T–S Fuzzy Control of Travelling-Wave Ultrasonic Motor

Jingzhuo, Shi; Huang, Wenwen; Zhou, Ying

doi:10.1007/s40313-019-00538-y

Cited by 9 publications

(6 citation statements)

References 17 publications

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“…There were many attempts to develop control strategies for manipulating the angular velocity of ultrasonic motors. Classical PI and PID controllers [15], [16], fuzzy logic controllers VOLUME ..., 2023 [17], [18], neural network controllers [19], and adaptive controllers [20], [21], have been proposed as control algorithms for ultrasonic motors. For instance, classical controllers are simple and offer a wide stability margin but fail to cope with time-varying systems if not equipped with self-learning capabilities or an auto-tuning algorithm.…”

Section: Introductionmentioning

confidence: 99%

A Closed-Loop Micro-Ultrasonic Motor Control System With Extremum Seeking

et al. 2023

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Microactuators are an essential technology to revolutionize existing micromachines, but making them closed-loop controlled with an internal sensor is a great challenge due to the limited size of the components. In this paper, we build a micro-ultrasonic motor in the 2-3 mm scale, combine it with a tunneling magneto resistance (TMR) sensor with dimensions of 1.4 mm × 1 mm, and construct a closedloop system with a control algorithm. It is currently the smallest rotary actuator-sensor system assembled into a size with a height of 3.2 mm, a width of 3.2 mm, and a length of 5 mm. In the system, we attempt to solve a steady-state error problem for the micro-ultrasonic motor's internal parameter changes, such as a temperature rise and the resonant frequency variation. A model-free, real-time-adaptive extremum seeking controller (ESC) that continuously tracks and localizes the optimum driving frequency is implemented into the system for the desired rotation with minimum steady-state error. Although the controller is simply designed, experiments show a good accordance between the resulting angular velocity and the reference.INDEX TERMS Piezoelectric actuators, ultrasonic motors, micromotors, closed-loop control, extremum seeking controller, and temperature disturbance.

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Section: Introductionmentioning

confidence: 99%

A Closed-Loop Micro-Ultrasonic Motor Control System With Extremum Seeking

et al. 2023

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“…In [8] and [9], a neural network controller was proposed due to its high flexibility for approximating complex functions. In [10], a fuzzy controller was developed using expert knowledge of PID tuning for constructing the fuzzy rules. Furthermore, in [11], neural networks were combined with fuzzy logic to get a controller with neural networks flexibility, and fuzzy logic expertise.…”

Section: Introductionmentioning

confidence: 99%

“…Fuzzy controllers require expert knowledge to craft fuzzy rules that can remain suboptimal. In [10], the PID gains were tuned for three different operation conditions, and the gains were interpolated through a sum of Gaussians for intermediate operation conditions. In addition to the suboptimality of the interpolation region, extrapolation might result in instability.…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, proposed controllers in the literature (linear controllers and controllers with inherently linear structure (i.e. Fuzzy-PID [10], MIT Control [13])) limit the frequency control range to avoid discontinuity and hysteresis in the speed response. Such constraints can produce reliable controllers at the expense of limiting the motor output.…”

Section: Introductionmentioning

confidence: 99%

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Robust Speed Control of Ultrasonic Motors Based on Deep Reinforcement Learning of a Lyapunov Function

2022

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Speed control of ultrasonic motors (USM) needs to be precise, fast, and robust; however, this becomes a challenging task due to the nonlinear behavior of these motors including nonlinear response, pull-out phenomenon, and speed hysteresis. However, linear controllers would be suboptimal and unstable, and nonlinear controllers would require expert knowledge, expensive online calculations, or costly model estimation. In this paper, we propose a model-free nonlinear offline controller that can significantly mitigate these challenges. Using deep reinforcement learning (DRL) algorithms, a neural network speed controller was optimized. A soft actor-critic (SAC) DRL algorithm was chosen due to its sample efficiency, fast convergence, and stable learning. To ensure controller stability, a custom control Lyapunov reward function was proposed. The steady-state USM behavior was mathematically modeled for easing controller design under simulation. The SAC agent was designed and trained first in simulation and then further trained experimentally. The experimental results support that the trained controller can successfully expand speed operation range ([0,300] rpm), plan optimal control trajectories, and stabilize performance under varying load torque and temperature drift.

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“…The same as in the neural network model, fuzzy model is also based on experimental data, easy to show the nonlinear information. The fuzzy method is mostly used to realize speed and position control [17]- [19].…”

Section: Introductionmentioning

confidence: 99%

A study on modeling of a piezoelectric motor

Baba¹,

Bouzi

2021

IJPEDS

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n this paper, a study on modeling of a piezoelectric transducer type rotary traveling wave ultrasonic motor (USM) is presented. First a mathematical model and numerical simulation results are achieved. The model is based on the theory of piezoelectricity and physic theory. An experimental model is worked out and compared to the numerical model. The influence of the temperature on characteristics such as the rotational speed of the motor is considered. The speed of the USM is measured at temperature between 17°Cand 50°C. To develop suitable control strategies for the drive, a fuzzy model type Takagi-Sugeno is used. The unknown parameters of the output membership functions are determined by least square method. Experimental data are used to examine the validity of the fuzzy model. Comparison between experimental and calculated data of the fuzzy model indicates that the fuzzy model can well describe the nonlinear characteristics among the frequency of driving voltage and rotating speed.

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T–S Fuzzy Control of Travelling-Wave Ultrasonic Motor

Cited by 9 publications

References 17 publications

A Closed-Loop Micro-Ultrasonic Motor Control System With Extremum Seeking

A Closed-Loop Micro-Ultrasonic Motor Control System With Extremum Seeking

Robust Speed Control of Ultrasonic Motors Based on Deep Reinforcement Learning of a Lyapunov Function

A study on modeling of a piezoelectric motor

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