Journal Article10.1109/ojcsys.2024.3368850
A Computationally-Efficient Data-Driven Safe Optimal Algorithm Through Control Merging
Marjan Khaledi,Bahare Kiumarsi +1 more
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TL;DR: A computationally-efficient data-driven safe optimal algorithm for continuous-time safety-critical systems with unknown dynamics guarantees safety and optimality by combining a safe controller and an optimal controller.
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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.
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Citations
Data‐Driven Distributed Safe Control Design for Multi‐Agent Systems
Marjan Khaledi,Bahare Kiumarsi,Marjan Khaledi,Bahare Kiumarsi +3 more
Abstract: ABSTRACT This paper presents a data‐driven control barrier function (CBF) technique for ensuring safe control of multi‐agent systems (MASs) with uncertain linear dynamics. A data‐driven quadratic programming (QP) optimization is first developed for CBF‐based safe control of single‐agent systems using a nonlinear controller. This approach is then extended to the distributed safe control of MASs. To bypass system identification, the closed‐loop dynamics are represented using collected data, and the safety constraints are imposed on this closed‐loop representation. This data‐efficient representation is subsequently integrated into QP optimizations, resulting in data‐driven QP formulations that learn the closed‐loop systems and their corresponding controllers, ensuring the safety of the MASs. As a result, the presented CBF‐based approach designs a safe controller based solely on input, state, and state‐derivative measurements without requiring knowledge of the underlying dynamics of the agents. Furthermore, for the special case of linear controllers, we show that the need for state‐derivative measurements can be eliminated. We also show that the sample complexity of learning closed‐loop dynamics is less than that of its model‐based counterpart, which relies on open‐loop system identification. The simulation results for a safe formation control problem demonstrate the efficacy of the proposed approach for the MASs.
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