bluuebunny/crossref_metadata_2025_split huggingface dataset:
ECOLOGICAL SYSTEMS CONTROL BY FUZZY LOGIC CONTROLLER
Authors: Lin Chih‐Min , Mon Yi‐Jen , Maa Jiann‐Hwa 
Published: September 2010

ABSTRACTThe 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.
DISCRETE OUTPUT FUZZY CONTROLLER DESIGN FOR ACHIEVING COMMON CONTROLLABILITY GRAMIAN
Authors: Chang Wen‐Jer , Sun Chein‐Chung , Fuh Chyun‐Chau 
Published: September 2010

ABSTRACTIn 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.
SLIDING VECTOR DESIGN BASED ON THE POLE‐ASSIGNMENT METHOD
Authors: Chang Jeang‐Lin , Chen Yon‐Ping 
Published: September 2010

ABSTRACTThis 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

ABSTRACTIn 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

ABSTRACTThis 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

ABSTRACTIn 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

ABSTRACTA 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

ABSTRACTA 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

ABSTRACTThis 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

ABSTRACTIn 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

ABSTRACTThe 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

ABSTRACTA 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

ABSTRACTIn 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

ABSTRACTIn 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

ABSTRACTPath 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

ABSTRACTA 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

ABSTRACTIndividual 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

ABSTRACTFault 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

ABSTRACTA 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

ABSTRACTA 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

ABSTRACTIn 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

ABSTRACTA 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

ABSTRACTIt 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

ABSTRACTThis 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

ABSTRACTUnderstanding 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.