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An Improved Neural Network Algorithm to Efficiently Track Various Trajectories of Robot Manipulator Arms

机译:一种改进的神经网络算法,以便有效地跟踪机器人操纵器臂的各种轨迹

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The tuning of the robot actuator represents many challenges to follow a predefined trajectory on account of the uncertainties of parameters and the model nonlinearity. Furthermore, the controller gains require proper optimization to achieve good performance. In this paper, the use of a modified neural network algorithm (MNNA) is proposed as a novel adaptive tuning algorithm to optimize the controller gains. Furthermore, a new mathematical modulation is introduced to promote the exploration manner of the NNA without initial parameters. Specifically, the modulation is formed by using a polynomial mutation. The proposed algorithm is applied to select the proportional integral derivative (PID) controller gains of a robot manipulator arms in lieu of conventional procedures of designer expertise. Another vital contribution is formulating a new performance index that guarantees to improve the settling time and the overshoot of every arm output simultaneously. The proposed algorithm is evaluated with different intelligent techniques in the literature, including the genetic algorithm (GA) and the cuckoo search algorithm (CSA) with PID controllers, where its superiority to follow various trajectories is demonstrated. To affirm the robustness and efficiency of the proposed algorithm, several trajectories and uncertainties of parameters are considered for assessing the response of a robotic manipulator.
机译:机器人执行器的调谐表示由于参数和模型非线性的不确定性而遵循预定义轨迹的许多挑战。此外,控制器增益需要适当的优化以实现良好的性能。本文提出了使用改进的神经网络算法(MNNA)作为新型自适应调谐算法,以优化控制器增益。此外,引入了一种新的数学调制以促进没有初始参数的NNA的探测方式。具体地,通过使用多项式突变形成调制。所提出的算法应用于选择机器人操纵器臂的比例积分衍生(PID)控制器增益,代替设计师专业知识的传统程序。另一个重要贡献制定了一种新的性能指标,可确保同时提高每个臂输出的稳定时间和过冲。在文献中的不同智能技术评估所提出的算法,包括遗传算法(GA)和CUCKOO搜索算法(CSA),其中PID控制器,其优越性地进行了遵循各种轨迹。为了确认所提出的算法的鲁棒性和效率,考虑了评估机器人操纵器的响应的几个轨迹和参数的不确定性。

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