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.