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. Firstly, 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, which will lead to infeasible solutions under the constraint conditions, the model prediction controller is designed by using the method of prediction in time domain of system input and output contraction constraints. Then based on the Lyapunov stability theory, it is proved that the closed-loop control system designed is asymptotically stable. Finally, through a simulation example, the effectiveness of the proposed control strategy based on PIOCC in expanding the feasible solution range and avoiding the divergence of the closed-loop system is verified, which achieves the good rapidity and stability of the unmanned vehicle in path tracking.