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Distributed Model Predictive Control for Probabilistic Signal Temporal Logic Specifications
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AI
DOI：10.1109/TASE.2023.3323472.png)
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Abstract
En 中文
This paper proposes a distributed model predictive control (DMPC) for a class of discrete-time stochastic multi-agent systems subject to partially coupled temporal logic tasks. For each agent, the given tasks are formulated as local and coupled probabilistic signal temporal logic (PrSTL) constraints in DMPC, and the control objective is to satisfy the PrSTL constraints against stochastic uncertainties and the coupled spatio-temporal relationship between agents. Considering that control under STL is historically dependent, a shrinking horizon DMPC framework is adopted and a probabilistic-tightening method is proposed to transform the complex form of PrSTL into deterministic constraints. Then, combining with the asynchronous update strategy, the satisfaction verification of coupled PrSTL tasks is achieved. Since large uncertainties may result in optimization infeasibility and affect the completion of the temporal logic tasks, a distributed PrSTL task softening method is further proposed, which can guarantee the softened tasks converge to the original ones and reduce the conservatism of the controller design. The recursive feasibility of the proposed PrSTL-DMPC strategy is proved and the efficiency of the algorithm is demonstrated by simulations.
Keywords:
Task analysis
Lenses
Uncertainty
Probabilistic logic
Multi-agent systems
Stochastic processes
Control systems
Predictive control
autonomous agents
signal temporal logic
uncertain systems
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