Browsing by Author "CHEN, FC"

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ADAPTIVE CONTROL OF NONLINEAR CONTINUOUS-TIME SYSTEMS USING NEURAL NETWORKS - GENERAL RELATIVE DEGREE AND MIMO CASES
(1994)
CHEN, FC
;
LIU, CC
;
電控工程研究所
;
Institute of Electrical and Control Engineering
ADAPTIVE-CONTROL OF A CLASS OF NONLINEAR DISCRETE-TIME-SYSTEMS USING NEURAL NETWORKS
(1995-05-01)
CHEN, FC
;
KHALIL, HK
;
電控工程研究所
;
Institute of Electrical and Control Engineering
Layered neural networks are used in a nonlinear self-tuning adaptive control problem, The plant is an unknown feedback-linearizable discrete-time system,represented by an input-output model. To derive the linearizing-stabilizing feedback control, a (possibly nonminimal) state-space model of the plant is obtained. This model is used to define the zero dynamics, which are assumed to be stable, i.e., the system is assumed to be minimum phase. A linearizing feedback control is derived in terms of some unknown nonlinear functions. A layered neural network is used to model the unknown system and generate the feedback control. Based on the error between the plant output and the model output, the weights of the neural network are updated. A local convergence result is given. The result says that for any bounded initial conditions of the plant, if the neural network model contains enough number of nonlinear hidden neurons and if the initial guess of the network weights is sufficiently close to the correct weights, then the tracking error between the plant output and the reference command will converge to a bounded ball, whose size is determined by a dead-zone nonlinearity. Computer simulations verify the theoretical result.
ADAPTIVE-CONTROL OF LINEARIZABLE DISCRETE-TIME-SYSTEMS
(1994)
CHEN, FC
;
TSAO, WC
;
電控工程研究所
;
Institute of Electrical and Control Engineering
ADAPTIVE-CONTROL OF NONLINEAR CONTINUOUS-TIME SYSTEMS USING NEURAL NETWORKS GENERAL RELATIVE DEGREE AND MIMO CASES
(1993-08-01)
LIU, CC
;
CHEN, FC
;
交大名義發表
;
電控工程研究所
;
National Chiao Tung University
Multilayer neural networks are used in a non-linear adaptive control problem. The plant is an unknown feedback linearizable continuous-time system with relative degree greater-than-or-equal-to 1. The single-input/single-output system is studied first and then the methodology is extended to control square multi-input/multi-output systems. The control objective is for the plant to track a reference trajectory, and the control law is defined in terms of the outputs of the neural networks. The parameters of the networks are updated on-line according to an augmented tracking error and the network derivatives. A local convergence theorem is given on the convergence of the tracking error. This control algorithm is applied to control a two-input/two-output relative-degree-two system.
ADAPTIVE-CONTROL OF NONLINEAR-SYSTEMS USING NEURAL NETWORKS
(1992-06-01)
CHEN, FC
;
KHALIL, HK
;
電控工程研究所
;
Institute of Electrical and Control Engineering
Layered networks are used in a nonlinear adaptive control problem. The plant is an unknown feedback-linearizable discrete-time system, represented by an input-output model. A state space model of the plant is obtained to define the zero dynamics, which are assumed to be stable. A linearizing feedback control is derived in terms of some unknown nonlinear functions. To identify these functions, it is assumed that they can be modelled by layered neural networks. The weights of the networks are updated and used to generate the control. A local convergence result is given. Computer simulations verify the theoretical result.
ADAPTIVE-CONTROL OF NONLINEAR-SYSTEMS USING NEURAL NETWORKS
(1990)
CHEN, FC
;
KHALIL, HK
;
電控工程研究所
;
Institute of Electrical and Control Engineering
ADAPTIVELY CONTROLLING NONLINEAR CONTINUOUS-TIME SYSTEMS USING MULTILAYER NEURAL NETWORKS
(1994-06-01)
CHEN, FC
;
LIU, CC
;
交大名義發表
;
電控工程研究所
;
National Chiao Tung University
Multilayer neural networks are used in a nonlinear adaptive control problem. The plant is an unknown feedback-linearizable continuous-time system. The control law is defined in terms of the neural network models of system nonlinearities to control the plant to track a reference command. The network parameters are updated on-line according to a gradient learning rule with dead zone. A local convergence result is provided, which says that if the initial parameter errors are small enough, then the tracking error will converge to a bounded area. Simulations are designed to demonstrate various aspects of theoretical results.
EFFECT OF ALUMINUM ON TRIP FE-MN-AL ALLOY-STEELS AT ROOM-TEMPERATURE
(1993-02-28)
CHEN, FC
;
CHOU, CP
;
LI, P
;
CHU, SL
;
機械工程學系
Fe-Mn-Al alloy steel has undergone many significant developments during the past few years, and it is considered to be a steel of high strength and toughness. Studies and test results have shown that Fe-Mn-Al is a good potential replacement for conventional stainless steels which contain expensive nickel and chromium additions. Recently, it has been found that strain-induced martensitic transformation at room temperature is possible in austenitic Fe-Mn-Al alloy. Further study has shown that the alpha' martensite can improve the mechanical properties of these Fe-Mn-Al alloys. However, as the aluminium content increases, the strain-induced phase transformation is inhibited. Moreover, the mechanical properties of these materials are very dependent on the aluminium content.
EVIDENCE OF STRAIN-INDUCED MARTENSITIC-TRANSFORMATION IN FE-MN-AL AUSTENITIC ALLOY-STEELS AT ROOM-TEMPERATURE
(1991-03-01)
CHEN, FC
;
LI, P
;
CHU, SL
;
CHOU, CP
;
機械工程學系
H-INFINITY CONTROL OF 2-TIME-SCALE SYSTEMS
(1992-07-01)
KHALIL, HK
;
CHEN, FC
;
電控工程研究所
;
Institute of Electrical and Control Engineering
H(infinity) control of linear time-invariant singularly perturbed systems is considered. A sequential procedure is described to decompose the problem into slow and fast subproblems. The fast problem is solved first. Then the slow problem is solved under a constraint on the value of the compensator at infinity. A composite compensator is formed as the parallel connection of the fast compensator with the strictly proper part of the slow compensator. The asymptotic validity of the composite compensator is established.
Practical stability issues in CMAC neural network control systems
(1995)
CHEN, FC
;
CHANG, CH
;
電控工程研究所
;
Institute of Electrical and Control Engineering
PRACTICAL STABILITY ISSUES IN CMAC NEURAL-NETWORK CONTROL-SYSTEMS
(1994)
CHEN, FC
;
CHANG, CH
;
電控工程研究所
;
Institute of Electrical and Control Engineering
THE EFFECT OF ALUMINUM AND CARBON ON THE STRUCTURAL-CHANGES AND STRAIN-HARDENING OF AUSTENITIC FE-MN-AL ALLOY-STEELS DURING PLASTIC-DEFORMATION
(1991-08-01)
LI, P
;
CHU, SL
;
CHOU, CP
;
CHEN, FC
;
機械工程學系
USE OF ULTRASONIC VELOCITY FOR NONDESTRUCTIVE EVALUATION OF FERRITE CONTENT IN DUPLEX FE-MN-AL ALLOY-STEELS
(1992-01-01)
LI, P
;
CHU, SL
;
CHOU, CP
;
CHEN, FC
;
機械工程學系