In An Underactuated Nonlinear System
Authors: Aguilar‐lbÅAñez Carlos , Sira‐Ramirez Hebertt
Published: September 2010

A Lyapunov based control approach is proposed for the specification of the constant gains of an asymptotically stabilizing Proportional‐Derivative (PD) controller, devised to damp the oscillations of an underactuated nonlinear mechanical system. The system consists of a rigid beam, sliding on the interior of a spherical surface, and an actuated cart, located over the beam, which acts as an active vibration damping element.
Learning Motion of Dexterous Manipulation For A Pair of Multi‐DOF Fingers With Soft‐TIPS
Authors: Nguyen Pham Thuc Anh , Arimoto Suguru
Published: September 2010

This paper shows that a pair of dual multi‐DOF fingers with soft‐tips can learn iteratively a desired periodic motion of manipulation of an object if sensory feedback signals are designed adequately. It is shown that dynamics of the overall fingers and object system satisfy passivity but residual error dynamics for a given periodic posture of the object and a fixed value of contact force satisfy output‐dissipativity only in an approximate sense. Numerical simulation results are presented which show that the pair of fingers manipulating an object is capable of learning iteratively a variety of dexterous motions with a good performance.
Recursive Direct Learning of Control Efforts for Trajectories with Different Magnitude Scales
Authors: Xu Jian‐Xin , Xu Jing , Viswanathan Badrinath
Published: September 2010

Direct learning control (DLC) schemes have been developed recently to address non‐repeatable trajectory tracking problems. Unlike conventional iterative learning schemes, DLC schemes learn a set of unknown basis function vectors which can be used to generate the desired control profile of a new trajectory. DLC schemes use all available trajectory tracking information to obtain the unknown basis function vectors in a Least Squares and pointwise manner. A drawback of DLC is that the inverse matrix calculation is inevitable, which is time consuming and may result in singularities due to the batch processing nature.A Recursive Direct Learning Controlmethod is proposed which learns the basis function vectors meanwhile overcomes the implementation difficulties in DLC schemes. The focus of this paper is on learning the control profile of trajectories with same operation period but different magnitude scales. The recursive learning method makes use of one trajectory information at a time, thus avoids the batch processing. The scheme is first developed for a class of nonlinear time varying systems and then extended to cover more general classes of nonlinear systems including robotic manipulator dynamics. Extensive simulation results on a two‐link robotic model are provided to confirm the features of the proposed algorithm.
Iterative Learning Control Using Adjoint Systems and Stable Inversion
Authors: Kinosita Koji , Sogo Takuya , Adachi Norihiko
Published: September 2010

In this paper, we investigate iterative learning control (ILC) for non‐minimum phase systems from a novel viewpoint. For non‐minimum phase systems, the magnitude of a desiredinput obtained by ILC using forward‐time updating and Silverman's inversion are too large because of the influence of the unstable zeros. On the other hand, stable inversion constructs a bounded desired input by using non‐causal inverse for non‐minimum phase systems.

In this paper, we first clarify that ILC using an adjoint system achieves the desired input defined by stable inversion. Hence, ILC using an adjoint system is an effective method for the control of non‐minimum phase systems with uncertainty. However, a useful convergence condition of ILC using an adjoint system was not achieved. Next, we develop a simple convergence condition in the frequency domain.
Model‐Based Learning Controls And Their Comparisons Using Lyapunov Direct Method
Authors: Qu Zhihua , Xu Jian‐Xin
Published: September 2010

Model‐based learning control of nonlinear systems is studied. Two types of learning algorithms, described by differential equations and/or difference equations to learn unknown time functions, are designed and compared using the Lyapunov's direct method. The time functions to be learned are classified into several classes according to their properties such as continuity, periodicity, and value at the origin of the state space. Conditions are found for iterative learning controls to achieve asymptotic stability and asymptotic learning convergence. For a comparative study, learning capability of a control is defined and, using the criterion, other model‐based controls with learning capability such as adaptive controls and robust controls are investigated. Through the study, iterative learning control is shown to be the one best suited for learning unknown time functions ofknownperiod. Finally, it is shown for the first time that an iterative learning control is directly applicable to systems described by nonlinear partial differential equation.
Robust Analysis of Discrete‐Time Lur'e Systems with Slope Restrictions Using Convex Optimization
Authors: Banjerdpongchai David , Kimura Hidenori
Published: September 2010

