Fractional-order sliding mode control for a bearingless induction motor based on improved load torque observer

Yang, Zebin; Ding, Qifeng; Sun, Xiaodong; Zhu, Huimin; Lu, Chengling

doi:10.1016/j.jfranklin.2021.03.006

Cited by 30 publications

(19 citation statements)

References 21 publications

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“…The research of sliding mode control is also a hot spot, and many scholars have made good achievements in this field. [19][20][21][22][23][24] In the control research of proton exchange membrane fuel cell system, Ahmed 25 uses second-order sliding mode control to overcome the flutter phenomenon caused by traditional sliding mode control. In the research of networked control systems, Wang et al 26 designed sliding mode control for a class of discrete networked switching systems under the condition of coded communication protocols.…”

Section: Introductionmentioning

confidence: 99%

Sliding mode control of Lorenz chaotic system of fluid thermal convection between infinite plates based on LMI

Gao

Zhang

Xiong

2023

Advances in Mechanical Engineering

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Aiming at the unpredictability and nonlinearity of the Lorenz chaotic system, this paper takes the fluid thermal convection of the infinite plate as the model, and studies the sliding mode control of the fluid thermal convection of the infinite plate based on LMI. Based on the characteristics of the Lorenz chaotic system, the sliding mode control method is applied to attenuate the effects of some nonlinear factors, and the Lyapunov function is designed to obtain the control law that satisfies the sliding mode conditions. Further, a dynamic compensation algorithm is presented, it can efficiently regulate the poles of a closed-loop system, and ensure that the specific matrix is negative definite. The simulation results show that under the action of the compensator, the dynamic perturbed Lorenz chaotic system has a good state response, which makes the sliding mode control of the Lorenz chaotic system stable.

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Section: Introductionmentioning

confidence: 99%

Sliding mode control of Lorenz chaotic system of fluid thermal convection between infinite plates based on LMI

Gao

Zhang

Xiong

2023

Advances in Mechanical Engineering

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“…This method of load shedding is usually the last method used when the power system collapses, but in some power systems, load shedding may cause some lowfrequency relays to operate abnormally, so Maresch et al proposed a high safety and reliability Sexual methods to avoid this problem, and so on. [5][6][7][8] However, there are not many researches on the intelligent control of motor load systems. It is common to use PID controllers to control them.…”

Section: Introductionmentioning

confidence: 99%

Research on a hybrid controller combining RBF neural network supervisory control and expert PID in motor load system control

Gao

Xiong

2022

Advances in Mechanical Engineering

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Considering the contradiction among the response speed, overshoot and stability of system when the motor load system adopts PID control, a control strategy combining RBF (Radial Basis Function) neural network supervisory control and expert PID control is designed to effectively improve this problem in this paper. First of all, the related algorithms of RBF neural network supervisory control composed of RBF neural network and PID control (RSC-PID) is introduced. This method can make the motor load system reach a steady state faster than simple PID control. But RSC-PID is also unsatisfactory in terms of overshoot. Based on the RSC-PID control method, a hybrid controller combining RBF neural network supervisory control and expert PID control (RSC-EPID) is proposed. This method combines RSC-PID control theory with expert PID control ideas, further improves the stability and rapidity of the system, reduces the overshoot. Moreover, when the input signal is a time-varying signal with interference, the motor load system shows better anti-interference performance after using RSC-EPID. The simulation results show that RSC-EPID control improves the tracking effect of the output signal of the motor load system, ensures the stability of the system, and improves the performance of the system.

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“…When the simulation and experimental results are examined, it is seen that FOPI controller has better dynamic response and reduces the overshoot. Yang et al (2021) proposed a fractional order sliding mode-based load torque observer for bearingless induction motor (BIM). The proposed control law is derived on a mathematical model of BIM.…”

Section: Introductionmentioning

confidence: 99%

Conformable fractional order controller design and optimization for sensorless control of induction motor

İlten

2022

COMPEL

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Purpose In recent years, use of sensorless control methods for electrical motor-based variable speed drive systems has been increasing rapidly to compensate the increasing costs in industrial systems. Also, use of induction motors is popular for a long time to decrease the cost of these industrial systems. This study aims to design an effective controller to improve the sensorless speed control performance of induction motor. To achieve this, a conformable fractional order proportional integral (CFOPI) controller is designed. Design/methodology/approach The system is modeled based on small signal analysis by using the input–output data, experimentally. To do this, system identification toolbox of Matlab is used. The proposed controller is established on conformable fractional integral approach proposed by Khalil et al. (2014). CFOPI controller coefficients are optimized using particle swarm optimization method on the created small signal-based simulation model of the system to minimize the integral time absolute error. To prove the success of the proposed method, a traditional fractional order proportional integral (TFOPI) controller is tested under the same experimental system with the CFOPI controller. Findings TFOPI and CFOPI controllers are tested with the optimum parameters. Reference and actual speed trends are obtained for both methods. In induction motor start-up test, settling-times are measured as 8.73 and 8.44 s and steady-state oscillations are 2.66% and 0% (almost) for TFOPI and CFOPI controllers, respectively. In variable referenced speed tracking test, CFOPI performs well at all speed levels, while TFOPI fails to reach the reference speed at most speed levels. Practical implications Proposed CFOPI control method can be easily implemented in industrial systems, thanks to its simple algorithm. digital signal peripheral interface controller (dsPIC) based driver circuit with designed CFOPI controller used in this study can be applied directly to industrial systems such as elevators, conveyors, cranes and drills. Moreover, it can improve the performance of induction motor-based variable speed drive systems. Originality/value The proposed method provides robust performance for induction motor used in control systems. Additionally, it does this by using less complex algorithm written on the processors according to the traditional fractional order controllers.

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Fractional-order sliding mode control for a bearingless induction motor based on improved load torque observer

Cited by 30 publications

References 21 publications

Sliding mode control of Lorenz chaotic system of fluid thermal convection between infinite plates based on LMI

Sliding mode control of Lorenz chaotic system of fluid thermal convection between infinite plates based on LMI

Research on a hybrid controller combining RBF neural network supervisory control and expert PID in motor load system control

Conformable fractional order controller design and optimization for sensorless control of induction motor

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