Trajectory Tracking of a 4wis4wid Robot Using Adaptive Receding Horizon Control Based on Neurodynamics Optimization

Abstract

This paper presents an adaptive receding horizon control (ARHC) based on neurodynamics optimization for a wheeled robot which equips with four independently driving and steering wheels (4wis4wid). The neurodynamics-based method takes advantage of a primal–dual neural network (PDNN) which is presented for the online solution based on the linear variational inequality (LVI). The LVI-PDNN ARHC can be used to solve the convex optimization problem by optimizing a finite time horizon. In order to select an appropriate prediction horizon, an ARHC is proposed to makes the 4wis4wid wheeled robot track a given reference spline trajectory. Experiments under various kinematic models of the 4wiswid wheeled robot have been performed to illustrate the effectiveness of the proposed control strategy.