Browsing by Author "KHALIL, HK"

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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 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
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.