Model predictive control of sensorless hybrid stepper motors in auxiliary adjuster for stereotactic frame fixation

Mande, Daouda; Lin, Chong; Lee, Chia-Sung; Chao, Yang; Chen, Chi-An

doi:10.1016/j.mechatronics.2017.10.001

Cited by 10 publications

(6 citation statements)

References 14 publications

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“…For any phase stepping motor, the step angle is set to be θe when it moves in the whole-step mode. After subdividing, the step angle becomes θe/d, where d represents the subdivision number, that is, the original step is θe, now the step is θe, and the total step is θe/d [10].…”

Section: Subdivided Driving Technologymentioning

confidence: 99%

The Subdivided Driving System Used in Stepping Motor

2020

IJOMAM

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Section: Subdivided Driving Technologymentioning

confidence: 99%

The Subdivided Driving System Used in Stepping Motor

2020

IJOMAM

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“…The open-loop control method utilizes a train of pulses generated by a power driver, in which speed and shaft position information has not been used. This method introduces large peak overshoot, torque ripple problems, and resonance that degrade the performance of the system (Daouda et al, 2017). Therefore, high precision applications need a closed-loop method (Butcher et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Actor–critic learning-based adaptive fuzzy controller for stepper motor

Babu¹,

Chellaswamy²,

Geetha³

2023

Transactions of the Institute of Measurement and Control

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This paper presents a combination of both the Takagi–Sugeno–Kang fuzzy controller and the advantage of reinforcement learning for the reduction in the effect of external disturbances and system uncertainties. A neural network is used for the implementation of a critic network; the parameters are updated using the Lyapunov criteria for avoiding local minima problems. The fewer learning parameters used helps online tuning of the controller by the critic network based on the reward function. MATLAB/Simulink is used for simulation of the proposed system under different conditions in the investigation of the performance of the proposed controller. The actor parameters are updated based on the change in the reward function error and a control function. Five different conditions, namely, no-load condition, sudden load-torque changes, system parameter uncertainty, sudden phase interruption, and the impact of noise have been studied. The proposed method was found to meet the high-speed response with the tracking error of ±2.23% for the tracking reference trajectory and tracking error of ±2.5% under constant load-torque disturbance. The test results were compared with two benchmark controllers for the verification of the effectiveness of the proposed controller. Simulation results showed the proposed method providing an adaptive and precise speed response. Therefore, it is suitable for non-linear and uncertain applications.

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“…Highly developed control methods are utilized in the HSM to decrease transmission torque to address the limitations of current technology. 12,13 Due to the turbulence generated in the electromagnetic torque, openly controlled HSMs are susceptible to mechanical resonance. 14,15 Furthermore, load fluctuations have no impact on the efficiency of open-loop HSMs in operation.…”

Section: Introductionmentioning

confidence: 99%

A BMO‐based MRPID controller with optimal control of speed in hybrid stepper motor

Deepa,

Venkatesh,

Nandalal

2023

Optim Control Appl Methods

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This paper proposes a Barnacles mating optimizer‐based multi‐resolution proportional‐integral derivative (MRPID) controller for precise speed control of the hybrid stepper motor (HSM). The proposed approach is a barnacle mating optimizer (BMO) control scheme. The main objective of this approach is to use the MRPID controller to improve speed control in particular and uncertain conditions. The BMO is utilized to create the proposed MRPID controller. The proposed converter has a low switching voltage and uses a low input current. The proposed converter supplies a large amount of power to the voltage source inverter (VSI), which converts DC to AC and then supplies it to the HSM. The HSM can be utilized in various settings, including robots and factory applications. Then, the performance of the proposed system has been evaluated in the MATLAB platform and compared with various existing systems. The existing adaptive neuro‐fuzzy inference system (ANFIS) and the moth flame optimization algorithm (MFO) methods are used to validate the efficiency of the proposed controller. The proposed system rise time is 0.0007, the settling time is 0.1, the recovery time is 0.221, the AMU is 1.205, the IAE is 0.1034, and the SSE is 0.234. According to the simulation findings, the suggested system is statistically significant.

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Model predictive control of sensorless hybrid stepper motors in auxiliary adjuster for stereotactic frame fixation

Cited by 10 publications

References 14 publications

The Subdivided Driving System Used in Stepping Motor

The Subdivided Driving System Used in Stepping Motor

Actor–critic learning-based adaptive fuzzy controller for stepper motor

A BMO‐based MRPID controller with optimal control of speed in hybrid stepper motor

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