School of Automation, Northwestern Polytechnical University, Shaanxi, Xi'an, PR China.
Department of Electrical-Electronics Engineering, Adana Alparslan Turkes Science and Technology University, Adana, Türkiye.
PLoS One. 2023 Jan 18;18(1):e0279253. doi: 10.1371/journal.pone.0279253. eCollection 2023.
High precision demands in a large number of emerging robotic applications strengthened the role of the modern control laws in the position control of the Permanent Magnet Synchronous Motor (PMSM) servo system. This paper proposes a learning-based adaptive control approach to improve the PMSM position tracking in the presence of the friction uncertainty. In contrast to most of the reported works considering the servos operating at high speeds, this paper focuses on low speeds in which the friction stemmed deteriorations become more obvious. In this paper firstly, a servo model involving the Stribeck friction dynamics is formulated, and the unknown friction parameters are identified by a genetic algorithm from the offline data. Then, a feedforward controller is designed to inject the friction information into the loop and eliminate it before causing performance degradations. Since the friction is a kind of disturbance and leads to uncertainties having time-varying characters, an Adaptive Proportional Derivative (APD) type Iterative Learning Controller (ILC) named as the APD-ILC is designed to mitigate the friction effects. Finally, the proposed control approach is simulated in MATLAB/Simulink environment and it is compared with the conventional Proportional Integral Derivative (PID) controller, Proportional ILC (P-ILC), and Proportional Derivative ILC (PD-ILC) algorithms. The results confirm that the proposed APD-ILC significantly lessens the effects of the friction and thus noticeably improves the control performance in the low speeds of the PMSM.
在大量新兴机器人应用中对高精度的需求增强了现代控制律在永磁同步电机（PMSM）伺服系统位置控制中的作用。本文提出了一种基于学习的自适应控制方法，以提高 PMSM 在存在摩擦不确定性时的位置跟踪性能。与大多数考虑高速运行的伺服系统的报告工作相比，本文侧重于低速情况，其中摩擦引起的恶化变得更加明显。本文首先建立了一个包含斯特里贝克摩擦动力学的伺服模型，并通过遗传算法从离线数据中识别未知的摩擦参数。然后，设计了一个前馈控制器，将摩擦信息注入到回路中，并在性能下降之前将其消除。由于摩擦是一种干扰，会导致具有时变特性的不确定性，因此设计了一种自适应比例微分（APD）型迭代学习控制器（ILC），称为 APD-ILC，以减轻摩擦的影响。最后，在 MATLAB/Simulink 环境中对所提出的控制方法进行了仿真，并与传统的比例积分微分（PID）控制器、比例迭代学习控制器（P-ILC）和比例微分迭代学习控制器（PD-ILC）算法进行了比较。结果表明，所提出的 APD-ILC 显著减轻了摩擦的影响，从而显著提高了 PMSM 低速时的控制性能。
