An adaptive neural controller based on neural emulator for single-input multi-output nonlinear systems

This paper deals with the adaptive control of single-input multi-output (SIMO) underactuated nonlinear systems. The restriction of the control authority for these systems causes major difficulties in control design. In this work, we propose an adaptive neural controller based on neural emulator to solve the control problems for a class of SIMO nonlinear systems. This controller is built by a set of partial neural controllers. New formulas are proposed to compute the validity degrees which manage the generation of the global control law. Simulations are carried out on a single-input multi-output nonlinear system. The obtained results show that the suggested control scheme provides a very good tracking and regulation performance.