Robust Adaptive MPC in the Presence of Nonlinear Time-Varying Uncertainties: An Uncertainty Compensation Approach
This work addresses robust control for systems with uncertainties, which is critical for safety-critical applications like aerospace, but it appears incremental as it builds on existing MPC and adaptive control methods.
The paper tackles controlling linear systems with nonlinear time-varying uncertainties by introducing an uncertainty compensation-based robust adaptive MPC framework that integrates L1 adaptive and robust feedback controllers, ensuring constraint satisfaction and showing enhanced performance in simulations of flight control and spacecraft landing.
This paper introduces an uncertainty compensation-based robust adaptive model predictive control (MPC) framework for linear systems with nonlinear time-varying uncertainties. The framework integrates an L1 adaptive controller to compensate for the matched uncertainty and a robust feedback controller, designed using linear matrix inequalities, to mitigate the effect of unmatched uncertainty on target output channels. Uniform bounds on the errors between the system's states and control inputs and those of a nominal (i.e., uncertainty-free) system are derived. These error bounds are then used to tighten the actual system's state and input constraints, enabling the design of an MPC for the nominal system under these tightened constraints. Referred to as uncertainty compensation-based MPC (UC-MPC), this approach ensures constraint satisfaction while delivering enhanced performance compared to existing methods. Simulation results for a flight control example and a spacecraft landing on an asteroid demonstrate the effectiveness of the proposed framework.