用于电动汽车(EV)直流(DC)电机 PIηDλ 控制器数值模拟的分数排序亚当斯-巴什福斯-穆尔顿(FABM)方法

Aashima Bangia , Rashmi Bhardwaj
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引用次数: 0

摘要

通过使用分数排序的亚当斯-巴什福斯-穆尔顿(ABM)方法开发基于比例积分微分(PID)控制器的不同模拟模型,建立了直流(DC)电机速度控制模型。为了获得更有效的见解,我们构建了 PID 型控制器的一般闭环及其实现方法。PID 控制系统由监测环境不同参数所必需的规则集组成。在实际应用中,通过分数阶控制(FOC)对机制进行控制,需要能够构建控制器、调整参数以实现准确和精确监控的技术。众所周知,PID 控制器对因运动学和动力学知识不精确而产生的不确定性非常敏感,因此有人提出了自适应分数 PID (AFPID) 控制器,以利用分数阶控制器的鲁棒性。在之前的研究中,FPID 控制器参数在控制过程中保持不变,但在本研究中,这些参数将通过适当的适应机制进行在线更新,以获得更好的结果。研究结果表明,混沌现象与分数微积分之间的关系是一致的。据观察,PIηDλ 控制动态可通过增加调节旋钮来提高控制器的性能。此外,初始化和执行时间分别从 2.64 秒和 0.5 秒大幅减少到 0.87 秒和 0.15 秒。
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Fractional-ordered Adams–Bashforth–Moulton (FABM) method for PIηDλ controllers’ numerical simulations for Direct Current (DC) motors in Electric Vehicles (EVs)
The model for the speed control in the Direct Current (DC) motors by developing different simulating models based upon Proportional Integral Derivative (PID) controllers with fractional-ordered Adams–Bashforth–Moulton (ABM) method has been developed. With the aim of more efficient insights, a general closed loop in PID type controllers have been constructed alongwith their implementation. PID control system consists of rule-set essential to monitor the different parameters of the environment. The control of mechanisms through Fractional-order controls (FOC) in real life applications require techniques that would build controllers; tune parameters for accurate and precise monitoring. It is known that PID controllers are sensitive to uncertainties which arise from imprecise knowledge of the kinematics and dynamics therefore an adaptive fractional PID (AFPID) controller has been proposed to use the robustness of fractional-ordered controller. In previous works, FPID controller parameters are constant during control process but in this study these parameters will be updated online with an adequate adaptation mechanism to have better results. Outcomes found to be consistent between represent a step towards understanding the relation between chaotic phenomena and fractional calculus. It has been observed that the PIηDλ control dynamics can boost the controllers’ performance by increase of tuning knobs. In addition, the initialization and execution time have decreased substantially from 2.64 to 0.87 secs and 0.5 to 0.15 secs.
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来源期刊
Results in Control and Optimization
Results in Control and Optimization Mathematics-Control and Optimization
CiteScore
3.00
自引率
0.00%
发文量
51
审稿时长
91 days
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