Nonlinear Adaptive Decoupling Control Based on Neural Networks and Multiple Models

For a class of uncertain nonlinear multivariable discrete time dynamic systems, an adaptive decoupling controller (ADC) is presented, which can deal with the case that the zero dynamics (ZD) of the system is not asymptotically stable. The ADC developed is composed of a linear robust ADC, a neural network (NN) nonlinear ADC and a switching mechanism. The linear robust ADC can assure the bounded-input-boundedoutput (BIBO) stability of the closed-loop system. The nonlinear NN ADC can improve the system performance. The switching mechanism is utilized to obtain the improved system performance and stability simultaneously. Theory analysis and simulation results are presented to show the effectiveness of the proposed method.