bluuebunny/crossref_metadata_2025 huggingface dataset:
FORCE/POSITION SLIDING‐MODE CONTROL OF A ROBOT MANIPULATOR IN A NON‐RIGID ENVIRONMENT
Authors: Chen Yon‐Ping , Lu Zhen‐Zhong
Published: September 2010

This paper proposes a sliding‐mode force/position controller for a robot manipulator in contact with an isotropic and homogenous environment with upper‐bounded uncertain stiffness. The sliding‐mode controller design is based on a contact force model linear with the environment deformation. Measurement of the derivative of the contact force is not needed. The sliding vector is chosen on purpose to enable the system to initially be located in the sliding mode so as to avoid undesired transient behavior.
TRACKING CONTROL IN THE PRESENCE OF NONLINEAR DYNAMIC FRICTIONAL EFFECTS: ROBOT EXTENSION
Authors: Feemster M. , Dawson D.M. , Behal A. , Dixon W.
Published: September 2010

In this paper, we extend the observer/control strategies previously published in [25] to an n‐link, serially connected, direct drive, rigid link, revolute robot operating in the presence of nonlinear friction effects modeled by the Lu‐Gre model. In addition, we also present a new adaptive control technique for compensating for the nonlinear parameterizable Stribeck effects. Specifically, an adaptive observer/controller scheme is developed which contains a feedforward approximation of the Stribeck effects. This feedforward approximation is used in a composite controller/observer strategy which forces the average square integral of the position tracking error to an arbitrarily small value. Experimental results are included to illustrate the performance of the proposed controllers.
TRAINING ALGORITHM OF UNIFORM PARZEN WINDOW NEURAL NETWORKS
Authors: Hsiao Chao‐Yin , Hsieh Shan‐Hung
Published: September 2010

In this article, we propose a uniform Parzen window neural network, and introduce the configuration of the neural network, methods for decision boundary determination, and forward training algorithms. For the neural network, we adopt the uniform distributed Parzen window density function to construct the nodes of the hidden layer, and the union function for the output nodes. We also design a pattern generator algorithm to create artificial pattern data, which can be used for simulation, performance evaluation, and neural network optimization.
ROBUST NEURAL NETWORK CONTROL OF RIGID LINK FLEXIBLE‐JOINT ROBOTS
Authors: Kwan C.M. , Lewis F.L. , Kim Y.H.
Published: September 2010

A robust Neural Network (NN) controller is proposed for the motion control of rigid‐link flexible‐joint (RLFJ) robots. No weak joint elasticity assumption is needed. The NNs are used to approximate three very complicated nonlinear functions. Our NN approach requiresno off‐line learning phase, and no lengthy and tedious preliminary analysis to find the regression matrices.Most importantly, we canguarantee the uniformly ultimately bounded (UUB) stability of tracking errors and NN weights.The controller can be regarded as auniversal reusable controllerbecause the same controller can be directly applied to different RLFJ robots with different masses and lengths within the same class, for instance, of two‐link revolute RLFJ robots.
TRACKING DOMAINS FOR UNSTABLE PLANTS WITH SATURATING‐LIKE ACTUATORS
Authors: Yakubovich V.A. , Nakaura S. , Furuta K.
Published: September 2010

In many mechanical systems, the tracking control is one of the most important issues. Therefore for the unstable system subject to actuator saturation, the domain of the initial states corresponding to an arbitrary reference signal, within which tracking condition can be achieved, is interesting to be considered.

In this paper, an approximate tracking domain is derived analytically. First in Theorem 1, for simple case, it is constructed by using the partial Lyapunov function and the absolute stability theory. In the following Theorems 2 and 3, a new idea is used to obtain larger domains. Finally in the last section, a simple numerical example is given.
OPTIMAL CONTROL FOR TWO‐DIMENSIONAL LINEAR SYSTEMS WITH VARIABLE COEFFICIENTS
Authors: Li Jimshone , Tsai Jason Sheng‐Hong , Shieh Leang‐San
Published: September 2010

An optimal control method for two‐dimensional (2‐D) linear systems with variable coefficients and free boundary conditions in Roesser's model is proposed in this paper. Based on Roesser's model, an equivalent general 1‐D model of the 2‐D system is presented, and the problem of minimizing a 2‐D linear quadratic (LQ) cost function is solved for the case where complete state information is available. The solution is obtained by using the proposed dynamic programming in 1‐D descriptor form to solve the Riccati equation and then arriving at the optimal control law and minimum cost. The proposed control methodology can be applied to discrete‐time models of systems described by partial differential equations and can also be used in the field of signal processing.
A RECURSIVE 4SID FROM THE INPUT‐OUTPUT POINT OF VIEW
Authors: Oku Hiroshi , Kimura Hidenori
Published: September 2010

