Nonlinear Model Predictive Control with Cost Function Scheduling for a Wheeled Mobile Robot

摘要

Designing a cost function for nonlinear model predictive control (MPC) with a sparse/binary stage cost is challenging. This paper proposes a novel MPC approach with a scheduled quadratic stage cost function that approximates the true stage cost in order to optimally control a nonlinear system with a sparse/binary stage cost. The cost function parameter is optimally scheduled by a parameter scheduling policy obtained by solving a Markov decision process (MDP) constructed from sampled trajectories from any nonlinear MPC solver. The pro-posed approach is implemented into a differential drive wheeled mobile robot (WMR) designed for smart warehousing via the robot operating system (ROS) framework. The simulation and experimental results successfully demonstrate the effectiveness of our MPC approach in cases of the point stabilization problem of a differential drive WMR.