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• W3033567324 abstract "We propose a counterexample-guided inductive synthesis framework for the formal synthesis of closed-form sampled-data controllers for nonlinear systems to meet STL specifications over finite-time trajectories. Rather than stating the STL specification for a single initial condition, we consider an (infinite and bounded) set of initial conditions. Candidate solutions are proposed using genetic programming, which evolves controllers based on a finite number of simulations. Subsequently, the best candidate is verified using reachability analysis; if the candidate solution does not satisfy the specification, an initial condition violating the specification is extracted as a counterexample. Based on this counterexample, candidate solutions are refined until eventually a solution is found (or a user-specified number of iterations is met). The resulting sampled-data controller is expressed as a closed-form expression, enabling both interpretability and the implementation in embedded hardware with limited memory and computation power. The effectiveness of our approach is demonstrated for multiple systems." @default.
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• W3033567324 date "2022-05-01" @default.
• W3033567324 modified "2023-10-14" @default.
• W3033567324 title "Formal synthesis of closed-form sampled-data controllers for nonlinear continuous-time systems under STL specifications" @default.
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• W3033567324 doi "https://doi.org/10.1016/j.automatica.2022.110184" @default.
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