Model-free adaptive speed control on travelling wave ultrasonic motor

Di, Sisi; Li, Huafeng

doi:10.1515/jee-2018-0002

Cited by 12 publications

(5 citation statements)

References 23 publications

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“…Indeed, a low speed is necessary for microscopy stages, robotic arms, medical operations, and so on. To enable high precision control of the LUSM, various control strategies have been investigated [9]- [14], such as model predictive control, fuzzy neural networks, nonlinear PID and sliding mode.…”

Section: Introductionmentioning

confidence: 99%

Improvement of Low-Speed Precision Control of a Butterfly-Shaped Linear Ultrasonic Motor

et al. 2020

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The control performance of a butterfly-shaped linear ultrasonic motor is not good when the target speed is less than 1 mm/s by traditional methods. To improve low-speed controlling characteristics of linear ultrasonic motor, a closed-loop control strategy by using both the step control and the fuzzy PID control was proposed. The controller was constructed with the function of providing a closed-loop control of the speed by adjusting the driving voltage amplitude in stepping driving mode. The corresponding step controlling parameters were determined by experiments. Comprehensive experiments on the developed control strategy were conducted under different target speeds. There was a maximum of 24.5% speed error at the target speed of 10 μm/s, meanwhile, the coefficient of variation and the response time were 16.3% and 0.11 s, respectively. With the triangular or sinusoidal waves speed tracking curves at amplitude of 1 mm/s, the maximum speed tracking error were 0.1 mm/s and 0.15 mm/s respectively. Thus, a good speed tracking performance was obtained. These results support the functional ability and potential of the proposed method as a low-speed precision control method for linear ultrasonic motors. INDEX TERMS linear ultrasonic motor, step control, fuzzy PID control, low speed NOMENCLATURE Coefficient of variation Maximum speed error of the platform Speed error of the platform Change of speed error of the platform Proportional coefficient of the PID controller Initial value of proportional coefficient Change of proportional coefficient Integral coefficient of the PID controller Initial value of integral coefficient Change of integral coefficient Differential coefficient of the PID controller Initial value of differential coefficient Change of differential coefficient Number of driving waves of the driving signal Set speed of the platform Coefficient of determination Driving period of the driving signal Average speed of the platform Actual position of the platform Actual speed of the platform Standard deviation of the platform speed

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Section: Introductionmentioning

confidence: 99%

Improvement of Low-Speed Precision Control of a Butterfly-Shaped Linear Ultrasonic Motor

et al. 2020

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“…Though it might be an unfair comparison, it clearly shows the advantage of using our proposed nonlinear controller. Our controller is compared to three other controllers; proportional integral derivative (PID), linear quadratic regulator (LQR), and model-free adaptive control (MFAC [36]). The LQR was not obtained through the knowledge of system dynamics ( Ẋ = AX + BU ), but rather an optimization of a full state feedback controller of scaled state (ŝ t .…”

Section: A Comparative Resultsmentioning

confidence: 99%

“…The Lyapunov normalizing gain (K v ) was chosen to scale V (s t ) between [0,10] as the speed error varied between [0,300] rpm. To normalize the input state, a min-max normalization was applied to the state vector to rescale all features between [−1,1] as in (36). the ranges of input variables were set as (f [39…”

Section: A Agent Trainingmentioning

confidence: 99%

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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“…[19]. Recently, MFAC has gained significant popularity in the syngas manufacturing industry [20], intelligent transportation [21], motor systems [22], and so on.…”

Section: Introductionmentioning

confidence: 99%

Particle Swarm Optimization-Based Model-Free Adaptive Control for Time-Varying Batch Processes

Wang,

Sadun,

Jalaludin

et al. 2024

Int. J. Automot. Mech. Eng.

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The batch process is a production process with strong nonlinearity, which usually suffers from time-varying parameters and uncertainty of disturbances. Concerning the mentioned problems, this study proposes to investigate the application of the particle swarm optimization-based model-free adaptive control (PSO-MFAC) method for time-varying batch processes. Model-Free Adaptive Control (MFAC) is a data-driven control method, which is one of the promising methods to solve the nonlinear process. Firstly, a Full Form Dynamic Linearization Model-Free Adaptive Control method has been adopted for the control of batch processes. Further, considering that the adopted model-free adaptive control involves seven control parameters, such as cognitive scaling factor (φ1), social scaling factor (φ2), inertia weight (φ3), learning rate (η), control parameter update rate, exploration rate and learning rate for MFAC obtained by a particle swarm optimization (PSO) algorithm in combination with a criterion function performance index. Finally, by comparing it with the existing methods, a typical batch fermentation was applied to verify that PSO-MFAC had a good control effect. The findings indicate that the PSO-MFAC controller exhibits a preference for exploiting the optimal option due to its φ3 value less than 0.1. The efficacy and feasibility of the PSO-MFAC control effect have been proven by obtaining the lowest integral square error (ISE) value of 1.1192 regarding the nonlinearity of the batch process due to time-varying challenges.

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Model-free adaptive speed control on travelling wave ultrasonic motor

Cited by 12 publications

References 23 publications

Improvement of Low-Speed Precision Control of a Butterfly-Shaped Linear Ultrasonic Motor

Improvement of Low-Speed Precision Control of a Butterfly-Shaped Linear Ultrasonic Motor

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

Particle Swarm Optimization-Based Model-Free Adaptive Control for Time-Varying Batch Processes

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