Start-up current adaptive control for sensorless high-speed brushless DC motors based on inverse system method and internal mode controller

He, Yu‐Ling; Zheng, Shiqiang; Fang, Jiancheng

doi:10.1016/j.cja.2016.12.006

Cited by 20 publications

(8 citation statements)

References 33 publications

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“…In addition, the remaining negative sequence component of the HF current will be processed by the position tracking observer, where the rotor position/speed signal is estimated. The low pass filter (LPF) is used in the inner loop to filter the HF signal in the feedback current (He et al , 2017). Automatic speed regulator (ASR) and automatic current regulator (ACR) are used in the speed control loop and current control loop, respectively.…”

Section: Position Estimation In S-l Speed Operation Based On High Frequency Signal Injection Methodsmentioning

confidence: 99%

A novel hybrid control approach for modular automation system: a case study of sensorless interior permanent magnet in-wheel motor

Huang

2019

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Purpose Sensorless interior permanent magnet in-wheel motor (IPMIWM), as an exemplar of modular automation system, has attracted considerable interests in recent years. This paper aims to investigate a novel hybrid control approach for the sensorless IPMIWM from a cyber-physical systems (CPS) perspective. Design/methodology/approach The control approach is presented based on the hybrid dynamical theory. In the standstill-low (S-L) speed, the rotor position/speed signal is estimated by the method of the high frequency (HF) voltage signal injection. The least square support vector machine (LS-SVM) is used to acquire the rotor position/speed signal in medium-high (M-H) speed operation. Hybrid automata model of the IPMIWM is established due to its hybrid dynamic characteristics in wide speed range. A hybrid state observer (HSO), including a discrete state observer (DSO) and a continuous state observer (CSO), is designed for rotor position/speed estimation of the IPMIWM. Findings The hardware-in-the-loop testing based on dSPACE is carried out on the test bench. Experimental investigations demonstrate the hybrid control approach can not only identify the rotor position/speed signal with a certain load but also be able to reject the load disturbance. The reliability and the effectiveness of the proposed hybrid control approach were verified. Originality/value The proposed hybrid control approach for the sensorless IPMIWM promotes the deep combination and coordination of sensorless IPMIWM drive system. It also theoretically supports and extends the development of the hybrid control of the highly integrated modular automation system.

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Section: Position Estimation In S-l Speed Operation Based On High Frequency Signal Injection Methodsmentioning

confidence: 99%

A novel hybrid control approach for modular automation system: a case study of sensorless interior permanent magnet in-wheel motor

Huang

2019

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“…The IMC has good abilities in tracking, rejecting disturbance, and enhancing the robustness. The IMC method was originally employed to process a control system and then extended to the motor control system, which relies on the "internal model" principle including a model of the plant [46,47]. It should be pointed out that the conventional IMC method could provide an adequate suppressing ability for the disturbances added to the output channel.…”

Section: Bpnni-based Imc Designmentioning

confidence: 99%

Bulletin of the Polish Academy of Sciences: Technical Sciences

Zhang

2018

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The permanent magnet in-wheel motor (PMIWM) is a nonlinear, multivariable, strongly coupled and highly complex system. The key to the development and application of the PMIWM consists in the improvement of its control accuracy and dynamic performance. In order to effectively decouple the PMIWM, this paper presents a novel internal model control (IMC) approach based on the back-propagation neural network inverse (BPNNI) control method. First, theoretical analysis is conducted to show the existence of the PMIWM inverse system, to be modeled mathematically. The inverse system approximated and identified by the back-propagation neural network (BPNN) constitutes the back-propagation neural network inverse (BPNNI) system. Then, by cascading the BPNNI system on the left side of the original PMIWM system, a new decoupling, pseudo-linear system is established. Moreover, the 2-DOF internal model control (IMC) method is employed to design the extra closed-loop controller that further improves disturbance rejection and robustness of the whole system. Consequently, the proposed decoupling control approach incorporates the advantages of both the BPNNI and the IMC. Effectiveness of thus proposed control approach is verified by means of simulation and real-time hardware-in-the-loop (HIL) experiments.

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“…11 The two-degrees-of-freedom internal model control (TDF-IMC) can overcome this shortcoming by adding a controller to the IMC. He et al 12 proposed an additional closed-loop feedback control strategy based on TDF-IMC for starting current control of high-speed brushless DC motor, which was proved by simulation to have good current tracking performance. Chekari et al 13 designed a TDF internal mode proportional–integral–derivative (PID) multi-loop controller for the coupled tank model and verified its tracking and disturbance rejection through simulation.…”

Section: Introductionmentioning

confidence: 99%

Two-degree-of-freedom internal model control of particle swarm optimization for flow temperature control system in phase change material packaging technology

Zhang

Duan

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part I:

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In this article, a phase change material packaging process system was designed. In view of the hysteresis of the system flow temperature control, a two-degree-of-freedom internal model PID (PSO-TDF-IMC-PID) controller based on particle swarm optimization was determined based on the flow regulating valve model, so as to control the phase change material outlet temperature to keep constant with establishing the system heat transfer model. Compared with other control algorithms, the PSO-TDF-IMC-PID algorithm is proved to be fast, anti-interference and robust. The traditional integral of time multiplied by the absolute value of error criterion performance index is improved, and the improved integral of time multiplied by the absolute value of error criterion is applied to PSO-TDF-IMC-PID algorithm for simulation comparison. Simulation results show that the improved integral of time multiplied by the absolute value of error criterion can effectively eliminate overshoot. The overshoot is reduced from the original 8% to almost 0. To reduce system energy consumption and improve economy, energy consumption constraint conditions are added into the variable trial-error algorithm to calculate economic flow rate and inlet temperature of heat conduction oil. The results show that the dynamic parameters of the system accord with the constraint condition of energy consumption.

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Start-up current adaptive control for sensorless high-speed brushless DC motors based on inverse system method and internal mode controller

Cited by 20 publications

References 33 publications

A novel hybrid control approach for modular automation system: a case study of sensorless interior permanent magnet in-wheel motor

A novel hybrid control approach for modular automation system: a case study of sensorless interior permanent magnet in-wheel motor

Bulletin of the Polish Academy of Sciences: Technical Sciences

Two-degree-of-freedom internal model control of particle swarm optimization for flow temperature control system in phase change material packaging technology

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