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首页> 外文期刊>Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on >Tracking Control of a Closed-Chain Five-Bar Robot With Two Degrees of Freedom by Integration of an Approximation-Based Approach and Mechanical Design
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Tracking Control of a Closed-Chain Five-Bar Robot With Two Degrees of Freedom by Integration of an Approximation-Based Approach and Mechanical Design

机译：基于近似的方法与机械设计相结合的具有两个自由度的闭合链五杆机器人跟踪控制

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摘要

The trajectory tracking problem of a closed-chain five-bar robot is studied in this paper. Based on an error transformation function and the backstepping technique, an approximation-based tracking algorithm is proposed, which can guarantee the control performance of the robotic system in both the stable and transient phases. In particular, the overshoot, settling time, and final tracking error of the robotic system can be all adjusted by properly setting the parameters in the error transformation function. The radial basis function neural network (RBFNN) is used to compensate the complicated nonlinear terms in the closed-loop dynamics of the robotic system. The approximation error of the RBFNN is only required to be bounded, which simplifies the initial “trail-and-error” configuration of the neural network. Illustrative examples are given to verify the theoretical analysis and illustrate the effectiveness of the proposed algorithm. Finally, it is also shown that the proposed approximation-based controller can be simplified by a smart mechanical design of the closed-chain robot, which demonstrates the promise of the integrated design and control philosophy.
机译：研究了闭链五杆机器人的轨迹跟踪问题。基于误差变换函数和反推技术，提出了一种基于近似的跟踪算法，该算法可以保证机器人系统在稳定和瞬态阶段的控制性能。特别是，可以通过正确设置错误转换功能中的参数来调整机器人系统的过冲，建立时间和最终跟踪错误。径向基函数神经网络（RBFNN）用于补偿机器人系统闭环动力学中的复杂非线性项。仅需要限制RBFNN的近似误差，这可以简化神经网络的初始“跟踪和误差”配置。给出了说明性的例子来验证理论分析并说明所提出算法的有效性。最后，还表明通过闭链机器人的智能机械设计可以简化所提出​​的基于逼近的控制器，这证明了集成设计和控制理念的希望。

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