Constraint-adaptive MPC for linear systems: a system-theoretic framework for speeding up MPC through online constraint removal (Q6049094)

In this paper, a new online constraint removal framework for linear systems, named constraint-adaptive model predictive control (ca-MPC), is proposed. In the so-called \textit{exact} ca-MPC, the imposed constraints are adapted by removing, at each time-step, a subset of the state constraints in order to reduce the computational complexity of the receding-horizon optimal control problem, while ensuring that the closed-loop behavior is identical to that of the original MPC law. Also, it is proposed an approximate ca-MPC scheme in which a further reduction of computation time can be accomplished by a tradeoff with closed-loop performance, while still preserving recursive feasibility, stability, and constraint satisfaction properties. The online constraint removal exploits fast backward and forward reachability computations combined with optimality properties.