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Consensus control for heterogeneous uncertain multi-agent systems with hybrid nonlinear dynamics via iterative learning algorithm
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DOI：10.1007/s11431-023-2411-2.png)
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In this study, We propose a compensated distributed adaptive learning algorithm for heterogeneous multi-agent systems with repetitive motion, where the leader's dynamics are unknown, and the controlled system's parameters are uncertain. The multi-agent systems are considered a kind of hybrid order nonlinear systems, which relaxes the strict requirement that all agents are of the same order in some existing work. For theoretical analyses, we design a composite energy function with virtual gain parameters to reduce the restriction that the controller gain depends on global information. Considering the stability of the controller, we introduce a smooth continuous function to improve the piecewise controller to avoid possible chattering. Theoretical analyses prove the convergence of the presented algorithm, and simulation experiments verify the effectiveness of the algorithm.
Keyword:
multi-agent systems
adaptive iterative learning control
hybrid nonlinear dynamics
composite energy function
consensus algorithm
期刊
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4.9
论文数:
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被引数:
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