Stochastic Time-Varying Model Predictive Control for Trajectory Tracking of a Wheeled Mobile Robot

In this paper, a stochastic model predictive control (MPC) is proposed for the wheeled mobile robot to track a reference trajectory within a finite task horizon. The wheeled mobile robot is supposed to subject to additive stochastic disturbance with known probability distribution. It is also supposed that the mobile robot is subject to soft probability constraints on states and control inputs. The nonlinear mobile robot model is linearized and discretized into a discrete linear time-varying model, such that the linear time-varying MPC can be applied to forecast and control its future behavior. In the proposed stochastic MPC, the cost function is designed to penalize its tracking error and energy consumption. Based on quantile techniques, a learning-based approach is applied to transform the probability constraints to deterministic constraints, and to calculate the terminal constraint to guarantee recursive feasibility. It is proved that, with the proposed stochastic MPC, the tracking error of the closed-loop system is asymptotically average bounded. A simulation example is provided to support the theoretical result.

Automated summary

This paper proposes a stochastic model predictive control (MPC) for wheeled mobile robots to track a reference trajectory within a finite task horizon. The control approach linearizes and discretizes the nonlinear robot model, and incorporates learning-based techniques for transforming probability constraints into deterministic ones. The proposed stochastic MPC is proven to bound the tracking error of the closed-loop system.