ABSTRACT
In this chapter, we present a neuro-adaptive control for a class of uncertain
nonlinear strict-feedback systems with full-state constraints and unknown actuation
characteristics where the break points of the dead-zone model are considered as
time-variant. In order to deal with the modeling uncertainties and the impact of the
non-smooth actuation characteristics, neural networks are utilized at each step of
the backstepping design. By using the barrier Lyapunov function (BLF), together
with the concept of virtual parameter, we develop a neuro-adaptive control scheme
ensuring tracking stability and at the same time maintaining full-state constraints.
The proposed control strategy bears the structure of proportional-integral (PI)
control, with the PI gains being automatically and adaptively determined, making
its design less demanding and its implementation less costly.
