Reach-Avoid Model Predictive Control with Guaranteed Recursive Feasibility via Input Constrained Backstepping
For control engineers, it provides a constructive method to ensure safety and feasibility in nonlinear systems with physical constraints, though the approach is incremental.
This paper proposes a sampled-data MPC framework for continuous nonlinear systems that guarantees reach-avoid and recursive feasibility under input and output constraints, using a backstepping-based invariant set as a terminal set. Numerical results show efficacy.
This letter proposes a novel sampled-data model predictive control framework for continuous control-affine nonlinear systems that provides rigorous reach-avoid and recursive feasibility guarantees under physical constraints. By propagating both input and output constraints through backstepping process, we present a constructive approach to synthesize a reach-avoid invariant set that complies with control input limits. Using this reach-avoid set as a terminal set, we prove that the proposed sampled-data MPC framework recursively admits feasible control inputs that safely steer the continuous system into the target set under fast sampling conditions. Numerical results demonstrate the efficacy of the proposed approach.