Constrained Control of a Class of Uncertain Nonlinear MIMO Systems Using Neural Networks

Authors:
Dingguo Chen;Jiaben Yang
Affiliations:
Siemens Power Transmission and Distribution Inc., 10900 Wayzata Blvd., Minnetonka, Minnesota 55305, USA;Department of Automation, Tsinghua University, Beijing, 100084, People's Republic of China
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Year:
2007

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Abstract

This paper attempts to present a neural inverse control design framework for a class of nonlinear multiple-input multiple-output (MIMO) system with uncertainties. This research effort is motivated by the following considerations: (a) An appropriate reference model that accurately represents the desired system dynamics is usually assumed to exist and to be available, and yet in reality this is not the case often times; (b) In real world applications, there are many cases where controls are constrained within a physically allowable range, which presents another layer of difficulties to directly apply the reference model based inverse control; (c) It is difficult to consider optimal control even for the reference model as in general the analytic solution to the optimal control problem is not available. The simulation study is conducted on a single-machine infinite-bus (SMIB) system to illustrate the proposed design procedure and demonstrates the effectiveness of the proposed control approach.