In this paper, recursive algorithms of subspace state‐space system identification (4SID) for multiple‐input, multiple‐output (MIMO), finite dimensional, linear time‐invariant (FDLTI) systems are proposed. These algorithms are derived based on the Matrix Inversion Lemma. The investigation of our algorithms clarifies that a series of 4SID is the extension of the classical least square method to identification for multivariable systems, and also that our algorithms are the direct extension of the recursive least square algorithm to such ones. For PO‐MOESP (the ordinary MOESP scheme with instrumental variables constructed from Past input and Output measurements), we show the mechanism of how the effect of the process and measurement noises is eliminated asymptotically by a projection related to the input and regressor matrices. “MOESP” is an abbreviation for “the MIMO output‐error state‐space model identification.”
VIBRATION ATTENUATION OF AN AXIALLY MOVING STRING VIAACTIVE IN‐DOMAIN CONTROL METHODS
Authors: Lin Jyh‐Feng , Hu Jwusheng
Published: September 2010

A study of active vibration control of axially moving string‐like system is presented in this paper. The approach is based on the concept of wave absorption or impedance matching through placement of both sensors and actuators in domain. A classical model‐matching framework for disturbance rejection is used to solve the control laws. For SISO cases, perfect wave absorption can be achieved if one of the boundary conditions is known. Further, by moving both the sensor and actuator to the boundary, the in‐domain control laws become the boundary controllers derived by previous researchers. To avoid the requirement of knowing the boundary condition precisely, a two‐sensor control law is then proposed. The resulting controller, though boundary independent, contains an infinite number of poles on the imaginary axis, and the closed‐loop system is not internally stable. A simple “notch filter” is added to the control law, and the stability of as well as deterioration in performance are analyzed. Numerical results for a moving string system under external excitation are presented to demonstrate the effectiveness of this controller.
STEERING A MOBILE ROBOT: SELECTION OF A VELOCITY PROFILE SATISFYING DYNAMICAL CONSTRAINTS
Authors: Benayad M.A. , Campion G. , Wertz V. , Achhab M.E.
Published: September 2010

We present an open loop control design allowing to steer a wheeled mobile robot along a prespecified smooth geometric path, minimizing a given cost index and satisfying a set of dynamical constraints. Using the concept of “differential flatness,” the problem is equivalent to the selection of the optimal time parametrization of the geometric path. This parametrization is characterized by a differential equation involving a function of the curvilinear coordinate along the path. For the minimum time problem, as well as for another index (such as the maximum value of the centripetal acceleration) to be minimized over a given time interval, the problem then reduces to the optimal choice of this function of the curvilinear coordinate. Using spline functions interpolation, the problem can be recast as a finite parameter optimization problem. Numerical simulation results illustrate the procedure.
A GLOBAL EXPONENTIAL OUTPUT‐FEEDBACK CONTROLLER FOR INDUCTION MOTORS
Authors: Feemster M. , Dawson D.M. , Aquino P. , Behal A.
Published: September 2010

In this paper, we design an output‐feedback, rotor position/rotor flux controller for the full‐order, nonlinear dynamic model of an induction motor. The singularity free controller does not require measurement of rotor flux or rotor velocity and yields global exponential rotor position and rotor flux tracking. The proposed controller is termed output‐feedback due to the inexpensive/simplistic manner in which stator current measurements can be obtained (e.g., the most primitive method of measuring current can be achieved through simple voltage measurements across known resistive elements). Experimental results are included to verify the effectiveness of the proposed controller.
H∞ ALMOST DISTURBANCE DECOUPLING FOR MIMO BILINEAR SYSTEMS
Authors: Zheng Yufan , Ho Daniel W.C. , Lu Guoping
Published: September 2010

The H∞ almost disturbance decoupling problem is considered. In this paper, a nonlinear design is proposed to find a state feedback controller for bilinear systems. The closed‐loop system is internally stable and achieves disturbance attenuation in nonlinear H∞ sense. We defined a special form of Lyapunov function, which is constructed in terms of one or a set of positive definite constant matrices. If, except of the origin of system, the corresponding polynomial of the positive definite matrix (or several polynomials relevant to the positive definite constant matrices) has (have) no zero on a given subset of state space, then we can construct a controller to solve our problem. It is found that the controller structure could be complicated, but is feasible in computation and may require optimization technique to search the solution. We consider both SIMO and MIMO cases with illustrated examples.
A MODIFIED METHOD FOR SUPERVISOR SPECIFICATION AND SYNTHESIS OF A CLASS OF DISCRETE EVENT SYSTEMS
Authors: Ou Yeong‐Chang , Hu Jwu‐Sheng
Published: September 2010

In this paper, a model‐refining method is proposed to alleviate the complexity involved in specification interpretation of DES control problems. The legal constraint language is defined in terms of illegal states and events in contrast with constructing the automaton of the specification language. This method could provide a more intuitive view of the DES control problem and would be suitable for practical implementation. Two examples, which have commonly been used in the literature, are employed to show the efficiency of the proposed method. Further, under this framework, it is shown that the supremal controllable sublanguage can take a simpler form based on the concept of an illegal state set. A state‐based supervisor synthesis procedure is presented, and a simple example is provided.
ECOLOGICAL SYSTEMS CONTROL BY FUZZY LOGIC CONTROLLER
Authors: Lin Chih‐Min , Mon Yi‐Jen , Maa Jiann‐Hwa
Published: September 2010

