Sample-Based Learning Model Predictive Control for Linear Uncertain Systems
For control engineers dealing with uncertain linear systems, this work extends LMPC to handle disturbances, but it is incremental as it builds on prior deterministic LMPC.
The paper presents a sample-based Learning Model Predictive Controller for constrained uncertain linear systems with bounded additive disturbances, demonstrating safe exploration and iterative improvement of worst-case closed-loop performance on a numerical example.
We present a sample-based Learning Model Predictive Controller (LMPC) for constrained uncertain linear systems subject to bounded additive disturbances. The proposed controller builds on earlier work on LMPC for deterministic systems. First, we introduce the design of the safe set and value function used to guarantee safety and performance improvement. Afterwards, we show how these quantities can be approximated using noisy historical data. The effectiveness of the proposed approach is demonstrated on a numerical example. We show that the proposed LMPC is able to safely explore the state space and to iteratively improve the worst-case closed-loop performance, while robustly satisfying state and input constraints.