A Sparse Neural Network-Based Control Method for Saturated Nonlinear Affine Systems

Abstract

Saturated nonlinear affine systems are widely encountered in many engineering fields. Currently, most control methods on saturated nonlinear affine systems are not specifically designed based on sparsity-based control methodologies, and they might require sparse identification at the beginning stage and applying tracking control afterwards. In this paper, a sparse neural network (SNN)-based control method from an lp-norm (1≤p<2) optimization perspective is proposed for saturated nonlinear affine systems by taking advantage of the nice properties of primal dual neural networks for optimization. In particular, when p=1, a new alternative controller based on SNN is derived, encountering computational difficulties distinct from those of another solution set in the basic dual neural network. The convergence properties of such SNN-based controllers are investigated and analyzed to find a control solution. Five illustrative examples further are shown to demonstrate the efficiency of the proposed SNN-based control method for tracking the desired references of saturated nonlinear affine systems. In the practical application scenario involving the UR5 robot control, the trajectory’s average errors are consistently confined to a minimal magnitude of 10−4 m. These findings substantiate the efficacy of the SNN-based control approach proposed for precise tracking control in saturated nonlinear affine systems.