The application of fuzzy logic control in the long‐term management of an ecological system is proposed. The ecological systems are subject to unpredictable but bounded disturbances due to changes in climatic conditions, disease, migrating species, etc. By applying the fuzzy logic controller, the biomasses of exploited and continually disturbed ecological systems will converge in a comparatively short time and keep the biomasses within a small neighborhood of the unique nontrivial optimal equilibrium state of the undisturbed exploited ecosystem. Demonstration through examples reveals that the accumulated yield with fuzzy logic control is better than that with state feedback control or no control.
A NOTE ON THE SOLUTION OF THE STOCHASTIC ALGEBRAIC RICCATI EQUATION
Authors: Zhang Wei‐Hai , Jia Lei
Published: September 2010

This paper deals with the solution of the stochastic algebraic Riccati equation, which has been discussed by many authors. A sufficient and necessary condition for the existence of the positive solution is derived, which improves the previous results.
DISCRETE OUTPUT FUZZY CONTROLLER DESIGN FOR ACHIEVING COMMON CONTROLLABILITY GRAMIAN
Authors: Chang Wen‐Jer , Sun Chein‐Chung , Fuh Chyun‐Chau
Published: September 2010

In control theory, the robust properties of linear systems can be related directly to properties of the controllability or observability Gramian. In this paper, a discrete fuzzy controller for a class of nonlinear systems is developed to achieve a common controllability Gramian. We assume that the nonlinear system is represented by the Takagi‐Sugeno fuzzy model. The purpose of this paper is to find the output feedback gains for the T‐S fuzzy controller after assigning a certain common controllability Gramian. Finally, we provide a numerical example to verify the effects of the proposed method.
CORRECTION TO “SINGULARITY COMPUTATION FOR ITERATIVE CONTROL OF NONLINEAR AFFINE SYSTEMS”
Authors: Popa Dan O. , Wen John T.
Published: September 2010

The ball‐on‐plate example in the above paper by the authors [3] contains a mistake in the vector field. This note presents the singularity result of the corrected vector field.
SLIDING VECTOR DESIGN BASED ON THE POLE‐ASSIGNMENT METHOD
Authors: Chang Jeang‐Lin , Chen Yon‐Ping
Published: September 2010

This paper mainly focuses on the sliding vector design based on the poleassignment method. Unlike the general sliding vector design, the poleassignment method is applied to the overall system, not to a reduced system in sliding mode. Funrthermore, because four important conditions are considered, this sliding vector design is much simpler and more straightforward. Finally, a numeric example is included to demonstrate the validity of the proposed technique.
ADAPTIVE ROBUST CONTROL OF UNCERTAIN SYSTEMS WITH TIME‐VARYING STATE DELAY
Authors: Xu Jian‐Xin , Jia Qing‐Wei , Lee Tong‐Heng
Published: September 2010

In this paper, a new adaptive robust control scheme is developed for a class of uncertain dynamical systems with time‐varying state delay, unknown parameters and disturbances. By incorporating adaptive techniques into the robust control method, we propose a continuous adaptive robust controller which guarantees the uniform boundedness of the system and at the same time, the regulating error enters an arbitrarily designated zone in a finite time. The proposed controller is independent of the time‐delay, hence it is applicable to a class of dynamical systems with uncertain time delays. The paper includes simulation studies demonstrating the performance of the proposed control scheme.
LINEAR OPTIMAL FEEDBACK AND LOAD‐DECOUPLED CONTROL OF A BUCK DC‐DC CONVERTER
Authors: Hsieh Fei‐Hu , Yen Nie‐Zen , Juang Yau‐Tarng
Published: September 2010

This paper describes a new controller design procedure and tuning method for a PWM buck dc‐dc converter. First, linear optimal feedback is designed using the LQR approach. Then the designed control law is implemented using a PID controller incorporated with a load‐decoupled PD compensator. The PID controller is tuned to achieve the optimal design based on the output error voltage directly, instead of using an estimator. When the proposed PD compensator is used, the converter is robust with respect to the input voltage and output current changes and the parameter perturbations. We also provide the conditions for the robust stability assurance of the closed‐loop system.
NEW TUNING METHOD FOR PID CONTROL OF A PLANT WITH UNDER‐DAMPED RESPONSE
Authors: Shen Jing‐Chung
Published: September 2010

In this paper, a tuning formula is derived for PID control of plants with under‐damped step responses. The under‐damped systems are modeled by second‐order plus dead time transfer functions. To derive the tuning rule, the dominant pole assignment method was applied to design the PID controllers for a variety of plant models. Then, the correlation between the controller parameters and the parameters of the models was found and the tuning formula derived. Several simulation examples are given to demonstrate the effectiveness and robustness of this formula.
EXTENSION OF KALMAN FILTER THEORY TO NONLINEAR SYSTEMS WITH APPLICATION TO WING ROCK MOTION
Authors: Shue Shyh‐Pyng , Agarwal Ramesh K.
Published: September 2010

A formulation for nonlinear Kalman filter theory is developed in this paper. It is shown that the solution of the nonlinear Kalman filter problem is governed by a Hamilton‐Jacobi‐Bellman inequality (HJBI). Choosing the closed loop Lyapunov function in a symmetric matrix form of the state vector results in the reduction of the HJBI to an algebraic Riccati inequality along with several other algebraic inequalities. These inequalities are formulated into a series of closed loop Lyapunov inequalities which are turned into equalities by adding a positive state vector function. Closed loop Lyapunov functions are then obtained successively by solving these eqauations based on the powers of the state vector. Control of a nonlinear wing rock motion is employed as an example to illustrate the theory.
ON STABILITY AND PERFORMANCE OF INDUCTION MOTOR SPEED DRIVES WITH SLIDING MODE CURRENT CONTROL
Authors: Lin Jong‐Lick , Shiau Lin‐Goei
Published: September 2010

