bluuebunny/crossref_metadata_2025_split huggingface dataset:
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

ABSTRACTThis 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

ABSTRACTA 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

ABSTRACTThis 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

ABSTRACTThis 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

ABSTRACTIn 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

ABSTRACTIn 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

ABSTRACTLinear 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

ABSTRACTWe 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

ABSTRACTA 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

ABSTRACTThis 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

ABSTRACTPID 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

ABSTRACTIn 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

ABSTRACTThis 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

ABSTRACTPID 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

ABSTRACTIn 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

ABSTRACTThis 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.
A New Adaptive Method For Identification Of Critical Gain Using Saturation Function
Authors: Saeki Masami 
Published: September 2010

ABSTRACTA new adaptive method for identification of the critical gain is proposed. Instead of using relay feedback, a saturation nonlinearity and an adjusting gain are inserted in the loop, and the gain is tuned by the difference signal between the input and the output of the saturation function. The gain decreases monotonically and converges to a constant, which can be very close to the critical gain. It is shown that the feedback system does not become unstable by this method. This method can be easily extended to the multi‐input multi‐output plants.
Modeling And Robust Pi Control Of A Fluidized Bed Combustor For Sewage Sludge
Authors: Jia Yingmin , Lunze Jan , Wolff Andreas 
Published: September 2010

ABSTRACTBased on experimental data, a fluidized bed combustor is modeled as an interval system. Severe model uncertainty and large time delays lead to the main difficulties in solving the control problem. The design uses the stability test of closed‐loop systems as the main guideline for developing a robust PI controller. In particular, a new formula to compute the maximal magnitude of an edge rational function at a fixed frequency is derived, which provides a way to deal with time delay by transforming it into multiplicative uncertainty. Both the theoretical and the experimental results show that the designed PI controller can satisfy the desired performance requirements.
P/Pd Types Of Override Control Systems
Authors: Tsai Yaw‐Ying , Cheng Yu‐Chang , Yu Cheng‐Ching 
Published: September 2010

ABSTRACTThe override control provides protective action against abnormal operation and/or incipient process failure. In terms of control theory, it is a logic‐based switching system and, in terms of operation, it is usually activated between the soft constraint and the hard constraint. It is used in all process industries, and most PID control loops are patched with some type of override system. Despite widespread application, little research has been done to analyze override control systems. In this work, the properties of the override control are explored. The stability of the override control is also studied. Counter to intuition, many well‐known override systems may exhibit instability and lead to oscillatory responses as the process approaches constraints. Approaches are proposed for the design of override control systems to ensure inherent safe operation.
Convergence and Robustness of Iterative Learning Control For Strongly Positive Systems
Authors: Andres Daniel , Pandit Madhukar 
Published: September 2010

ABSTRACTIn this paper, we consider the convergence and robustness of a general iterative learning control scheme for a class of systems which we term “strongly positive”. The analysis is made in the framework of Hilbert‐space theory. Thus the results are valid for discrete‐time as well as continuous‐time systems which may be time‐variant or time‐invariant. For the special case of continuous linear time‐invariant systems which are defined over the Hilbert‐space of square integrable functions, we will give a characterization of strongly positive systems in the frequency domain.
Iterative Learning Control With Advanced Output Data
Authors: Jeong Gu‐Min , Choi Chong‐Ho 
Published: September 2010

ABSTRACTIn this paper, iterative learning control using output data, which are more advanced than the relative degree of the system, is being investigated. It is known that the output error can be made zero with the conventional iterative learning control in which the input is updated with the output data advanced by the relative degree. However, the input can become too large for nonminimum phase systems. With the proposed scheme, the input can be prevented from becoming unnecessarily too large, which in turn makes it practical to apply iterative learning control for nonminimum phase systems. Simulations are performed to show the effectiveness of the scheme.
Higher Relative Degree Nonlinear Systems With ilc Using Lower‐order Differentiations
Authors: Sun Mingxuan , Wang Danwei 
Published: September 2010

ABSTRACTThis paper addresses the problem of iterative learning control (ILC) for a class of nonlinear continuous‐time systems with higher relative degree. The proposed ILC solution is a family of updating laws using differentiations of tracking error with the order less than the system relative degree. A unified convergence condition for this family of ILC updating laws is provided and proved to be independent of the highest order of differentiation. The application to path tracking of a robotic manipulator is presented to illustrate the effectiveness of the proposed method.
Analysis Of Linear Iterative Learning Control Schemes Using Repetitive Process Theory
Authors: Owens D.H. , Rogers E. , Moore K. L. 
Published: September 2010

