Event-Triggered Optimal Consensus Control for MASs With Multiple Constraints: A Flexible Performance Approach

This paper investigates the challenge of achieving event-triggered optimal consensus control for multiagent systems (MASs) with multiple constraints, encompassing saturation constraint at the input and performance constraint at the output. To achieve performance constraint while satisfying input saturation, a flexible prescribed performance method (FPPM) is designed. Utilizing non-negative signals generated by the improved auxiliary system to design the performance functions, the FPPM can adaptively adjust the performance constraint boundaries to ensure safe operation of the MASs with multiple constraints. Meanwhile, the proposed FPPM can achieve different performance behaviors by changing core parameters without the need to alter the control structure. Subsequently, a simplified reinforcement learning algorithm with actor-critic structure is integrated into the FPPM. By designing actor-critic neural networks and dynamic event-triggered mechanism, optimal consensus control for MASs under multiple constraint conditions is achieved cleverly while avoiding unnecessary communication transmissions. Finally, a simulation example verifies the effectiveness of the proposed method. Note to Practitioners—Considering the limitations of physical devices and the practical requirements for control performance, the input saturation constraint and performance constraint often coexist during the operation of practical systems, such as robotic systems, manipulator systems and aerospace systems. Therefore, this paper aims to design an event-triggered reinforcement learning algorithm for MASs with multiple constraints. To resolve the conflict problem caused by input saturation and performance constraint, a FPPM with adjustable performance functions is proposed. By flexibly adjusting the performance constraint boundaries, the coexistence problem of multiple constraints can be solved effectively. Meanwhile, the constructed FPPM framework can achieve various performance behaviors by adjusting parameters according to the practical application scenario without changing the controller structure. Additionally, the proposed event-triggered reinforcement learning algorithm can optimize the designed cost function and promote the utilization of communication resources.