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• W4384471488 abstract "Abstract There is a pressing need for learning controllers to endow systems with properties of safety and goal-reaching, which are crucial for many safety-critical systems. Reinforcement learning (RL) has been deployed successfully to synthesize controllers from user-defined reward functions encoding desired system requirements. However, it remains a significant challenge in synthesizing provably correct controllers with safety and goal-reaching requirements. To address this issue, we try to design a special hybrid polynomial-DNN controller which is easy to verify without losing its expressiveness and flexibility. This paper proposes a novel method to synthesize such a hybrid controller based on RL, low-degree polynomial fitting and knowledge distillation. It also gives a computational approach, by building and solving a constrained optimization problem coming from verification conditions to produce barrier certificates and Lyapunov-like functions, which can guarantee every trajectory from the initial set of the system with the resulted controller satisfies the given safety and goal-reaching requirements. We evaluate the proposed hybrid controller synthesis method on a set of benchmark examples, including several high-dimensional systems. The results validate the effectiveness and applicability of our approach." @default.
• W4384471488 created "2023-07-17" @default.
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• W4384471488 date "2023-01-01" @default.
• W4384471488 modified "2023-09-24" @default.
• W4384471488 title "Hybrid Controller Synthesis for Nonlinear Systems Subject to Reach-Avoid Constraints" @default.
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• W4384471488 doi "https://doi.org/10.1007/978-3-031-37706-8_16" @default.
• W4384471488 hasPublicationYear "2023" @default.
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