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Automated particle swarm optimization based PID tuning for control of robotic arm

机译：基于自动粒子群优化的PID整定控制机械臂

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摘要

In this paper, we revisit the Proportional-Integral-Derivative (PID) controller design for torque control of robotic manipulators, for which, appropriate tuning of the said controller could prove very burdensome, especially with increasing degrees-of-freedom (DOF) and/or when designing a Multi-Input Multi-Output (MIMO) PID controller. That is, when generating and tuning matrix P-I-D gains as opposed to single values, in order to take in account possible coupling effects between involved joints. We tackle both joint space and workspace PID control tuning problems for reference tracking from an optimization standpoint. Using a previously developed stable Adaptive Particle Swarm Optimizer, we are able to automatically and systematically tune P-I-D gains, be it as single gain values or gain matrices, while optimizing a cost or fitness function. The aforesaid cost function can be arranged to feature various aggregated performance measures, `normalized' so as to overcome differences in scale if any. Taking in account some practical limitations, a 2-DOF arm is used here as a case study. Numerical simulations are provided to substantiate the adequacy of our method.
机译：在本文中，我们将重新讨论用于机器人操纵器转矩控制的比例积分微分（PID）控制器设计，为此，对该控制器进行适当的调整可能会非常繁重，尤其是随着自由度（DOF）的增加和/或在设计多输入多输出（MIMO）PID控制器时。也就是说，当生成和调整矩阵P-I-D增益而不是单个值时，为了考虑到相关关节之间可能的耦合效应。我们从优化角度解决关节空间和工作空间PID控制的调节问题，以进行参考跟踪。使用以前开发的稳定的自适应粒子群优化器，我们可以自动，系统地调整P-I-D增益（无论是单个增益值还是增益矩阵），同时优化成本或适应度函数。可以将上述成本函数安排为具有“标准化”的各种综合绩效指标，以克服规模差异（如果有）。考虑到一些实际限制，此处以2-DOF臂为例进行研究。提供数值模拟以证实我们方法的适当性。

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