This paper considers robust stability and robust performance analysis for discrete‐time linear systems subject to nonlinear uncertainty. The uncertainty set is described by memoryless, time‐invariant, sector bounded, and slope restricted nonlinearities. We first give an overview of the absolute stability criterion based on the Lur'e‐Postkinov Lyapunov function, along with a frequency domain condition. Subsequently, we derive sufficient conditions to compute the upper bounds of the worst case H2 and worst case H∞ performance. For both robust stability testing and robust performance computation, we show that these sufficient conditions can be readily and efficiently determined by performing convex optimization over linear matrix inequalities.
A Dual‐Type Method Based Algorithm for Nonlinear Large Network Optimization Problems
Authors: Lin Shin‐Yeu , Lin Shieh‐Shing , Lin Ch'i‐Hsin
Published: September 2010

In previous research, we have proposed a Dual Projected Pseudo Quasi Newton (DPPQN) method which differs from the conventional Lagrange relaxation method by treating the inequality constraints as the domain of the primal variables in the dual function and using Projection Theory to handle the inequality constraints. We have combined this dual‐type method with a Projected Jacobi (PJ) method to solve nonlinear large network optimization problems with decomposable inequality constraints, and have achieved several attractive features.

To retain the attractive features and to remedy the flaw of the previous method, in the current paper, we propose an active set strategy based DPPQN method to solve the projection problem formed by coupling functional inequality constraints. This method associated with the DPPQN method and the PJ method can be used to solve general nonlinear large network optimization problems. We present this algorithm, demonstrate its computational efficiency through numerical simulations and compare it with the previous method.
Adaptive Precision Control of Permanent Magnet Linear Motors
Authors: Huang S.N. , Tan K.K. , Lee T.H.
Published: September 2010

This paper describes an adaptive control scheme developed for precision control of DC permanent magnet linear motors (PMLM). A sliding surface is defined on which a self‐tuning version of robust control capable of achieving tight set‐point regulation is developed, where the control gains are tuned using an adaptive algorithm. Parameter and states convergence properties of the adaptive controller are derived based on the control law formulated. Finally, simulation study and a real‐time experiment are provided to evaluate the performance of the proposed control system.
An Output Tracking VSS Control in The Presence of A Class of Mismatched Uncertainties
Authors: Lee Bore‐Kuen , Mau Li‐Wei
Published: September 2010

A variable structure control system for output tracking in the presence of a class of mismatched uncertainty is studied in this paper. For the mismatched uncertainty, we use an unknown input observer to estimate the states and then apply the VSS control methodology to attain asymptotic output tracking for an arbitrary desired trajectory. The robustness of the VSS control system in the presence of mismatched uncertainty is analyzed using the quadratic stability approach.
Simple Recurrent Neural Network‐Based Adaptive Predictive Control for Nonlinear Systems
Authors: Li Xiang , Chen Zengqiang , Yuan Zhuzhi
Published: September 2010

Making use of the neural network universal approximation ability, a nonlinear predictive control scheme is studied in this paper. On the basis of a uniform structure of simple recurrent neural networks, a one‐step neural predictive controller (OSNPC) is designed. The whole closed‐loop system's asymptotic stability and passivity are discussed, and stable conditions for the learning rate are determined based on the Lyapunov stability theory for the whole neural system. The effectiveness of OSNPC is verified via exhaustive simulations.
Adaptive Dynamic Output Feedback Stabilisation of Nonlinear Systems
Authors: Ilchmann A. , Isidori A.
Published: September 2010

An adaptive dynamical output feedback controller is introduced for a class of nonlinear non‐minimum phase systems. This adaptive controller achieves practical stabilisation, that means the output will asymptotically tend to a pre‐specified neighbourhood of the origin. In case of linear systems, we can even prove adaptive stabilisation.
Controller Design Using Walsh‐Basis‐Function Neural Networks
Authors: Chen Shing‐Chia , Chen Wen‐Liang
Published: September 2010

