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Creators/Authors contains: "Tang, Sunbochen"

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  1. ; ; ; (, Journal of Guidance, Control, and Dynamics)
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  2. ; ; ; (, Proceedings of 2021 60th IEEE Conference on Decision and Control (CDC))
    In this paper, we propose a convex optimization approach to chance-constrained drift counteraction optimal control (DCOC) problems for linear systems with additive stochastic disturbances. Chance-constrained DCOC aims to compute an optimal control law to maximize the time duration before the probability of violating a prescribed set of constraints can no longer be maintained to be below a specified risk level. While conventional approaches to this problem involve solving a mixed-integer programming problem, we show that an optimal solution to the problem can also be found by solving a convex second-order cone programming problem without integer variables. We illustrate the application of chance-constrained DCOC to an automotive adaptive cruise control example. 
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  3. ; ; ; (, Proceedings of 2021 American Control Conference)
    Drift counteraction optimal control (DCOC) aims at optimizing control to maximize the time (or a yield) until the system trajectory exits a prescribed set, which may represent safety constraints, operating limits, and/or efficiency requirements. To DCOC problems formulated in discrete time, conventional approaches were based on dynamic programming (DP) or mixed-integer programming (MIP), which could become computationally prohibitive for higher-order systems. In this paper, we propose a novel approach to discrete-time DCOC problems based on a nonlinear programming formulation with purely continuous variables. We show that this new continuous optimization-based approach leads to the same exit time as the conventional MIP-based approach, while being computationally more efficient than the latter. This is also illustrated by numerical examples representing the drift counteraction control for an indoor airship. 
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