Download PDFOpen PDF in browserExperiments in Verification of Linear Model Predictive Control: Automatic Generation and Formal Verification of an Interior Point Method Algorithm17 pages•Published: October 23, 2018AbstractClassical control of cyber-physical systems used to rely on basic linear controllers. These controllers provided a safe and robust behavior but lack the ability to perform more complex controls such as aggressive maneuvering or performing fuel-efficient controls. Another approach called optimal control is capable of computing such difficult trajectories but lacks the ability to adapt to dynamic changes in the environment. In both cases, the control was designed offline, relying on more or less complex algorithms to find the appropriate parameters. More recent kinds of approaches such as Linear Model-Predictive Control (MPC) rely on the online use of convex optimization to compute the best control at each sample time. In these settings optimization algorithms are specialized for the specific control problem and embed on the device.This paper proposes to revisit the code generation of an interior point method (IPM) algorithm, an efficient family of convex optimization, focusing on the proof of its implementation at code level. Our approach relies on the code specialization phase to produce additional annotations formalizing the intended specification of the algorithm. Deductive methods are then used to prove automatically the validity of these assertions. Since the algorithm is complex, additional lemmas are also produced, allowing the complete proof to be checked by SMT solvers only. This work is the first to address the effective formal proof of an IPM algorithm. The approach could also be generalized more systematically to code generation frameworks, producing proof certificate along the code, for numerical intensive software. Keyphrases: convex optimization, interior point method, model predictive control, numerical software verification, program analysis, satisfiability modulo theory In: Gilles Barthe, Geoff Sutcliffe and Margus Veanes (editors). LPAR-22. 22nd International Conference on Logic for Programming, Artificial Intelligence and Reasoning, vol 57, pages 290-306.
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