ABSTRACT

This paper investigates the function approximation problem by using Walsh functions to establish a Walsh‐basis‐function neural network (WBFNN). The proposed novel system avoids the possible heavy computation problem of a controller usually encountered in adaptive neural controller design. With the developed adaptation scheme combined with the sliding mode control strategy for a class of nonlinear systems, the proposed WBFNN‐based controller can guarantee global stability of the closed‐loop system in the Lyapunov sense. The output tracking error then converges to zero asymptotically, and boundedness of all the signals in the whole system is ensured. Simulation validation for a nonlinear unstable system was performed to verify the effectiveness of the proposed controller design.
A Fuzzy Logic Based Discrete Mode Power System Stabilizer
Authors: Chetty M.
Published: September 2010

A Fuzzy Power System Stabiliser (FPSS) design using discrete inputs is presented. The FPSS basically utilises only one measurable plant signal as input, namely, the generator speed. The speed signal is discretised resulting in three inputs to the FPSS. A simplified power system represented by a single generator transmitting power to the infinite bus is considered for investigations. The system is simulated in simulink while the FPSS is implemented using Fuzzy Logic Toolbox. There are six rules for the fuzzification and two rules for defuzzification. To provide satisfactory behaviour in the entire region of operation of the generator, additional signals namely, generator's active and reactive power are used as inputs to FPSS. Simulation studies show the superior performance of the proposed FPSS compared to an optimally designed Conventional Power System Stabiliser.
Systematic Gain‐Scheduling Control Design: A Missile Autopilot Example
Authors: Wu Fen , Packard Andy , Balas Gary
Published: September 2010

The missile autopilot was designed using linear parameter‐varying (LPV) control techniques. The controller provides exponential stability guarantee and performance bound in terms of induced L2 norm for the missile plant. The systematic gain‐scheduling approach is motivated by the recent development in LPV control theory and provides a well founded and systematic procedure for high performance missile autopilot design problem.
Adaptive Control For Robotic Manipulators Executing Multilateral Constrained Task
Authors: Kawasaki Haruhisa , Taniuchi Ryo
Published: September 2010

This paper presents a model‐based adaptive control in task coordinates for robotic manipulators executing multilateral constrained tasks The controller works based on the concept of orthogonality between force and motion in the subspaces derived from the constraints. The control gains are independently adjustable in each subspace. The friction force, depending on the contact force, is compensated adaptively. Asymptotic convergence for both force and motion tracking errors is guaranteed by the Lyapunov‐Like Lemma. Experimental results obtained using a 3 D.O.F. robot are given.
Restricted Structure Control Loop Performance Assessment For Pid Controllers And State‐Space Systems
Authors: Grimble M.J.
Published: September 2010

A novel H2 optimal control performance assessment and benchmarking problem is considered for discrete‐time state‐space multivariable systems, where the structure of the controller is assumed to be fixed apriori. The controller structure may be specified to be of PID, reduced order, or lead/lag forms. The theoretical problem considered is to represent the state‐space model in discrete polynomial matrix form and to then obtain the causal, stabilising, controller, of a prespecified form, that minimises an H2 criterion. This then provides the performance measure against which other controllers can be judged. The underlying practical problem of importance is to obtain a simple method of performance assessment and benchmarking low order controllers.

The main theoretical step is to derive a simpler cost‐minimization problem whose solution can provide both the full order and restricted structure (PID) optimal benchmark cost values. This problem involves the introduction of spectral factor and diophantine equations and is solved via a Wiener type of cost‐function expansion and simplification. The numerical solution of this problem is straightforward and involves approximating the simplified integral criterion by a fixed number of frequency points. The main benchmarking theorem applies to multivariable systems that may be unstable, non‐minimum phase and non‐square.
Nonlinear Adaptive Tracking Of Surface Vessels With Exogenous Disturbances
Authors: Fu Simon Hsu‐Sheng , Haddad Wassim M.
Published: September 2010

