Liu Yebao, An Tianjiao, Chen Jianguo, Zhong Luyang, Qian Yuhan
Aerospace Times Feihong Technology Company Limited, Beijing 130012, China.
Department of Control Science and Engineering, Changchun University of Technology, Changchun 130012, China.
Sensors (Basel). 2025 Jan 7;25(2):314. doi: 10.3390/s25020314.
Decreasing the position error and control torque is important for the coordinate control of a modular unmanned system with less communication burden between the sensor and the actuator. Therefore, this paper proposes event-trigger reinforcement learning (ETRL)-based coordinate control of a modular unmanned system (MUS) via the nonzero-sum game (NZSG) strategy. The dynamic model of the MUS is established via joint torque feedback (JTF) technology. Based on the NZSG strategy, the existing coordinate control problem is transformed into an RL issue. With the help of the ET mechanism, the periodic communication mechanism of the system is avoided. The ET-critic neural network (NN) is used to approximate the performance index function, thus obtaining the ETRL coordinate control policy. The stability of the closed-loop system is verified via Lyapunov's theorem. Experiment results demonstrate the validity of the proposed method. The experimental results show that the proposed method reduces the position error by 30% and control torque by 10% compared with the existing control methods.
对于传感器与执行器之间通信负担较小的模块化无人系统的坐标控制而言，降低位置误差和控制转矩至关重要。因此，本文提出了一种基于事件触发强化学习（ETRL）的模块化无人系统（MUS）坐标控制方法，该方法采用非零和博弈（NZSG）策略。通过关节转矩反馈（JTF）技术建立了MUS的动态模型。基于NZSG策略，将现有的坐标控制问题转化为强化学习问题。借助事件触发（ET）机制，避免了系统的周期性通信机制。利用ET-评论家神经网络（NN）逼近性能指标函数，从而获得ETRL坐标控制策略。通过李雅普诺夫定理验证了闭环系统的稳定性。实验结果证明了所提方法的有效性。实验结果表明，与现有控制方法相比，所提方法将位置误差降低了30%，控制转矩降低了10%。
