Tracking Control Method of Multi Motor Actuator Saturation Based on Total Amount Consistency

IF 0.7 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Lin Jia, Junming Zhang, Changfan Zhang, Jing He
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引用次数: 0

Abstract

In collaborative control of multi motors for heavy-duty locomotives, the output value of the motor frequently exceeds its maximum allowable value during power redistribution. This results in a saturated motor owing to the power redundancy of each wheel set of the train. In this study, an algorithm for the tracking control of a consistent total amount of the extended observer through anti-saturation is proposed. First, mathematical models of multi-motor traction systems are developed. The system includes uncertain parameter perturbations and external perturbations. Second, a new type of extended sliding mode observer (ESMO) is designed to reduce the influence of the tracking effect on the input saturation of the system. Subsequently, for collaborative control of multi motors in heavy-duty locomotives, a new scheme for dynamic and auxiliary anti-saturation compensation (anti-windup) is established. The perturbation observation results and the systems’ auxiliary status are respectively input into the sliding mode controller (SMC). A traction total-amount coordinated tracking control (TACTC) of multi motors is achieved to ensure consistency of the system’s total output torque and the given traction characteristic curve. Finally, simulations and tests are performed on the motor actuators to demonstrate a good control effect.
基于总量一致性的多电机执行器饱和跟踪控制方法
在重型机车多电机协同控制中,在功率分配过程中,电机的输出功率经常超过其最大允许值。由于列车的每个轮对的功率冗余,这导致电机饱和。本文提出了一种通过抗饱和实现扩展观测器总量一致的跟踪控制算法。首先,建立了多电机牵引系统的数学模型。该系统包括不确定参数摄动和外部摄动。其次,设计了一种新型的扩展滑模观测器(ESMO),以减小跟踪效应对系统输入饱和的影响。随后,针对重型机车多电机协同控制问题,提出了一种动态辅助抗饱和补偿方案。将扰动观测结果和系统辅助状态分别输入滑模控制器(SMC)。为了保证系统的总输出转矩与给定的牵引特性曲线的一致性,实现了多电机牵引总量协调跟踪控制。最后,对电机执行器进行了仿真和试验,验证了控制效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
1.50
自引率
14.30%
发文量
89
期刊介绍: JACIII focuses on advanced computational intelligence and intelligent informatics. The topics include, but are not limited to; Fuzzy logic, Fuzzy control, Neural Networks, GA and Evolutionary Computation, Hybrid Systems, Adaptation and Learning Systems, Distributed Intelligent Systems, Network systems, Multi-media, Human interface, Biologically inspired evolutionary systems, Artificial life, Chaos, Complex systems, Fractals, Robotics, Medical applications, Pattern recognition, Virtual reality, Wavelet analysis, Scientific applications, Industrial applications, and Artistic applications.
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