Significant interaction disturbances in surface vessel maneuvering and position tracking often require high precision controllers. In this paper we develop a coupled nonlinear two‐vessel tracking model for addressing the problem of underway replenishment. Next, using this model we develop an inverse optimal adaptive controller that guarantees disturbance rejection to exogenous disturbances while maintaining a desired separation between the two vessels.
Adaptive L2 Disturbance Attenuation Of Hamiltonian Systems With Parametric Perturbation And Application To Power Systems
Authors: Shen Tielong , Ortega Romeo , Lu Qiang , Mei Shengwei , Tamura Katsutoshi
Published: September 2010

This paper deals with the problem of L2 disturbance attenuation for Hamiltonian systems. We first show that the L2 gain from the disturbance to a penalty signal may be reduced to any given level if the penalty signal is defined properly. Then, an adaptive version of the controller will be presented to compensate the parameter perturbation. When the perturbed parameters satisfy a suitable matching condition, it is easy to introduce the adaptive mechanism to the controller. Another contribution of this paper is to apply the proposed method to the excitation control problem for power systems. An adaptive L2 controller for the power system is designed using the proposed method and a simulation result with the proposed controller is given.
Output Regulation Of A Class Of Singular Nonlinear Systems With The Normal Output Feedback Controller
Authors: Wang Wei , Huang Jie
Published: September 2010

Existing results for output regulation of singular nonlinear systems via normal output feedback control require the normalizability assumption. In this paper, we will show that, for a large class of singular nonlinear systems, it is possible to construct a normal output feedback control to solve the regulation problem without the normalizability assumption. The major result is illustrated by an example.
An Automatic Building Approach To Special Takagi‐Sugeno Fuzzy Network For Unknown Plant Modeling And Stable Control
Authors: Juang Chia‐Feng
Published: September 2010

In previous studies, several stable controller design methods for plants represented by a special Takagi‐Sugeno fuzzy network (STSFN) have been proposed. In these studies, the STSFN is, however, derived directly from the mathematical function of the controlled plant. For an unknown plant, there is a problem if STSFN cannot model the plant successfully. In order to address this problem, we have derived a learning algorithm for the construction of STSFN from input‐output training data. Based upon the constructed STSFN, existing stable controller design methods can then be applied to an unknown plant. To verify this, stable fuzzy controller design by parallel distributed compensation (PDC) method is adopted. In PDC method, the precondition parts of the designed fuzzy controllers share the same fuzzy rule numbers and fuzzy sets as the STSFN. To reduce the controller rule number, the precondition part of the constructed STSFN is partitioned in a flexible way. Also, similarity measure together with merging operation between each neighboring fuzzy set are performed in each input dimension to eliminate the redundant fuzzy sets. The consequent parts in STSFN are designed by correlation measure to select only the significant input terms to participate in each rule's consequence and reduce the network parameters. Simulation results in the cart‐pole balancing system have shown that with the proposed STSFN building approach, we are able to model the controlled plant with high accuracy and, in addition, can design a stable fuzzy controller with small parameter number.
Pid Performance Tuning Methods For A Robotic Manipulator Based On Iss
Authors: Choi Youngjin , Chung Wan Kyun
Published: September 2010

This paper proposes performance tuning methods for a PID controller for a robotic manipulator. The design procedures of an inverse optimal PID control method employed to assure extended disturbance input‐to‐state stability (ISS) are suggested. The performance tuning methods for an inverse optimal PID control are derived from the performance limitation and the bounds of the state vector. Also, the effects of proportional and integral gains are considered in evaluating the performance tuning. Finally, we show the validity of the performance tuning methods through the experiment on a robotic manipulator.
Optimal Process Control Using Neural Networks
Authors: Padhi Radhakant , Balakrishnan S. N.
Published: September 2010