A novel inner loop sliding mode current control scheme for induction motor speed drives is proposed in this paper. The controller design is based on the nonlinear mathematical model of an induction motor in a coordinate system oriented along the rotor flux. The parameters of the PI speed controller are taken into consideration in the inner loop sliding mode current control. Rigorous stability verification for the overall system is provided in this paper. The chattering in sliding mode control is attenuated using the reaching law design method. The experimental results show that the proposed approach exhibits robust tracking performance in the presence of motor parameter variations and load disturbances.
STABILIZATION OF A CLASS OF NONLINEAR NON‐MINIMUM PHASE SYSTEMS
Authors: Cheng Daizhan
Published: September 2010

This paper tackles the problem of stabilization of a class of non‐minimum phase nonlinear systems which have zero dynamics with an eigenvalue zero of multiplicity 2. By adding some new terms, called cross terms, we are able to generalize the concept of the Lyapunov function with a homogeneous derivative along the trajectory, which was introduced in [4], to produce a suitable Lyapunov function. The Lyapunov function assures that the stability of an approximate system, which consists of some lower order terms of a nonlinear system with an eigenvalue zero of multiplicity 2, implies the stability of the whole system. Applying these to non‐minimum phase zero dynamics of nonlinear systems with such a center, a sufficient condition and a design method of state feedback control are obtained for stabilizing the systems.
DYNAMIC ANALYSES AND ROBUST STEERING CONTROLLER DESIGN FOR AUTOMATED LANE GUIDANCE OF HEAVY‐DUTY VEHICLES
Authors: Wang Jeng‐Yu , Tomizuka Masayoshi
Published: September 2010

In this paper, we present various linear analyses of the linearized lateral dynamics of heavy‐duty vehicles (HDVs) (tractor‐semitrailer type), which include time domain, frequency domain and pole/zero analyses. These analyses are conducted to examine the vehicle response to the steering input subjected to variations of speed, road adhesion coefficient, cargo load in the trailer, and look‐ahead distance for the lateral deviation sensor. These parameters (uncertainties) have significant influence on vehicle dynamics. It has been shown that redefining the look‐ahead lateral error as the controlled output has a favorable impact on the lateral control problem. Based on these analyses, a robust steering controller using H∞ loop‐shaping procedure is designed for a tractor semitrailer combination to follow the road center line on both curved and straight highway sections. The proposed controller ensures the robust performance under model uncertainties which include varying vehicle longitudinal speed, road adhesion coefficient, and cargo load in the trailer. The performance of the designed controller is evaluated by simulations and validated by experiments.
PIECEWISE LYAPUNOV FUNCTIONS FOR SWITCHED SYSTEMS WITH AVERAGE DWELL TIME
Authors: Zhai Guisheng , Hu Bo , Yasuda Kazunori , Michel Anthony N.
Published: September 2010

The stability properties of linear switched systems consisting of both Hurwitz stable and unstable subsystems are investigated by using piecewise Lyapunov functions incorporated with an average dwell time approach. It is shown that if the average dwell time is chosen sufficiently large and the total activation time ratio between Hurwitz stable and unstable subsystems is not smaller than a specified constant, then exponential stability of a desired degree is guaranteed. The above result is also extended to the case where nonlinear norm‐bounded perturbations exist.
BOUNDED ROBUST CONTROLLER DESIGN FOR MISMATCHED STATE‐DEPENDENT UNCERTAINTY
Authors: Chang Jeang‐Lin , Chen Yon‐Ping
Published: September 2010

A bounded robust control is presented for dealing with matched external disturbance and mismatched state‐dependent uncertainty in linear time‐invariart systems. By using the famous pole‐assignment method, an important switching vector is selected first before the controller is designed. Based on the Lyapunov theory, system stability is proven, and the upper bound of the control input is discussed. It is found that the system initial state has to be located in a suitable region determined by the upper bounds on the control input, the matched disturbance and the mismatched uncertainty. A numerical example is used to demonstrate use of the bounded robust controller in simulation.
A NEW DESIGN APPROACH FOR FUZZY‐LEARNING FUZZY CONTROLLERS
Authors: Tao C.W. , Taur J.S.
Published: September 2010

In this paper, a new approach to designing fuzzy‐learning fuzzy controllers for a system plant without an exact mathematical model is presented. The cost function is defined as the square of the sliding function to alleviate the difficulty of overshoot when on‐line learning is conducted. The learning mechanism of a fuzzy controller is constructed so as to minimize the cost function with a set of linguistic rules. Moreover, to reduce the complexity of the fuzzy‐learning fuzzy controller, the fuzzy mechanism used for learning and the fuzzy mechanism contained in the fuzzy controller are designed so as to have the identical structures. Finally, simulations are included to show the effectiveness of the fuzzy‐learning fuzzy controllers.
Recursive Approaches for Single Sample Path Based Markov Reward Processes
Authors: Fang Hai‐Tao , Chen Han‐Fu , Cao Xi‐Ren
Published: September 2010

