Robust adaptive neural network control for a class of uncertain nonlinear systems with actuator amplitude and rate saturations

Authors:
Ruyi Yuan;Xiangmin Tan;Guoliang Fan;Jianqiang Yi
Affiliations:
Institute of Automation, Chinese Academy of Sciences, Beijing, PR China;Institute of Automation, Chinese Academy of Sciences, Beijing, PR China;Institute of Automation, Chinese Academy of Sciences, Beijing, PR China;Institute of Automation, Chinese Academy of Sciences, Beijing, PR China
Venue:
Neurocomputing
Year:
2014

Citing 3
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Robust adaptive control

Robust adaptive control
Neural Networks: A Comprehensive Foundation

Neural Networks: A Comprehensive Foundation
Nearly optimal control laws for nonlinear systems with saturating actuators using a neural network HJB approach

Automatica (Journal of IFAC)

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Abstract

An adaptive controller which is designed with a priori consideration of actuator saturation effects and guarantees H^~ tracking performance for a class of multiple-input-multiple-output (MIMO) uncertain nonlinear systems with extern disturbances and actuator saturations is presented in this paper. Adaptive radial basis function (RBF) neural networks are used in this controller to approximate the unknown nonlinearities. An auxiliary system is constructed to compensate the effects of actuator saturations. Furthermore, in order to deal with approximation errors for unknown nonlinearities and extern disturbances, a supervisory control is designed, which guarantees that the closed loop system achieves a prescribed disturbance attenuation level so that H^~ tracking performance is achieved. Steady and transient tracking performance are analyzed and the tracking error is adjustable by explicit choice of design parameters. Computer simulations are presented to illustrate the efficiency of the proposed controller.