Safe Linear-Quadratic Dual Control with Almost Sure Performance Guarantee
It addresses control problems requiring simultaneous parameter identification and optimization, offering stronger guarantees than probabilistic bounds, though it is incremental in improving existing methods.
This paper tackles the linear-quadratic dual control problem by proposing an online algorithm that guarantees asymptotic optimality in the almost sure sense, with parameter inference error scaling as O(T^{-1/4+ε}) and suboptimality gap as O(T^{-1/2+ε}).
This paper considers the linear-quadratic dual control problem where the system parameters need to be identified and the control objective needs to be optimized in the meantime. Contrary to existing works on data-driven linear-quadratic regulation, which typically provide error or regret bounds within a certain probability, we propose an online algorithm that guarantees the asymptotic optimality of the controller in the almost sure sense. Our dual control strategy consists of two parts: a switched controller with time-decaying exploration noise and Markov parameter inference based on the cross-correlation between the exploration noise and system output. Central to the almost sure performance guarantee is a safe switched control strategy that falls back to a known conservative but stable controller when the actual state deviates significantly from the target state. We prove that this switching strategy rules out any potential destabilizing controllers from being applied, while the performance gap between our switching strategy and the optimal linear state feedback is exponentially small. Under our dual control scheme, the parameter inference error scales as $O(T^{-1/4+ε})$, while the suboptimality gap of control performance scales as $O(T^{-1/2+ε})$, where $T$ is the number of time steps, and $ε$ is an arbitrarily small positive number. Simulation results on an industrial process example are provided to illustrate the effectiveness of our proposed strategy.