In this paper, two single sample path‐based recursive approaches for Markov decision problems are proposed. One is based on the simultaneous perturbation approach and can be applied to the general state problem, but its convergence rate is low. In this algorithm, the small perturbation on current parameters is necessary to get another sample path for comparison, but it may worsen the system. Hence, we introduce another approach, which directly estimates the gradient of the performance for optimization by “potential” theory. This algorithm, however, is limited to finite state space systems, but its convergence speed is higher than the first one. The estimate for gradient can be obtained by using the sample path with current parameters without any perturbation. This approach is more acceptable for practical applications.
Dynamic Parameterization for Path Following Control
Authors: Kumon Makoto , Adachi Norihiko
Published: September 2010

Path following is a basic skill for robots in industrial use. Since the objective of path following is to make a robot follow the reference path, the velocity of the robot or the timing of the motion does not need to be strictly controlled. In this paper, the reference path is defined as a function of the parameter that has dynamics. Using this parameter, we can define the contour error, which is the error that enables us to measure the distance from the reference path to the configuration of the robot. Although the contour error is suitable for use in path following, the dynamics of the parameter contain singular points at which the dynamics cannot be defined. In order to overcome this difficulty, globally defined dynamics are introduced, and two dynamics are integrated with a switching scheme. In this paper, the asymptotic stability of the whole system is proved theoretically. Computer simulation results also show the effectiveness of the method.
Fuzzy‐Neuron Intelligent Coordination Control Fora Unit Power Plant
Authors: Zhang Jianming , Wang Ning , Wang Shuqing
Published: September 2010

A novel fuzzy‐neuron intelligent coordination control method for a unit power plant is proposed in this paper. Based on the complementarity between a fuzzy controller and a neuron model‐free controller, a fuzzy‐neuron compound control method for Single‐In‐Single‐Out (SISO) systems is presented to enhance the robustness and precision of the control system. In this new intelligent control system, the fuzzy logic controller is used to speed up the transient response, and the adaptive neuron controller is used to eliminate the steady state error of the system. For the multivariable control system, the multivariable controlled plant is decoupled statically, and then the fuzzy‐neuron intelligent controller is used in each input‐output path of the decoupled plant. To the complex unit power plant, the structure of this new intelligent coordination controller is very simple and the simulation test results show that good performances such as strong robustness and adaptability, etc. are obtained. One of the outstanding advantages is that the proposed method can separate the controller design procedure and control signals from the plant model. It can be used in practice very conveniently.
Application of Fuzzy Logic to Vehicle Classification Algorithm in Loop/Piezo‐Sensor Fusion Systems
Authors: Kim Sung‐Wook , Kim Kwangsoo , Lee Joo‐hyung , Cho Dong‐il (Dan)
Published: September 2010

Individual vehicle information, especially, vehicle classification data play a key role in Advanced Traffic Management and Information Systems (ATMIS). In inductive loop and piezo‐sensor fusion systems, traffic data such as the vehicle length and the distance between axles are used for vehicle classification. However, classification errors often occur in distinguishing passenger cars from small trucks and in distinguishing medium‐sized trucks from small trucks. It is mainly attributed to the fact that they are similar in lengths and have similar inter‐axle distances. To improve the performance in vehicle classification, we develop a new algorithm using a fuzzy logic. Vehicle weight and speed are used as the inputs to the fuzzy logic block. The output of the fuzzy logic block is a weighting factor to modify the calculated vehicle length. Experimental results show that the developed algorithm significantly improves the classification performance.
On‐Line Fault Diagnosis Based on B‐Spline Neural Networks Using Asymptotic Local Approach
Authors: Wang Y. , Chan C.W. , Cheung K.C.
Published: September 2010

Fault diagnosis is crucial in monitoring industrial processes. Faults con be often detected from residuals generated from the system model. For systems with known models, residuals can be readily generated. However, for systems with unknown models, neural networks can be used to model the system. For small or incipient faults, it is difficult to detect faults directly from the residuals. The asymptotic local approach, which transforms the fault diagnosis problem into one that detects statistical changes in a random variable, is proposed here. The proposed scheme is illustrated by a simulation example, and comparison with faults obtained directly from the residuals is also made.
Nonlinear Control VIA Generalized Feedback Linearization Using Neural Networks
Authors: Goodwin Graham C. , Rojas Osvaldo , Takata Hitoshi
Published: September 2010

A novel approach to nonlinear control, called Generalized Feedback Linearization (GFL), is presented. This new strategy overcomes one important drawback of the well known Feedback Linearization strategy, in the sense that it is able to handle a broader class of nonlinear systems, namely those having unstable zero dynamics. It is shown that the use of a nonlinear predictor for the system output is a key feature in the derivation of the control strategy. For certain types of systems this predictor can be found as a nonlinear function of the system input and output, allowing an output feedback control solution. The use of Artificial Neural Networks (ANN) to directly parameterize the predictor of the controlled variable when an explicit model for the system is not available, is investigated via computer simulations. This approach is based on the functional approximation capability of multi layer ANN.
Fault Diagnosis Based on the Fuzzy‐Recurrent Neural Network
Authors: Xiang Zhao , Deyun Xiao
Published: September 2010

