Learning Model Predictive Control for iterative tasks. A Data-Driven Control Framework
For control systems performing repetitive tasks, this work provides a data-driven framework that ensures safety and monotonic improvement, but the approach is incremental as it builds on existing MPC and learning methods.
The paper presents a Learning Model Predictive Controller (LMPC) for iterative tasks that improves performance over iterations without a reference, using safe sets and terminal costs to guarantee recursive feasibility and non-increasing performance. Simulation results demonstrate effectiveness.
A Learning Model Predictive Controller (LMPC) for iterative tasks is presented. The controller is reference-free and is able to improve its performance by learning from previous iterations. A safe set and a terminal cost function are used in order to guarantee recursive feasibility and non-increasing performance at each iteration. The paper presents the control design approach, and shows how to recursively construct terminal set and terminal cost from state and input trajectories of previous iterations. Simulation results show the effectiveness of the proposed control logic.