Trajectory-Tracking Control of Mobile Robot Systems Incorporating Neural-Dynamic Optimized Model Predictive Approach

Abstract

Mobile robots tracking a reference trajectory are constrained by the motion limits of their actuators, which impose the requirement for high autonomy driving capabilities in robots. This paper presents a model predictive control (MPC) scheme incorporating neural-dynamic optimization to achieve trajectory tracking of nonholonomic mobile robots (NMRs). By using the derived tracking-error kinematics of nonholonomic robots, the proposed MPC approach is iteratively transformed as a constrained quadratic programming (QP) problem, and then a primal–dual neural network is used to solve this QP problem over a finite receding horizon. The applied neural-dynamic optimization can make the cost function of MPC converge to the exact optimal values of the formulated constrained QP. Compared with the existing fast MPC, which requires repeatedly calculating the Hessian matrix of the Langragian and then solves a quadratic program. The computation complexity reaches ${O}({n}^{{3}})$ , while the proposed neural-dynamic optimization contains ${O}({n}^{{2}})$ operations. Finally, extensive experiments are provided to illustrate that the MPC scheme has an effective performance on a real mobile robot system.