Infinite time optimal controllers have been designed for a dispersion type tubular reactor model by using the framework of adaptive critic optimal control design. For the reactor control problem, which is governed by two coupled nonlinear partial differential equations, an optimal controller synthesis is presented through two sets of neural networks. One set of neural networks captures the relationship between the states and the control, whereas the other set of networks captures the relationship between the states and the costates. This innovative approach embeds the solutions to the optimal control problem for a large number of initial conditions in the domain of interest. Although the main aim of this paper is to solve a process control problem, the methodology presented here can be viewed as a practical computational tool for many problems associated with nonlinear distributed parameter systems. Numerical results demonstrate the viability of the proposed method.
Sliding Mode Control For Invertible Systems Based On A Direct Design Of Interactors
Authors: Mita Tsutomu , Nam Taek‐Kun , Xin Xin
Published: September 2010

The sliding mode tracking control is reviewed and extended so that it can be applicable to general invertible systems. The main advantage of this paper is a direct and numerical stable design of interactors for general invertible systems which is performed by calculating infinite eigenbasis of system matrices.
Plotting Robust Root Locus For Polynomial Families Of Multilinear Parameter Dependence Based On Zero Inclusion/Exclusion Tests
Authors: Hwang Chyi , Yang Shih‐Feng
Published: September 2010

The Mapping Theorem by Zadeh and Desoer [17] is a sufficient condition for the zero exclusion of the image or value set of an m‐dimensional box B under a multilinear mapping f: Rm → C, where R and C denote the real line and the complex plane, respectively. In this paper, we present a sufficient condition for the zero inclusion of the value set f(B). On the basis of these two conditions and the iterative subdivision of the box B, we propose a numerical procedure for testing whether or not the value set f(B) includes the origin. The procedure is easy to implement and is more efficient than that based on constructing the value set f(B) explicitly. As an application, the proposed zero inclusion test procedure is used along with a homotopy continuation algorithm to trace out the boundary curves of the robust root loci of polynomial families with multilinear parametric uncertainties.
Robust State Feedback Control Through Actuators With Generalized Sector Nonlinearities And Saturation
Authors: Hsu Chih‐Chin , Fong I‐Kong
Published: September 2010

In control systems, actuators often have nonlinear characteristics that can not be neglected. For linear systems driven by actuators satisfying the generalized sector condition, a robust state feedback controller synthesis method is proposed to achieve the ultimate boundedness control. The method is based on the linear matrix inequality approach and is easy to apply. As an important special case of the generalized sector condition, the saturation characteristic of actuators is discussed separately, and non‐conservative results are obtained.
New Sampling Mode For Linear Time‐Varying Systems
Authors: Guo Ge , Qiang Qiu
Published: September 2010

It is proved in this paper that the nonequidistant periodic sampling pattern and zero‐order hold operations do not change the controllability and observability of time variant linear systems during the procedure of discretization for digital control. This result is an extension of the corresponding result for time invariant cases presented in [5].
FREQUENCY DOMAIN ANALYSIS OF FAST AND SLOW MOTIONS IN SLIDING MODES
Authors: Boiko Igor
Published: September 2010

A frequency domain approach to analysis of fast and slow motions in sliding modes is proposed in the paper. The proposed approach is based on the construction of a relay system that is equivalent to the original sliding mode control system and on the application of the locus of a perturbed relay system method. A precise model of the fast motions and a non‐reduced order model of the slow motions for the SISO linear time‐invariant case are obtained as a result of the proposed approach.
SECOND‐ORDER NONSINGULAR TERMINAL SLIDING MODE DECOMPOSED CONTROL OF UNCERTAIN MULTIVARIABLE SYSTEMS
Authors: Feng Yong , Zheng Xuemei , Yu Xinghuo
Published: September 2010

This paper proposes a second‐order nonsingular terminal sliding mode decomposed control method for multivariable linear systems with internal parameter uncertainties and external disturbances. First, the systems are converted into the block controllable form, consisting of an input‐output subsystem and a stable internal dynamic subsystem. A special second‐order non‐singular terminal sliding mode is proposed for the input‐output subsystem. The control law is designed to drive the states of the input‐output subsystem to converge to the equilibrium point asymptotically. Then the states of the stable zero‐dynamics of the system converge to the equilibrium point asymptotically. The method proposed in the paper has advantages for higher‐dimensional multivariable systems, in the sense that it simplifies the design and makes it possible to realize a robust decomposed control. Meanwhile, because of the adoption of the second‐order sliding mode, the control signal is continuous. Simulation results are presented to validate the design.
OPTIMAL AND ROBUST SLIDING MODE CONTROL FOR LINEAR SYSTEMS WITH MULTIPLE TIME DELAYS IN CONTROL INPUT
Authors: Basin Michael , Fridman Leonid , Rodriguez‐González Jesús , Acosta Pedro
Published: September 2010

