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Adaptive Optimal Consensus Control for Nonlinear Multi-Agent Systems With Quantized States and Input
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DOI：10.1109/TASE.2026.3672934.png)
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Abstract
En 中文
In this article, a new adaptive quantized consensus control strategy is proposed for uncertain nonlinear multi-agent systems. To achieve optimized consensus performance, the reinforcement learning algorithm is introduced, in which critic, actor, and identifier are utilized to value system performance, execute control behavior, and estimate unknown dynamics, respectively. Unlike existing reinforcement learning algorithms, the designed updating laws are simpler and facilitate more efficient training. In addition, the states and input of every agent are quantized before communication to alleviate bandwidth constraints. By integrating the command-filtered backstepping technique and substituting unquantized states with their quantized counterparts, the final controller is obtained. Different from existing quantized control studies, the virtual and final controllers contain both actor and critic neural networks, and a new lemma is accordingly established to compensate for the quantization errors. Note to Practitioners—In practical multi-agent systems, owing to the capacity constraints of communication channels, control signals are typically quantized. Nevertheless, most existing optimal control schemes for multi-agent systems fail to consider signal quantization. Thus, it is impractical for engineering applications. Based on reinforcement learning algorithm and command-filtered backstepping technique, a novel adaptive distributed optimized tracking control methodology for nonlinear multi-agent systems with state and input quantization is proposed in this article. This methodology ensures that the systems can attain the desired tracking performance as well as optimal performance.
Keywords:
Consensus control
reinforcement learning
state and input quantization
multi-agent systems
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