ABSTRACTThe purposes of this paper are (i) to critically review existing results on the use of the systems theory for repetitive processes in the analysis of a wide class of linear iterative control laws, and (ii) to present some new results on controller design using this general approach. This paper first presents results on the stability and convergence properties of a general class of iterative learning control schemes using, in the main, theory first developed for the subclass of so‐called differential and discrete linear repetitive processes. A general learning law that uses information from the current and a finite number of previous trials is considered and the results are interpreted in terms of basic systems theoretic concepts such as the relative degree and minimum phase characteristics. It is also shown that a number of other approaches reported in the literature are, in fact, special cases of the results obtained in the repetitive process setting. In the second part of the paper, new results on controller design are given based on 2D transfer function matrices together with new results on the robustness of norm optimal iterative learning control schemes.
A Practical Iterative Learning Path‐Following Control Of An Omni‐Directional Vehicle
Authors: Chen Yang Quan , Moore Kevin L. 
Published: September 2010

ABSTRACTA new practical iterative learning control (ILC) updating law is proposed to improve the path following accuracy for an omni‐directional autonomous mobile robot. The ILC scheme is applied as a feedforward controller to the existing feedback controller. By using the local symmetrical double‐integral of the feedback control signal of the 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. Simulation results on a difficult maneuver are presented to illustrate the effectiveness of the proposed simple and yet practical scheme. The simulation is based on the model of a novel robotic platform, the Utah State University (USU) Omni‐Directional Vehicle (ODV), which uses multiple “smart wheels,” whose speed and direction can be independently controlled through dedicated processors for each wheel.
Neuro‐Fuzzy Impact Control (NFIC) for Anti‐Personnel (AP) Mines Detection
Authors: Shahri Ali M. , Naghdy Fazel 
Published: September 2010

ABSTRACTAn explicit impact control scheme is modified as the main control scheme, while an intelligent control method is designed to deal with uncertainties and varying environment parameters in a mechatronics approach to anti‐personnel (AP) mine detection. The device imitates the manual hand‐prodding technique for mine detection. It inserts a bayonet into the soil and models the dynamics of the manipulator and environment parameters, such as stiffness variation in the soil, to control the impact caused by making contact with a stiff object.An adaptive neuro‐fuzzy plus PID controller is employed to switch from a conventional PID controller to neuro‐fuzzy impact control (NFIC) when an impact is detected. The developed control schemes are validated through experimental work.
Right Coprime Factorizations for Single‐Input Descriptor Linear Systems: A Simple Numerically Stable Algorithm
Authors: Duan Guang‐Ren 
Published: September 2010

ABSTRACTBased on the upper‐triangular Hessenberg forms for descriptor linear systems and a method for right coprime factorization of descriptor linear systems using matrix elementary transformations, a simple, neat and numerically stable iterative formula for right coprime factorization of a regular singleinput descriptor linear system is proposed. This iterative formula gives directly the coefficients of the solutions to the coprime factorization of the system, and involves only multiplication and additions of non‐negative scalar terms. Numerical examples are presented to demonstrate the proposed algorithm.
Robust Analytical Redundancy Relations for Fault Diagnosis In Nonlinear Systems
Authors: Shumsky Alexey 
Published: September 2010

ABSTRACTThe problem of robust analytical redundancy relations design for fault diagnosis in nonlinear systems is investigated. An approach is proposed to solve this problem through an extension of the system state‐space by means of constant (or slow varying) unknown parameters, followed by designing analytical redundancy relations that are insensitive both to state variables and unknown parameters. To make the design practicable, some problems related to nonlinear transformation of the extended system model into observable one are considered. The quasi‐linear computational form for robust analytical redundancy relations is described. Fault detectability and distinguishability conditions are formulated, and a recommendation for choosing the size of the moving time window is made. The results are demonstrated through an example involving an SISO nonlinear model of an underwater vehicle.
Adaptive Control of Partially Known Systems and Application To Active Suspensions
Authors: Fukao Takanori , Yamawaki Akira , Adachi Norihiko 
Published: September 2010

ABSTRACTIn this paper, an adaptive control method is proposed for systems whose structures can be divided into a known part and an unknown part. A non‐adaptive control design, such as H∞ control design can be introduced into the known part of the system, and adaptive control can cope with the unknown part to realize the property designed by non‐adaptive control. This is achieved by means of backstepping. This method is applied to the control design of an active suspension system for a railway vehicle, which is divided into two parts: a main car body part and an actuator part. Some simulation results of the control system designed using H∞ control for the body part and adaptive control for the actuator part are provided.