This paper presents the optimal regulator for a linear system with multiple time delays in control input and a quadratic criterion. The optimal regulator equations are obtained using the duality principle, which is applied to the optimal filter for linear systems with multiple time delays in observations. Performance of the obtained optimal regulator is verified in the illustrative example against the best linear regulator available for linear systems without delays. Simulation graphs and comparison tables demonstrating better performance of the obtained optimal regulator are included. The paper then presents a robustification algorithm for the obtained optimal regulator based on integral sliding mode compensation of disturbances. The general principles of the integral sliding mode compensator design are modified to yield the basic control algorithm oriented to time‐delay systems, which is then applied to robustify the optimal regulator. As a result, the sliding mode compensating control leading to suppression of the disturbances from the initial time moment is designed. The obtained robust control algorithm is verified by simulations in the illustrative example.
OUTPUT‐FEEDBACK CONTROL OF CONTAINER CRANES: A COMPARATIVE ANALYSIS
Authors: Bartolini Giorgio , Pisano Alessandro , Usai Elio
Published: September 2010

A number of advanced control systems devoted to speed up the operation of container cranes have been proposed in the literature. In this paper the authors present a comparative analysis involving different control techniques applied to a laboratory sized gantry crane prototype. The comparison is carried out by means of both simulations and real experiments.

In particular, two sliding‐mode control (SMC) schemes, a P.I.‐based controller and a linear observer‐based time‐varying feedback scheme are compared. The control algorithms have been suited to the path tracking problem of a suspended mass in order to make the comparison more effective.
THE PERFORMANCE OF DISCRETE LINEAR TIME VARYING CONTROL OF LINEAR PERIODIC PLANTS
Authors: Zhang Jingxin , Zhang Cishen
Published: September 2010

This paper analyzes the performance of discrete linear time varying (LTV) control of discrete linear periodically time varying (LPTV) plants for l2 disturbance rejection. It extends the results of [11–13] for linear periodic controllers to general LTV control of LPTV plants. It is shown that LPTV control provides strictly better control performance than linear strictly time varying control for LPTV plants. The analysis is carried out in frequency domain. This approach provides not only new results on disturbance rejection of LTV control but also some new insight into properties of general LTV systems.
ROBUST GAIN‐SCHEDULED CONTROL OF A VERTICAL TAKEOFF AIRCRAFT WITH ACTUATOR SATURATION VIA THE LMI METHOD
Authors: Chen P. C. , Jeng Y. F. , Chang Y. H. , Wang Y. M. , Chen G.
Published: September 2010

This paper presents a robust gain‐scheduled approach for the control of a vertical/short takeoff. and landing (V/STOL) aircraft. The nonlinear aircraft dynamics exhibit non‐minimum phase characteristics arising from the parasitic coupling effect between the aircraft's lateral force and rolling moment. The undesired coupling effect also causes modelling uncertainy of the aircraft dynamics. The nonlinear aircraft dynamics are considered to be composed of a nominal linear parameter varying (LPV) system and a linear system with a norm bounded uncertainy matrix multiplied by the parasitic uncertain non‐minimum phase coupling parameter. The nominal LPV system is considered to be affinely dependent on a measurable varying parameter. The ranges of the varying parameter and its variation as well as its parasitic induced uncertain matrix are addressed by introducing the parameter‐dependent invariant ellipsoid interpretation for dealing with the issue of affinely quadratic stabilization. In this paper, the relations among the magnitude of actuator saturation, the maximum achievable relative stability, and the sustainable coupling uncertainty are investigated for the considered robust gain‐scheduled design.
OPTIMAL MECHANISM DESIGN AND DYNAMIC ANALYSIS OF A 3‐LEG 6‐DOF LINEAR MOTOR BASED PARALLEL MANIPULATOR
Authors: Hwang Thong‐Shing , Liao Ming‐Yang
Published: September 2010

