A Computationally-Efficient Data-Driven Safe Optimal Algorithm Through Control Merging

Abstract

This article presents a proactive approach to resolving the conflict between safety and optimality for continuous-time (CT) safety-critical systems with unknown dynamics. The presented method guarantees safety and performance specifications by combining two controllers: a safe controller and an optimal controller. On the one hand, the safe controller is designed using only input and state data measurements and without requiring the state derivative data, which are typically required in data-driven control of CT systems. State derivative measurement is costly, and its approximation introduces noise to the system. On the other hand, the optimal controller is learned using a low-complexity one-shot optimization problem, which again does not rely on prior knowledge of the system dynamics and state derivative data. Compared to existing optimal control learning methods for CT systems, which are typically iterative, a one-shot optimization is considerably more sample-efficient and computationally efficient. The share of optimal and safe controllers in the overall control policy is obtained by solving a computationally efficient optimization problem involving a scalar variable in a data-driven manner. It is shown that the contribution of the safe controller dominates that of the optimal controller when the system's state is close to the safety boundaries, and this domination drops as the system trajectories move away from the safety boundaries. In this case, the optimal controller contributes more to the overall controller. The feasibility and stability of the proposed controller are shown. Finally, the simulation results show the efficacy of the proposed approach.

Keywords

• Control barrier functions (CBFs)
• data-driven initiative controller
• optimality
• safe control
• unknown systems