A Stackelberg-game approach for tracking problems of flexible robots

Jank, Gerhard; Kremer, Dirk; Kun, Gábor; Polzer, Jan; Scholt, T.

doi:10.23919/ecc.2001.7075908

Cited by 3 publications

(1 citation statement)

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“…It is evident that up to now, work on Stackelberg games has focused on offline matrix computations that do not allow the systems and the players to achieve real‐time gaming capabilities. Jank et al introduced a robot motion controller based on a Stackelberg game‐theoretic approach and show improved performance through experiments. Static infinite Stackelberg games for resilient control against an intelligent attacker in the cyber and physical layers is developed in the work of Yuan et al…”

Section: Introductionmentioning

confidence: 99%

Open‐loop Stackelberg learning solution for hierarchical control problems

Vamvoudakis

Lewis²,

Dixon

2017

Adaptive Control & Signal

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This work presents a novel framework based on adaptive learning techniques to solve the continuous-time open-loop Stackelberg games. The method yields real-time approximations of the game value and convergence of the policies to the open-loop Stackelberg-equilibrium solution, while also guaranteeing asymptotic stability of the equilibrium point of the closed-loop system. It is implemented as a separate actor/critic parametric network approximator structure for every player and involves simultaneous continuous-time adaptation. To introduce and implement the hierarchical structure to the coupled optimization problem, we adjoin to the leader the controller dynamics of the follower. A persistence of excitation condition guarantees convergence of both critics to the actual game values that eventually solve the hierarchical optimization problem. A simulation example shows the efficacy of the proposed approach. KEYWORDS leader-follower coupled Riccati equations, learning-based adaptation, noncooperative games, Stackelberg equilibrium Int J Adapt Control Signal Process. 2019;33:285-299.wileyonlinelibrary.com/journal/acs

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Section: Introductionmentioning

confidence: 99%