ABSTRACT

This chapter considers the tracking control problem for a class of multi-input

multi-output (MIMO) nonlinear systems subject to unknown actuation

characteristics and external disturbances. Neuro-adaptive PI control with self-tuning

gains is proposed, which is structurally simple and computationally inexpensive.

Different from traditional PI control, the proposed one is able to adjust its PI gains

online using stability-guaranteed analytic algorithms without involving manual

tuning or the trial and error process. It is shown that the proposed neuro-adaptive PI

control is continuous and smooth everywhere and ensures the uniformly ultimately

boundedness of all the signals of the closed-loop system. Furthermore, the crucial

compact set precondition for a neural network (NN) to function properly is

guaranteed with the barrier Lyapunov function (BLF), allowing the NN unit to play

its learning/approximating role during the entire system operation.The salient

feature also lies in its low complexity in computation and effectiveness in dealing

with modeling uncertainties and nonlinearities. Both square and non-square

nonlinear systems are addressed. The benefits and feasibility of the developed

control are also confirmed by simulations.