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首页> 外文会议>International Conference on Information Science and Technology >Critic-Identifier Structure-Based ADP for Decentralized Robust Optimal Control of Modular Robot Manipulators
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Critic-Identifier Structure-Based ADP for Decentralized Robust Optimal Control of Modular Robot Manipulators

机译：基于批评者的结构基于模块化机器人操纵器的分散强大的最佳控制的基于结构的ADP

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摘要

This paper presents a decentralized robust optimal control method for modular robot manipulators (MRMs) via a novel critic-identifier (CI) structure-based adaptive dynamic programming (ADP) scheme. The robust control problem of MRMs is transformed into an optimal compensation control approach, which combines model-based compensation control, identifier-based learning control and ADP-based optimal control. The dynamic model of MRMs is formulated based on a torque sensing technique that is deployed for each joint module, where the local dynamic information is utilized effectively to design the model compensation controller. A neural network (NN) identifier is established to approximate the dynamics of the interconnected dynamic coupling (IDC). Based on the ADP algorithm, the Hamiltonian-Jacobi-Bellman (HJB) equation can be solved by constructing a critic NN, and the approximate optimal control policy is derived. The closed-loop robotic system is guaranteed to be asymptotic stable by the implementation of a set of decentralized control policies that have been developed. Finally, simulations verify the effectiveness of the proposed method.
机译：本文介绍了通过新型批评标识符（CI）结构的自适应动态编程（ADP）方案的模块化机器人机械手（MRMS）分散的鲁棒最优控制方法。 MRM的鲁棒控制问题被转换为最佳补偿控制方法，其结合了基于模型的补偿控制，基于标识符的学习控制和基于ADP的最优控制。 MRMS的动态模型基于为每个接合模块部署的扭矩传感技术配制，其中局部动态信息有效地设计模型补偿控制器。建立神经网络（NN）标识符以近似于互连的动态耦合（IDC）的动态。基于ADP算法，通过构建评论家NN来解决Hamiltonian-jacobi-Bellman（HJB）方程，并且导出了近似的最佳控制策略。通过实施已经开发的一套分散控制策略，闭环机器人系统保证是渐近稳定的。最后，仿真验证了该方法的有效性。

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