A fuzzy‐recurrent neural network (FRNN) has been constructed by adding some feedback connections to a feedforward fuzzy neural network (FNN). The FRNN expands the modeling ability of a FNN in order to deal with temporal problems. A basic concept of the FRNN is first to use process or expert knowledge, including appropriate fuzzy logic rules and membership functions, to construct an initial structure and to then use parameter‐learning algorithms to fine‐tune the membership functions and other parameters. Its recurrent property makes it suitable for dealing with temporal problems, such as on‐line fault diagnosis. In addition, it also provides human‐understandable meaning to the normal feedforward multilayer neural network, in which the internal units are always opaque to users. In a word, the trained FRNN has good interpreting ability and one‐step‐ahead predicting ability. To demonstrate the performance of the FRNN in diagnosis, a comparison is made with a conventional feedforward network. The efficiency of the FRNN is verified by the results.
Neural Network Adaptive Robust Control Of Siso Nonlinear Systems In A Normal Form
Authors: Gong J.Q. , Yao Bin
Published: September 2010

In this paper, performance oriented control laws are synthesized for a class of single‐input‐single‐output (SISO)n‐th order nonlinear systems in a normal form by integrating the neural networks (NNs) techniques and the adaptive robust control (ARC) design philosophy. All unknown but repeat‐able nonlinear functions in the system are approximated by the outputs of NNs to achieve a better model compensation for an improved performance. While all NN weights are tuned on‐line, discontinuous projections with fictitious bounds are used in the tuning law to achieve a controlled learning. Robust control terms are then constructed to attenuate model uncertainties for a guaranteed output tracking transient performance and a guaranteed final tracking accuracy. Furthermore, if the unknown nonlinear functions are in the functional ranges of the NNs and the ideal NN weights fall within the fictitious bounds, asymptotic output tracking is achieved to retain the perfect learning capability of NNs. The precision motion control of a linear motor drive system is used as a case study to illustrate the proposed NNARC strategy.
Neural Network Based Algorithm for Dynamic System Optimization
Authors: Romero Roseli Francelin , Kacprzyk Janusz , Gomide Fernando
Published: September 2010

A class of artificial neural networks with a two‐layer feedback topology to solve nonlinear discrete dynamic optimization problems is developed. Generalized recurrent neuron models are introduced. A direct method to assign the weights of neural networks is presented. The method is based on Bellmann's Optimality Principle and on the interchange of information which occurs during the synaptic chemical processing among neurons. A comparative analysis of the computational requirements is made. The analysis shows advantages of this approach as compared to the standard dynamic programming algorithm. The technique has been applied to several important optimization problems, such as shortest path and control optimal problems.
Robust Adaptive Identification of Nonlinear System Using Neural Network
Authors: Song Q. , Hu W.J. , Soh Y.C.
Published: September 2010

It is well known that disturbance can cause divergence of neural networks in the identification of nonlinear systems. Sufficient conditions using so‐called modified algorithms are available to provide guaranteed convergence for adaptive system. They are dead zone scheme, adaptive law modification, and σ‐modification. These schemes normally require knowledge of the upper bound of the disturbance. In this paper, a robust weighttuning algorithm is used to train the multi‐layered neural network with an adaptive dead zone scheme. The proposed robust adaptive algorithm does not require knowledge of either the upper bound of the disturbance or the bound on the norm of the estimate parameter. A complete convergence proof is provided based on Lyapunov theorem to deal with the nonlinear system. Simulation results are presented to show good perfor‐mance of the algorithm.
Stability of Linear Systems With Time‐Varying Delay: a Generalized Discretized Lyapunov Functional Approach
Authors: Han Qing‐Long , Gu Keqin
Published: September 2010

This paper investigates the stability of linear uncertain systems with time‐varying delay. Stability criteria are derived based on a generalized discretized Lyapunov functional approach. The kernel of the functional, which is a function of two variables, is chosen as piecewise linear. The stability conditions are written in the form of linear matrix inequalities. Numerical examples indicate significant improvements over the existing results.
Structured Operation Environment And Perceived Complexity In Human Supervisory Control
Authors: Li K. , Wieringa P.A.
Published: September 2010

Understanding the impact of system complexity on operator performance is important for task allocation and human‐machine system design in human supervisory control. In this paper a conceptual framework is proposed to relate the perceived complexity with various objective complexities. An experimental setup is designed to verify the conceptual framework and quantify the relations between perceived complexity and objective complexities. The experimental setup consists of four operation environments. A subjective rating scale is used to measure the perceived complexity in each of the four operation environments when the operator performs assigned tasks. Experimental results show a significantly different perceived complexity between the fully coupled and uncoupled operation environments. Furthermore it is revealed that the operator would perceive the system as extremely complex if (s)he would have to operate over 10 subsystems extensively in 30 minutes, i.e. 20 subsystems per hour.
On Maximum Stability Margin Design of Nonlinear Uncertain Systems: Fuzzy Control Approach
Authors: Tseng Chung‐Shi , Chen Bor‐Sen , Uang Huey‐Jian
Published: September 2010

