1.Lanzhou University of Technology;2.Qufu Normal University
The National Natural Science Foundation of China (General Program, Key Program, Major Research Plan)
This paper investigates optimal path planning and tracking in the obstacle avoidance process of nonlinear unmanned vehicles with bounded disturbances, a model predictive control (MPC) method based on the predictive input and output contraction constraints (PIOCC) of the system is proposed. First, when constructing the objective function, the idea of soft constraints is introduced to expand the range of feasible solutions, and the problem of following the optimal planning path is transformed into the solution of the model predictive control optimization problem. Secondly, in order to avoid the divergence of the closed-loop system caused by the short prediction time domain, resulting in infeasible solutions under the constraint conditions, the method of the predictive input and output contraction constraints of the system in the time domain is further adopted. Then based on the Lyapunov stability theory, the stability of the closed-loop model predictive control system designed is proved in this paper. Finally, through a simulation example, it verifies the effectiveness of the proposed control strategy based on PIOCC in solving the problem of expanding the range of feasible solutions and avoiding the divergence of the closed-loop system, the control requirements of good following and stability are realized when the driverless vehicle follows the optimal planning path during obstacle avoidance.