Disturbance-adaptive Model Predictive Control for Bounded Average Constraint Violations
For control engineers dealing with safety-critical systems under unknown disturbances, this work provides a method to guarantee average constraint violations while improving performance.
This paper proposes a disturbance-adaptive MPC framework for stochastic linear systems that adjusts the disturbance model based on measured constraint violations, ensuring bounds on average constraint violations even with inaccurate models. Simulations show reduced cumulative cost compared to state-of-the-art methods across different target violation rates.
This paper considers stochastic linear time-invariant systems subject to constraints on the average number of state-constraint violations over time without knowing the disturbance distribution. We present a novel disturbance-adaptive model predictive control (DAD-MPC) framework, which adjusts the disturbance model based on measured constraint violations. Using a robust invariance method, DAD-MPC ensures recursive feasibility and guarantees asymptotic or robust bounds on average constraint violations. Additionally, the bounds hold even with an inaccurate disturbance model, which allows for data-driven disturbance quantification methods to be used, such as conformal prediction. Simulation results demonstrate that the proposed approach reduces closed-loop cumulative cost compared to state-of-the-art methods across different target violation rates, while satisfying average violation bounds.