This paper presents the optimal mechanism design and dynamic analysis of a prototype 3‐leg 6‐DOF (degree‐of‐freedom) parallel manipulator. Inverse kinematics, forward kinematics, inverse dynamics and working space characterizing the platform motion are derived. In the presented architecture, the base platform has three linear slideways individually actuated by a synchronous linear servo motor, and each extensible vertical link connecting the upper and base platforms is actuated by an inductive AC servo motor. The linear motors contribute high‐speed movements to the upper platform. This kind of architecture using hybrid (linear and AC) motors yields high level performance of motions, especially in the working space. The novel result of maximal working angles is the significant contribution of this architecture. The Taguchi Experimental Method is applied to design the optimal mechanism of the platform system, and the result is used as the actual data to build this system.
NANO TRAJECTORY CONTROL OF MULTILAYER LOW‐VOLTAGE PZT BENDER ACTUATOR SYSTEMS
Authors: Hwang Chih‐Lyang , Jan Chau
Published: September 2010

In this paper, nano trajectory control of the multilayer low‐voltage PZT (lead zirconate titanate) bender actuator system (MLVPZTBAS) is developed. System analyses in of the hysteresis characteristic, frequency response and sinusoidal response, were conducted to evaluate the system features. Because not all of the states of the MLVPZTBAS are directly available, an observer is required to estimate the states. The proposed scheme contains feedback linearization with a sliding‐mode controller and a state observer to estimate the system states of the MLVPZTBAS. The sliding‐mode control possesses an equivalent control and a switching control. To track a trajectory dominant by a specific frequency, a reference model consisting of desired amplitude and phase features is established. The equivalent control using the signals from the observer and the reference model is designed so as to produce the desired control behavior. Due to the existence of modeling and estimation errors, the switching control is then employed to achieve robust performance. Experiments on the MLVPZTBAS were carried out to verify the usefulness of the proposed control.
MODELING AND CONTROL OF ELEVATORS BY STATECHARTS
Authors: Huang Yi‐Sheng , Chung Sheng‐Luen , Jeng Mu‐Der
Published: September 2010

Statechart has been utilized as a visual formalism for the modeling of complex and interactive systems for its illuminating features on describing properties of causality, concurrency, and synchronization. This paper presents the application of satechart to the modeling, design and implementation of an elevator system, whose system behavior involves aggregating complexity of state descriptions, and imposition of underlying control policy. Based on the operational flow of an elevator, we derive the associated statechart model by looking into the inherent hierarchical structure of the elevator. The advantage of the proposed approach is the clear presentation of system behavior in terms of conditions and events that cause the transitions in system dynamics. Implementation of the controlled elevator based on the modeled statechart is also presented.
NON‐BLOCKING SUPERVISORY CONTROL FOR INITIALISED RECTANGULAR AUTOMATA
Authors: Spathopoulos Michael P.
Published: September 2010

We consider the problem of supervisory control for a class of rectangular automata and more specifically for compact rectangular automata with uniform rectangular activity, i.e. initialised. The supervisory controller is state feedback and disables discrete‐event transitions in order to solve the non‐blocking forbidden state problem. The non‐blocking problem is defined under both strong and weak conditions. For the latter maximally permissive solutions that are computable on a finite quotient space characterised by language equivalence are derived.
DISCRETIZED SUB‐OPTIMAL TRACKER FOR NONLINEAR CONTINUOUS TWO‐DIMENSIONAL SYSTEMS
Authors: Chen Chia‐Wei , Tsai Jason Sheng‐Hong , Shieh Leang‐San
Published: September 2010