This paper studies the maximum stability margin design for nonlinear uncertain systems using fuzzy control. First, the Takagi and Sugeno fuzzy model is employed to approximate a nonlinear uncertain system. Next, based on the fuzzy model, the maximum stability margin for a nonlinear uncertain system is studied to achieve as much tolerance of plant uncertainties as possible using a fuzzy control method. In the proposed fuzzy control method, the maximum stability margin design problem is parameterized in terms of a corresponding generalized eigenvalue problem (GEVP). For the case where state variables are unavailable, a fuzzy observer‐based control scheme is also proposed to deal with the maximum stability margin for nonlinear uncertain systems. Using a suboptimal approach, we characterize the maximum stability margin via fuzzy observer‐based control in terms of a linear matrix inequality problem (LMIP). The GEVP and LMIP can be solved very efficiently via convex optimization techniques. Simulation examples are given to illustrate the design procedure of the proposed method.
A New Approach To Optimal and Self‐Tuning Filtering, Smoothing and Prediction For Discrete‐Time Systems
Authors: Zhang Huanshui , Xie Lihua , Soh Yeng Chai
Published: September 2010

A new approach to optimal and self‐tuning state estimation of linear discrete time‐invariant systems is presented, using projection theory and innovation analysis method in time domain. The optimal estimators are calculated by means of spectral factorization. The filter, predictor, and smoother are given in a unified form. Comparisons are made to the previously known techniques such as the Kalman filtering and the polynomial method initiated by Kucera. When the noise covariance matrices are not available, self‐tuning estimators are obtained through the identification of an ARMA innovation model. The self‐tuning estimator asymptotically converges to the optimal estimator.
Performance Analysis of Periodic Control for l1 and l∞ Disturbance Rejection
Authors: Schmid Robert , Zhang Cishen
Published: September 2010

This paper presents a performance analysis of discrete time periodically time varying controllers for the rejection of lp specific and uniform disturbances. Earlier results on l2 performance are extended to l1 and l∞ performance to present a unified treatment of lp performance for all p ε [1, ∞]. For a given linear time varying periodic controller, a linear time invariant controller is constructed and necessary and sufficient conditions are presented under which the linear time invariant controller gives strictly better lp disturbance rejection performance than the time varying periodic controller.
Application of Fuzzy Modified Potential Gradients for Vehicle Maneuvering Problems
Authors: Nagarajan N.
Published: September 2010

This paper presents a strategy of combining the fuzzy membership function and the potential function for controlling the course of a vehicle in the presence of obstacles. The strategy provides additional freedom in selecting the potential function and the steering parameters. In case of multiple vehicles (with different steering capabilities) moving on a given surface, the proposed methodology helps to choose appropriate fuzzy functions for each vehicle according to its steering capabilities. Applications for this strategy are demonstrated for two different cases ‐ (1) for maneuvering a vehicle moving on plane surface, and (2) for planning in‐plane orbit maneuvers to achieve orbit acquisition with required phase angle for constellation establishment.
Discrete‐Time Risk‐Sensitive Filters with Non‐Gaussian Initial Conditions and Their Ergodic Properties
Authors: Dey Subhrakanti , Charalambous Charalambos D.
Published: September 2010

In this paper, we study asymptotic stability properties of risk‐sensitive filters with respect to their initial conditions. In particular, we consider a linear time‐invariant systems with initial conditions that are not necessarily Gaussian. We show that in the case of Gaussian initial conditions, the optimal risk‐sensitive filter asymptotically converges to a suboptimal filter initialized with an incorrect covariance matrix for the initial state vector in the mean square sense provided the incorrect initializing value for the covariance matrix results in a risk‐sensitive filter that is asymptotically stable, that is, results in a solution for a Riccati equation that is asymptotically stabilizing. For non‐Gaussian initial conditions, we derive the expression for the risk‐sensitive filter in terms of a finite number of parameters. Under a boundedness assumption satisfied by the fourth order absolute moment of the initial state variable and a slow growth condition satisfied by a certain Radon‐Nikodym derivative, we show that a suboptimal risk‐sensitive filter initialized with Gaussian initial conditions asymptotically approaches the optimal risk‐sensitive filter for non‐Gaussian initial conditions in the mean square sense. Some examples are also given to substantiate our claims.
Stochastic Stability and H∞ Disturbance Attenuation of A Class of Nonlinear Stochastic Delayed Systems
Authors: Cao Yong‐Yan , Hu Lisheng
Published: September 2010

In this paper, we consider stochastic stability analysis and H∞ disturbance attenuation for a class of discrete‐time nonlinear stochastic retarded systems whose nonlinearities are described by statistical means. First a sufficient condition on mean square stability, which is independent of delay, is proposed using the stochastic Lyapunov functional approach. Then, H∞ disturbance attenuation property of the system is analyzed. It is shown that the H∞ disturbance attenuation of such nonlinear systems can be reduced to a linear matrix inequality problem.
Neural Networks For Matrix Scaling And S‐Procedure Problem Solving
Authors: Lin Chun‐Liang
Published: September 2010

