Sequential Quadratic Programming Algorithm for Real-Time Mixed-Integer Nonlinear MPC

Quirynen, Rien; Cairano, Stefano Di

doi:10.1109/cdc45484.2021.9683714

Cited by 9 publications

(2 citation statements)

References 55 publications

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“…The MIQP of the first stage is formed as an approximation of the original MINLP, in a manner similar to how the Quadratic Programming (QP) sub-problems are formed in Sequential Quadratic Programming (SQP) [11]. The idea of using SQPlike methods in approximate solution of MINLPs has been used in other works [13], [14], however, to the authors' best knowledge, it has not been adapted and applied to vehicle coordination problems. A structurally similar heuristic was presented in [9], where a scheduling problem is derived from the original MINLP, with the introduction of a number of approximations, and solved as an MIQP.…”

Section: B Main Contribution and Outlinementioning

confidence: 99%

Optimization based coordination of autonomous vehicles in confined areas

Kojchev

Hult

Fredriksson

2022

2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)

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Confined areas present an opportunity for early deployment of autonomous vehicles (AV) due to the absence of non-controlled traffic participants. In this paper, we present an approach for coordination of multiple AVs in confined sites. The method computes speed-profiles for the AVs such that collisions are avoided in cross-intersection and merge crossings. Specifically, this is done through the solution of an optimal control problem where the motion of all vehicles is optimized jointly. The order in which the vehicles pass the crossings is determined through the solution of a Mixed Integer Quadratic Program (MIQP). Through simulation results, we demonstrate the capability of the algorithm in terms of performance and satisfaction of collision avoidance constraints.

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Section: B Main Contribution and Outlinementioning

confidence: 99%

Optimization based coordination of autonomous vehicles in confined areas

Kojchev

Hult

Fredriksson

2022

2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)

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“…There exist several numerical approaches such as ADMM [9], sequential convex programmings [12], and so on. However, those methods focus on getting locally optimal solutions sacrificing optimality.…”

Section: Introductionmentioning

confidence: 99%

Learning Near-global-optimal Strategies for Hybrid Non-convex Model Predictive Control of Single Rigid Body Locomotion

Li¹,

Xing²,

Schperberg³

et al. 2022

Preprint

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Convex model predictive controls (MPCs) with a single rigid body model have demonstrated strong performance on real legged robots. However, convex MPCs are limited by their assumptions such as small rotation angle and pre-defined gait, limiting the richness of potential solutions. We remove those assumptions and solve the complete mixed-integer nonconvex programming with single rigid body model. We first collect datasets of pre-solved problems offline, then learn the problem-solution map to solve this optimization fast for MPC. If warm-starts can be found, offline problems can be solved close to the global optimality. The proposed controller is tested by generating various gaits and behaviors depending on the initial conditions. Hardware test demonstrates online gait generation and adaptation running at more than 50 Hz based on sensor feedback.

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