Approximately Optimal Controllers for Quantitative Two-Phase Reach-Avoid Problems on Nonlinear Systems

Weber, Alexander; Knoll, Alexander

doi:10.1109/cdc42340.2020.9304048

Cited by 6 publications

(12 citation statements)

References 22 publications

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“…To be specific, the considered class of optimal control problems is defined in Section III-A and its solution in Section III-B. To a large extend, the used concepts are adopted from [27], [29], [30].…”

Section: Preliminariesmentioning

confidence: 99%

“…The function ( 4) is called total cost. Altogether, the following compact form for an optimal control problem can be given [29], [30].…”

Section: A System Dynamics and Optimal Control Problemmentioning

confidence: 99%

“…Before discussing the algorithm, we recall quantitative reach-avoid problems as they play an important role in the Roughly speaking, the key idea is to split the TSP into a sequence of special quantitative reach-avoid problems and to use an optimality result in [29].…”

Section: A Statement and Properties Of The Algorithmmentioning

confidence: 99%

“…5 indicates what goes wrong in this case, namely targets sets are reached without taking into account that another target set will follow. See [29].…”

Section: A Reconnaissance Missionmentioning

confidence: 99%

“…This class of specifications has been investigated in [24]- [26] for second order robot models. The special case of two target sets for quite general sampled-data control systems is investigated in [27]- [29]. The contribution of this paper is a constructive method for synthesizing state-feedback controllers enforcing the said coverage specification on plants possessing dynamics (1).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

On the Solution of the Travelling Salesman Problem for Nonlinear Salesman Dynamics using Symbolic Optimal Control

Weber¹,

Knoll²

2021

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This paper proposes an algorithmic method to heuristically solve the famous Travelling Salesman Problem (TSP) when the salesman's path evolves in continuous state space and discrete time but with otherwise arbitrary (nonlinear) dynamics. The presented method is based on the framework of Symbolic Control. In this way, our method returns a provably correct state-feedback controller for the underlying coverage specification, which is the TSP leaving out the requirement for optimality on the route. In addition, we utilize the Lin-Kernighan-Helsgaun TSP solver to heuristically optimize the cost for the overall taken route. Two examples, an urban parcel delivery task and a UAV reconnaissance mission, greatly illustrate the powerfulness of the proposed heuristic.

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Section: Preliminariesmentioning

confidence: 99%

“…The function ( 4) is called total cost. Altogether, the following compact form for an optimal control problem can be given [29], [30].…”

Section: A System Dynamics and Optimal Control Problemmentioning

confidence: 99%

Section: A Statement and Properties Of The Algorithmmentioning

confidence: 99%

“…5 indicates what goes wrong in this case, namely targets sets are reached without taking into account that another target set will follow. See [29].…”

Section: A Reconnaissance Missionmentioning

confidence: 99%