Linear matrix inequalities (LMIs) play a very important role in postmodern control by providing a framework that unifies many concepts. While many papers have addressed the issue for solving LMIs using sequentially numerical algorithms, few have examined solving related LMIs using neural network processing. The aim of this paper is to show the potential of using recurrent neural networks to solve these problems. Two representative LMI problems are considered. First, the problem of scaling a matrix to reduce its norm, which appears often in robust control applications, is considered. Second, the approach is extended to solve the S‐procedure problem, which is closely related to the stability of particular nonlinear systems. Illustrative examples are provided to demonstrate use of the proposed approach.
An Adaptive Learning Control and Its Application to Vibration Isolation Systems
Authors: Iwase Masami , Yamakita Masaki , Maruta Yuji , Yamazoe Hidetoshi
Published: September 2010

We present adaptive learning control algorithms which use Markov parameters. Since in the algorithm, the control input and the model parameter are updated at each iteration, it is applicable to unknown systems in a class. In order to overcome the effect of a disturbance, Markov parameters of the inverse model is also introduced. The effectiveness of the algorithm is illustrated by means of a numerical simulation and experimental application to an active vibration isolation system.
Iterative Learning Control Via Weighted Local‐Symmetrical‐Integration
Authors: Chen Yangquan , Dou Huifang , Tan Kok Kiong
Published: September 2010

A new iterative learning control (ILC) updating law is proposed for tracking control of continuous linear system over a finite time interval. The ILC is applied as a feedforward controller to the existing feedback controller. By using the weighted local symmetrical integral (WLSI) of feedback control signal of previous iteration, the ILC updating law takes a simple form with only two design parameters: the learning gain and the range of local integration. Convergence analysis is presented together with a design procedure. A set of experimental results are presented to illustrate the effectiveness of the proposed WLSI‐ILC scheme.
Revisiting The Ziegler‐Nichols Tuning Rules For Pi Control
Authors: Hägglund T. , Åström K. J.
Published: September 2010

This paper presents new tuning rules for PI control of processes with essentially monotone step response that are typically encountered in process control. The rules are based on characterization of process dynamics by three parameters that can be obtained from a step response experiment. The rules are obtained by maximizing integral gain subject to a constraint on the maximum sensitivity. They are almost as simple as the Ziegler Nichols tuning rules but they give substantially better performance.
A Synthesis Method For Robust Pid Controllers For A Class Of Uncertain Systems
Authors: Solyom Stefan , Ingimundarson Ari
Published: September 2010

PID controller design is considered where optimal controller parameters are found with constraint on maximum sensitivity and robustness with regard to a cone bounded static nonlinearity acting in feedback with part of the plant. The design procedure has been successfully applied in the synthesis of a controller for an Anti‐lock Braking System (ABS).
Pi Tuning Under Performance Constraints
Authors: Wang Qing‐Guo , Ru He
Published: September 2010

In this paper, a PI controller tuning method is proposed to meet several practical constraints such as the system bandwidth, the stability robustness and the low‐frequency disturbance rejection ability. An objective sensitivity function is first given subject to the above constraints. The PI controller is then designed by making the actual closed‐loop sensitivity function close to the optimal one in the frequency domain. Simulation examples are provided to demonstrate the effectiveness of the proposed method.
Loop‐Shaping Design Of Pid Controllers With Constant Ti/Td RATIO
Authors: Wallén A. , ÅRström K. J. , Hägglun T.
Published: September 2010

This paper presents new tuning rules for PID controllers based on loop shaping. Previous research has shown that maximization of the integral gain subject to constraints on the maximum sensitivity is an efficient design method for PI controllers, but that additional constraints are needed for design of PID controllers. In this paper, an additional constraint is obtained by restricting the ratio between integral time and derivative time. It is shown that this gives a numerically efficient method that gives good results for a large class of processes.
Tuning Rules For The Pid Controller Using A Dmc Strategy
Authors: Haeri Mohammad
Published: September 2010

PID controllers have found wide application in industries because of their simple structures and relatively good robustness against process uncertainties. There are several different methods to determine parameters of these controllers. These methods rely on formulas, which are obtained based on performance consideration in either the time domain or the frequency domain. In this paper, a new method is developed in which the parameters of the controller are adjusted in such a way that similar performance as for Dynamic Matrix Control (DMC) is obtained. The behaviour of the controller is compared with those obtained in previous approaches both from the time and the frequency domains. Results obtained from several computer simulations are presented.
Design Of Robust Pid Controllers
Authors: Wu Tai‐Zu , Juang Yau‐Tarng , Wan Jinn‐Der
Published: September 2010

In this paper, a new method is proposed to design the robust PID controllers for uncertain linear time‐invariant systems. We describe a simple and programming procedure for designing PID controllers by applying Kharitonov's theorem and LMI method. Such a controller enables the resulting control system to achieve robust stability as well as asymptotic tracking.
Pid And Pid‐Like Controller Design By Pole Assignment Within D‐Stable Regions
Authors: Wang Yongji , Schinkel M. , Schmitt‐Hartmann Tilmann , Hunt Ken J.
Published: September 2010

This paper presents a new PID and PID‐like controller design method that permits the designer to control the desired dynamic performance of a closed‐loop system by first specifying a set of desired D‐stable regions in the complex plane and then running a numerical optimisation algorithm to find the controller parameters such that all the roots of the closed‐loop system are within the specified regions. This method can be used for stable and unstable plants with high order degree, for plants with time delay, for controller with more than three design parameters, and for various controller configurations. It also allows a unified treatment of the controller design for both continuous and discrete systems. Examples and comparative simulation results are provided to illustrate its merit.