The discretized quadratic sub‐optimal tracker for nonlinear continuous two‐dimensional (2‐D) systems is newly proposed in this paper. The proposed method provides a novel methodology for indirect digital redesign for nonlinear continuous 2‐D systems with a continuous performance index. This includes the following features: (1) the 2‐D optimal‐linearization approach of the nonlinear 2‐D Roesser's model (RM), (2) the dynamic programming‐based discretized quadratic optimal tracker for linear continuous 2‐D systems, (3) the steady‐state discretized quadratic sub‐optimal tracker for linear continuous 2‐D systems, and (4) the discretized quadratic sub‐optimal tracker for nonlinear continuous 2‐D systems. Illustrative examples are presented to demonstrate the effectiveness of the proposed procedure.
DIRECT ADAPTIVE FEEDBACK DESIGN FOR LINEAR DISCRETE‐TIME UNCERTAIN SYSTEMS
Authors: Fu Simon Hsu‐Sheng , Cheng Chi‐Cheng
Published: September 2010

In this paper, we develope a direct adaptive control framework for linear discrete‐time uncertain MIMO systems, based on assumptions that the system is controllable and the difference of the unknown system matrix from the stable solution is bounded by a given value. The proposed framework is Lyapunov‐based, and the controller guarantees adaptive stabilization. In addition, the adaptive laws are characterized by means of Kronecker calculus. Furthermore, the results can easily be extended to time‐varying cases, where the deviation from the stable solution is bounded, and the controllability assumption holds. The controller not only tolerates plant deviation, but also guarantees asymptotical stability of the closed‐loop system. Two numerical examples are provided to demonstrate the performance of the controller.
EXISTENCE ANALYSIS FOR LIMIT CYCLES OF RELAY FEEDBACK SYSTEMS
Authors: Lin Chong , Wang Qing‐Guo , Lee Tong Heng
Published: September 2010

This paper is concerned with the problems on the existence of limit cycles for SISO linear systems under relay feedback. A sufficient condition is given based on the Brouwer's fixed point theorem.
RANDOM APPROXIMATED GREEDY SEARCH FOR FEATURE SUBSET SELECTION
Authors: Gao Feng , Ho Yu‐Chi
Published: September 2010

We propose a sequential approach called Random Approximated Greedy Search (RAGS) in this paper and apply it to the feature subset selection for regression. It is an extension of GRASP/Super‐heuristics approach to complex stochastic combinatorial optimization problems, where performance estimation is very expensive. The key points of RAGS are from the methodology of Ordinal Optimization (OO). We soften the goal and define success as good enough but not necessarily optimal. In this way, we use more crude estimation model, and treat the performance estimation error as randomness, so it can provide random perturbations mandated by the GRASP/Super‐heuristics approach directly and save a lot of computation effort at the same time. By the multiple independent running of RAGS, we show that we obtain better solutions than standard greedy search under the comparable computation effort.
ADAPTIVE SLIDING MODE BACKSTEPPING CONTROL OF NONLINEAR SYSTEMS WITH UNMATCHED UNCERTAINTY
Authors: Koshkouei Ali J. , Zinober Alan S. I. , Burnham Keith J.
Published: September 2010

This paper considers an adaptive backstepping algorithm for designing the control for a class of nonlinear continuous uncertain processes with disturbances that can be converted to a parametric semi‐strict feedback form. Sliding mode control using a combined adaptive backstepping sliding mode control (SMC) algorithm, is also studied. The algorithm follows a systematic procedure for the design of adaptive control laws for the output tracking of nonlinear systems with matched and unmatched uncertainty.
A NEW DESIGN APPROACH TO DELAY‐DEPENDENT ROBUST H∞ CONTROL FOR UNCERTAIN TIME‐DELAY SYSTEMS
Authors: Su Ning‐Jun , Su Hong‐Ye , Chu Jian
Published: September 2010

A new design approach to delay‐dependent robust stabilization and robust H∞ control for a class of uncertain time‐delay systems is provided in this paper. The sufficient conditions for delay‐dependent robust stabilization and robust H∞ control are derived based on a new state transformation and given in terms of linear matrix inequalities (LMI). Numerical examples are presented to show that the proposed results can be less conservative and can be used to deal with not only small but also large delay systems.