Conventional designs for model predictive control typically rely on terminal costs and constraints derived from a steady state to guarantee closed-loop stability and performance. However, this dependence on a steady-state assumption limits the applicability of this control method to systems in which such a fixed operating point is either not available or not desirable. This work introduces a novel framework, termed policy-guided MPC, to address this limitation. Our approach constructs terminal costs and constraints using a known sub-optimal control policy. Specifically, the terminal region is defined around a center determined by a rollout of the policy, and a penalty on deviation from this center is used to define the terminal cost. This method obviates the need for a steady state or reference trajectory. Closed-loop performance guarantees are established relative to the guiding policy, for both finite and infinite horizon problems. The effectiveness of the proposed framework is demonstrated through numerical simulations on an energy management example.
The main contribution is to explore the time‐varying definition of the constraints in the indirect feedback SMPC (IF‐SMPC) to improve the feasible domain and explore the time‐varying definition of the constraints in the indirect feedback SMPC to improve the feasible domain.
T. Santos, Sorin Olaru· Optimal control applications...· 0 citations
A novel spectrum assignment method is proposed to obtain an initial stabilizer for PI in continuous-time indefinite stochastic linear quadratic control with the help of the Lyapunov-type operator's spectrum, which is gradually approximated from the stable auxiliary system by adjusting a cumulative factor, thereby obtai...
We study data-driven policy construction for dynamical systems using trajectories generated by surrogate finite-horizon Model Predictive Control (MPC), with the goal of approximating an infinite-horizon optimal control policy. We interpret these trajectories as non-expert demonstrations and propose a memory-based, nonp...
This paper proposes an adaptive-sampling control framework for linear systems subject to polytopic state and input constraints and bounded additive disturbances that continuously calculates Model Predictive Control update rates that ensure robust constraint satisfaction at all time steps.
Spencer Schutz, Charlott Vallon, Francesco Borrelli· 0 citations
This article investigates data-driven policy iteration (PI) for continuous-time linear systems without requiring an initially stabilizing policy. Standard infinite-horizon PI is not self-starting because its policy-evaluation step is well posed only when the feedback gain is stabilizing. However, verifying this propert...
Jia-Cheng Wu, Yang Zhu· 0 citations
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