Formation Control Design and Optimization for Uncertain Underactuated Robots Team Using Cooperative Game Theory

ABSTRACT This manuscript introduces an innovative approach to address the formation control challenges for a specific category of underactuated robots (UARs). It transforms the formation control problem into a constraint‐following problem with three basic constraints: (1) The configuration constraints (CCs), which are to induce behaviors for each UAR. (2) The formation constraints (FCs), which are used to maintain the team formation. (3) The trajectory constraints (TCs), which are imposed to make the team move with a predefined formation. The UAR team is assumed to suffer from time‐varying while bounded uncertainty, and the uncertainty bound is unknown. Invoking an interval method, we build a dynamic model for the uncertain UAR team. Considering the three basic constraints in a unified form, we then employ an improved adaptive formation controller based on an extended Udwadia‐control (UC) scheme for the uncertain UAR team. The controller can guarantee the UAR team deterministic convergence, which is verified by an explicit Lyapunov analysis. As there are three tunable control gains in the proposed controller, this paper adopts a cooperative game (CG) algorithm to obtain the optimal formation control. Regarding the tunable parameters as three players in a CG with their cost functions representing the tracking performance or the energy cost, the optimal formation control is realized by achieving an equilibrium among players. Simulations on a UAR team consisting of three inverted pendulum robots (IPRs) are performed to test the designed optimal formation controller.

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Publication Details

Journal
International Journal of Adaptive Control and Signal Processing
Published
2025-12-07
DOI
https://doi.org/10.1002/acs.70019
Primary Topic
Distributed Control Multi-Agent Systems
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
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article

Formation Control Design and Optimization for Uncertain Underactuated Robots Team Using Cooperative Game Theory

Ke Shao, Jian Zhao, Chunsheng He
International Journal of Adaptive Control and Signal Processing
Distributed Control Multi-Agent Systems
article

Formation Control Design and Optimization for Uncertain Underactuated Robots Team Using Cooperative Game Theory

Ke Shao, Jian Zhao, Chunsheng He
article en

Abstract

ABSTRACT This manuscript introduces an innovative approach to address the formation control challenges for a specific category of underactuated robots (UARs). It transforms the formation control problem into a constraint‐following problem with three basic constraints: (1) The configuration constraints (CCs), which are to induce behaviors for each UAR. (2) The formation constraints (FCs), which are used to maintain the team formation. (3) The trajectory constraints (TCs), which are imposed to make the team move with a predefined formation. The UAR team is assumed to suffer from time‐varying while bounded uncertainty, and the uncertainty bound is unknown. Invoking an interval method, we build a dynamic model for the uncertain UAR team. Considering the three basic constraints in a unified form, we then employ an improved adaptive formation controller based on an extended Udwadia‐control (UC) scheme for the uncertain UAR team. The controller can guarantee the UAR team deterministic convergence, which is verified by an explicit Lyapunov analysis. As there are three tunable control gains in the proposed controller, this paper adopts a cooperative game (CG) algorithm to obtain the optimal formation control. Regarding the tunable parameters as three players in a CG with their cost functions representing the tracking performance or the energy cost, the optimal formation control is realized by achieving an equilibrium among players. Simulations on a UAR team consisting of three inverted pendulum robots (IPRs) are performed to test the designed optimal formation controller.

International Journal of Adaptive Control and Signal ProcessingVol. 40(3)
Northwestern Polytechnical University (CN), Suzhou University of Science and Technology (CN), Yangtze River Delta Physics Research Center (China) (CN), Suzhou Vocational Health College (CN)
Science and Technology Program of Suzhou
Openalex Percentile: Top 56%
Distributed Control Multi-Agent Systems
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