Combined Robust and Stochastic Model Predictive Control for Models of Different Granularity

Abstract

Long prediction horizons in Model Predictive Control (MPC) often prove to be efficient, however, this comes with increased computational cost. Recently, a Robust Model Predictive Control (RMPC) method has been proposed which exploits models of different granularity. The prediction over the control horizon is split into short-term predictions with a detailed model using MPC and long-term predictions with a coarse model using RMPC. In many applications robustness is required for the short-term future, but in the long-term future, subject to major uncertainty and potential modeling difficulties, robust planning can lead to highly conservative solutions. We therefore propose combining RMPC on a detailed model for short-term predictions and Stochastic MPC (SMPC), with chance constraints, on a simplified model for long-term predictions. This yields decreased computational effort due to a simple model for long-term predictions, and less conservative solutions, as robustness is only required for short-term predictions. The effectiveness of the method is shown in a mobile robot collision avoidance simulation.

Accepted to the 2020 IFAC World Congress. The published version is available at https://doi.org/10.1016/j.ifacol.2020.12.515

Cited by in corpus (5)

• Grid-Based Stochastic Model Predictive Control for Trajectory Planning in Uncertain Environments
• Minimization of Constraint Violation Probability in Model Predictive Control
• Model Predictive Control with Models of Different Granularity and a Non-uniformly Spaced Prediction Horizon
• Minimal Constraint Violation Probability in Model Predictive Control for Linear Systems
• Multistage Stochastic Model Predictive Control for Urban Automated Driving