Adaptive Control for Nonaffine Nonlinear Systems Using Reliable Neural Network Approximation

This paper presents a once-differentiable control strategy for a class of uncertain nonaffine nonlinear systems based on self-structuring neural networks (SSNNs) approximation, such that the system output tracks the desired trajectory. The optimal weight for each neuron in current SSNN is time-varying signals factually, and current stability analysis is only fit for a dwell time. Current SSNN control laws are not smooth and even not continuous, due to addition or pruning of neurons in the approximation procedure. In this paper, a new SSNN estimator and a new weight update law are proposed to ensure the optimal SSNN weights being constant values and the control law being once-differentiable. The effectiveness of the proposed control law is illustrated by the stability analysis in the whole tracking procedure and shown by the simulation results.