Sun Youbo, Zhao Tao, Liu Nian
College of Electrical Engineering, Sichuan University, Chengdu 610065, China.
Entropy (Basel). 2023 May 12;25(5):789. doi: 10.3390/e25050789.
In order to solve the high-precision motion control problem of the n-degree-of-freedom (n-DOF) manipulator driven by large amount of real-time data, a motion control algorithm based on self-organizing interval type-2 fuzzy neural network error compensation (SOT2-FNNEC) is proposed. The proposed control framework can effectively suppress various types of interference such as base jitter, signal interference, time delay, etc., during the movement of the manipulator. The fuzzy neural network structure and self-organization method are used to realize the online self-organization of fuzzy rules based on control data. The stability of the closed-loop control systems are proved by Lyapunov stability theory. Simulations show that the algorithm is superior to a self-organizing fuzzy error compensation network and conventional sliding mode variable structure control methods in control performance.
为解决由大量实时数据驱动的n自由度（n-DOF）机械手的高精度运动控制问题，提出了一种基于自组织区间二型模糊神经网络误差补偿（SOT2-FNNEC）的运动控制算法。所提出的控制框架能够有效抑制机械手运动过程中的各种干扰，如基座抖动、信号干扰、时间延迟等。利用模糊神经网络结构和自组织方法，基于控制数据实现模糊规则的在线自组织。通过李雅普诺夫稳定性理论证明了闭环控制系统的稳定性。仿真结果表明，该算法在控制性能上优于自组织模糊误差补偿网络和传统滑模变结构控制方法。
