Distributed Dynamic Average Consensus for Nonholonomic Mobile Robots via Game-Theoretic Nash Equilibrium Approach
Abstract: This paper proposes a distributed algorithm to achieve dynamic average consensus for multiple nonholonomic mobile robot systems, which is critical for practical applications such as enemy capture, island defense, and escorting, among others. Unlike most existing studies on dynamic average consensus, this approach requires both the formation of the desired shape and the optimization of a global distance function for multiple nonholonomic mobile robot systems. By formulating the problem using a game-based approach, the dynamic average consensus control issue is converted into a least-norm Nash equilibrium seeking problem. A distributed Nash equilibrium seeking strategy is proposed by integrating adaptive control laws with regularization-based optimization algorithms. The adaptive control laws are designed to handle unknown parameters within the multiple nonholonomic mobile robot systems, while the regularization-based optimization algorithms are utilized to achieve both formation and distance optimization simultaneously. The proposed algorithm is proven to globally asymptotically direct the states of multiple nonholonomic mobile robots to the least-norm Nash equilibrium, thereby achieving dynamic average consensus control. Finally, the effectiveness of the proposed algorithm is demonstrated through simulation studies.
Submission Number: 238
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