Adaptive Neural Prescribed Performance DSC for Non-affine SISO Nonlinear Systems with External Disturbances

Abstract Explosion of complexity and undesirable transient response of systems, are two major problems that conventional backstepping methods suffer from it. Furthermore, lack of information about the system and undesirable external disturbances are other problems that have been addressed in this paper. Therefore, an adaptive neural controller is designed to consider the proposed problems in this paper. The presented controller is constructed for the class of single-input, single-output (SISO) non-affine strict feedback systems with unknown gain signs and a neural network is employed to approximate unknown functions. By applying dynamic surface control (DSC) and prescribed performance functions, two major problems of an explosion in terms and the transient response of the system will be solved, respectively. Nussbaum functions are also utilized to address the problem of unknown gain signs. The proposed controller guarantees that all the closed-loop signals are semi-globally, uniformly ultimately bounded (SGUUB). Finally, in order to show the feasibility of this approach, a simulation example is provided.