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• W2887505977 abstract "The growing interest in robust motion planning under safety constraints for robotic systems, such as autonomous vehicles or collaborative robots, lead to an increased interest to prove the stability or stabilizability of dynamic models of such systems. The proofs are usually based on Lyapunov theory and often rely on “funnels”, tubes or similar invariants that bound variations along reference trajectories. Recent methods have shown how to compute such funnels via sum-of-square (SoS) optimization. In this work we propose a new approach to solve this problem relying on optimal control based state-space partitioning and relaxation techniques for polynomial programming. In contrast to SoS techniques, our approach is inherently suitable to supply certificates of local stability and deal with bounded control inputs without increasing the complexity of the arising optimization problem. We present how to use our approach to obtain a large inner approximation of the true region of attraction around a given trajectory for which it is ensured that all states inside the funnel will reach goal states in a given time period. This property makes it interesting to integrate our approach in a general path or task planning algorithm. We compare our results in simulation with the ones obtained using a state-of-art method based on SoS techniques for a torque controlled simple pendulum and an underactuated double pendulum (Acrobot)." @default.
• W2887505977 created "2018-08-22" @default.
• W2887505977 creator A5070485120 @default.
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• W2887505977 date "2018-06-01" @default.
• W2887505977 modified "2023-10-15" @default.
• W2887505977 title "Computing regions of stabilizability for nonlinear control systems with input constraints" @default.
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• W2887505977 doi "https://doi.org/10.23919/acc.2018.8430798" @default.
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