Fault-Tolerant H _∞ Output Regulation of Uncertainty Multi-Agent Systems via Anti-Saturation Policy Learning

This paper addresses the fault-tolerant <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</i><sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> output regulation problem of multi-agent systems (MASs) subject to input saturation, structural uncertainties, and actuator faults. The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</i><sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> output regulation problem is reformulated as a distributed two-step zero-sum game problem to enhance both steady-state and transient performance in the presence of disturbances. A novel anti-saturation reinforcement learning algorithm with a feedforward-feedback structure is proposed, enabling saturation-free optimal output regulation while effectively mitigating both modeled and unmodeled disturbances. An active fault-tolerant control (FTC) approach based on the anti-saturation policy algorithm is also introduced to compensate for actuator faults and structural uncertainty. The salient feature of the proposed algorithm is its ability to prevent saturation during optimal control policy learning, while improving both steady-state and transient performance. Finally, simulation studies are conducted to validate the effectiveness of the proposed approach.