A Model Predictive Cloud-Based Control Scheme for Trajectory-Tracking: Effects of Round-Trip Time Over-Estimates

摘要

This paper addresses the design and implementation of a Model Predictive Control (MPC) scheme for trajectory tracking on a robotic vehicle with limited onboard computation power by exploiting an external web-server to solve the related MPC optimization problem. Specifically, an MPC controller that globally asymptotically stabilizes the origin of a suitable tracking error is designed, deployed in a web-server, and utilized in a web-server-in-the-Ioop fashion. The optimal state and input predictions computed by the web-server are fused in the onboard unit using smart buffers to obtain the control signal to apply to the vehicle. Numerical simulations show the robustness of the proposed scheme to additive disturbances in the state equation of the vehicle model and highlight the effect of the selection of the round-trip time guess that is utilized in the implementation of the web-server-in-the-loop strategy, in the closed-loop tracking performance.