Adaptive neural network control of uncertain nonlinear plants with input saturation

In this paper, an adaptive controller is developed for uncertain nonlinear systems in the presence of input saturation. The control design is achieved by using backstepping technique with neural network approximation. Unlike some existing control schemes for systems with input saturation, the developed controller does not require uncertain parameters within a known compact set. Besides showing stability, transient performance is also established and can be adjusted by tuning certain design parameters. Simulation results obtained on a drilling system are presented to demonstrate the effectiveness of the proposed control scheme.

1 Introduction

In many practical dynamic systems, physical input saturation on hardware dictates that the magnitude of the control signal is always constrained. Saturation is a potential problem for actuators of control systems. It often severely limits system performance, giving rise to undesirable inaccuracy or leading instability. The development of adaptive control schemes for systems with input saturation has been a task of major practical interest as well as theoretical significance.