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Heidelberg: Springer.Īnnpureddy, Y., Liu, C., Fainekos, G.

Tarraf (Ed.), Control of cyber-physical systems: workshop held at Johns Hopkins University (pp. Towards the unification of locomotion and manipulation through control Lyapunov functions and quadratic programs. In 2014 48th annual conference on information sciences and systems (CISS) (pp. DSOS and SDSOS optimization: LP and SOCP-based alternatives to sum of squares optimization. Transactions on Embedded Computing Systems (TECS), 12, 95.Īhmadi, A. Probabilistic temporal logic falsification of cyber-physical systems. Our approach is able to synthesize relatively simple polynomial control Lyapunov-like functions, and in that process replace the MPC using a guaranteed and computationally less expensive controller.Ībbas, H., Fainekos, G., Sankaranarayanan, S., Ivancic, F., & Gupta, A. We show how the verifier can be constructed efficiently using convex relaxations of the verification problem for polynomial systems to semi-definite programming problem instances. We also implement this scheme using nonlinear MPC controllers to serve as demonstrators for a set of state and trajectory stabilization problems for nonlinear dynamical systems.

We prove its convergence using ellipsoidal approximation techniques from convex optimization. The overall learning framework is iterative, eliminating a set of candidates on each iteration using the counterexamples discovered by the verifier and the demonstrations over these counterexamples. The learning framework uses a demonstrator that implements a black-box, untrusted strategy presumed to solve the problem of interest, a learner that poses finitely many queries to the demonstrator to infer a candidate function, and a verifier that checks whether the current candidate is a valid control Lyapunov-like function. We present a technique for learning control Lyapunov-like functions, which are used in turn to synthesize controllers for nonlinear dynamical systems that can stabilize the system, or satisfy specifications such as remaining inside a safe set, or eventually reaching a target set while remaining inside a safe set.