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Event-triggered distributed H∞ control of physically interconnected mobile Euler–Lagrange systems with slipping, skidding and dead zone

机译:具有滑移,滑移和死区的物理互连移动欧拉-拉格朗日系统的事件触发分布式H∞控制

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摘要

This study addresses an event-triggered distributed H-infinity control method by extending traditional zero-sum differential games for physically interconnected non-holonomic mobile mechanical multi-agent systems with external disturbance and slipping, skidding and dead-zone disturbances. Initially, a problem of physically interconnected kinematic and dynamic control is transformed into an equivalent problem of event-triggered distributed H-infinity control. Subsequently, the traditional two-player zero-sum differential game is extended to a three-player zero-sum differential game, where a new player is included to approximate the worst dead-zone disturbance. To find player policies, an event-triggering condition and an event-triggered control law are proposed via neural networks (NNs). Although an NN weight-tuning law is designed on the basis of adaptive dynamic programming techniques, it can relax identification procedures for unknown drift dynamics and persistent excitation conditions. It also guarantees that the closed system is stable and the cost function converges to the bounded L-2-gain optimal value, while the Zeno behaviour is excluded. Finally, the effectiveness of the proposed method is verified by an application to a dead-zone torque multi-mobile robot system through numerical simulations.
机译:这项研究通过扩展传统的零和差分博弈来解决事件触发的分布式H无穷大控制方法,该博弈适用于具有外部干扰以及滑移,打滑和死区干扰的物理互连非完整移动机械多主体系统。最初,将物理互连的运动学和动态控制问题转化为事件触发的分布式H无限控制的等效问题。随后,传统的两人零和差动游戏被扩展为三人零和差动游戏,其中包括了一个新玩家,以近似最坏的死区干扰。为了找到参与者策略,通过神经网络(NN)提出了一个事件触发条件和一个事件触发控制律。尽管基于自适应动态编程技术设计了NN权重调整定律,但它可以放宽针对未知漂移动力学和持续激励条件的识别程序。它还保证了封闭系统是稳定的,成本函数收敛于有界的L-2-增益最优值,而芝诺行为被排除在外。最后,通过数值仿真,将所提出的方法应用于死区扭矩多移动机器人系统,验证了所提方法的有效性。

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