摘要
This article presents a robust optimal consensuscontrol strategy for switched nonlinear multi-agent systems(MASs). The robust control problem with unmatcheduncertainties is first reformulated as an optimal controlframework through the introduction of augmented control inputsand a tailored cost function. To construct a distributed integralsliding-mode control (ISMC), a novel measurement error term isdesigned to enforce finite-time convergence of the consensus errorto the origin. An event-based mechanism is incorporated tominimize controller execution, thereby optimizing computationaland communication resource utilization. A concurrent learningapproach is proposed to update neural network (NN) weights,where the value function is approximated by a critic NN. Thismethod circumvents the constraints of initial admissible controland persistent excitation (PE) conditions inherent in traditionaladaptive dynamic programming (ADP) designs. Theoreticalanalysis demonstrates that the developed event-triggered ADPcontroller guarantees system robustness for nonlinear MASs andensures the uniform ultimate boundedness (UUB) of critic NNweight estimation errors. Finally, simulation results are presentedto validate the effectiveness of the proposed control methodology.
基金
supported by the National Key R&D Program of China(No.2024YFB4709100)
the National Natural Science Foundation of China(No.62425310)
the National Defense Basic Scientific Research Program(Nos.JCKY2019203C029 and JCKY2020130C025)
the Science and Technology Innovation Project of China Academy of Chinese Medical Sciences(No.CI2023C005YG)
the Science and Technology Development Fund,Macao SAR(Nos.FDCT-22-009-MISE,0060/2021/A2,and 0015/2020/AMJ).
