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RISS 인기검색어
- 검색키워드
- 저자 : Jingzhuo Shi (검색결과 5 건)
- 무료
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Generalized Minimum Variance Iterative Learning Speed Control of Ultrasonic Motor
Jingzhuo Shi,Wenwen Huang 대한전기학회 2021 Journal of Electrical Engineering & Technology Vol.16 No.5
In order to reduce the infl uence of time-varying disturbance on motion control performance of ultrasonic motor, the speed control strategy of ultrasonic motor is studied in this paper. An iterative learning control strategy including prediction and closed-loop control is proposed by combining iterative learning control with generalized minimum variance self-tuning control. By introducing the previous control information into the objective function and using the design method of the generalized minimum variance control strategy, the generalized minimum variance iterative learning control law is obtained, which has both self-learning and self-adaptive ability. The proposed control strategy is applied to the speed control of ultrasonic motor and validated by simulation and experiment. The results of experiments under diff erent load conditions and diff erent given values show that good control performance can be obtained by adopting the proposed control strategy. The results of intermittent loading experiments indicate that, the ability to adapt to the non-repetitive disturbances such as sudden load mutation is enhanced.
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Self-Tuning Speed Control of Ultrasonic Motor Combined with Efficiency Optimization
Jingzhuo Shi,Fangfang Lv,Bo Liu 제어·로봇·시스템학회 2014 International Journal of Control, Automation, and Vol.12 No.1
Aiming at the nonlinear characteristics of ultrasonic motor (USM) control system, a self-tuning pole assignment speed control strategy is proposed in this paper. The parameters of motor system are identified online, and then the controller is modified online according to the identified parameters of motor. In view of the short-time working characteristic of USM, a method for determining initial values of parameters with online identification algorithms is presented. Using this method, efficient self-tuning control can be realized using a small quantity of testing data. The proposed strategy is robust and relatively simple. Experimental results indicate that the speed control performance is good.
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Improved Indirect Iterative Learning MRAC Strategy for Ultrasonic Motor
Jingzhuo Shi,Shubei Liu 대한전기학회 2023 Journal of Electrical Engineering & Technology Vol.18 No.2
As a kind of model-based control strategy, the control performance of model reference adaptive control (MRAC) system is directly determined by the precision of the object’s model. But in practical applications, it is difficult to obtain accurate mathematical models of many objects. Even if the model can be obtained, the order of the model will be relatively high. If a low-order model can be used to design a low-complexity MRAC controller and maintain good control performance, it will greatly expand the application of MRAC strategy. Iterative learning control (ILC) method is used to improve the robustness of MRAC to model deviation in this paper. A simple iterative learning controller is designed to adjust the adaptive law of feed-forward gain in MRAC system. The proposed method is applied to the speed control of ultrasonic motor. Experimental results show that even if the MRAC controller is designed using the low-order model, the good control performance that meets expectations can still be obtained through iterative learning. Moreover, the proposed control method has a small amount of calculation, and is suitable for industrial applications.
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A Model Deviation Correction Method Based on Iterative Learning for Ultrasonic Motor
Wenwen Huang,Jingzhuo Shi 제어·로봇·시스템학회 2022 International Journal of Control, Automation, and Vol.20 No.11
Most control strategies are designed based on the mathematical model of the controlled object. The consistency between the mathematical model and the actual object is the necessary prerequisite to ensure that the actual control performance meets the expectation. In order to make the actual control performance entirely consistent with the expectation expressed by the simulation results, taking the ultrasonic motor control system as an example, a new nonlinear model correction method is proposed. The proposed method generates the timevarying correction of control variable through a simple P-type iterative learning process, so that the response process of the actual system is consistent with the simulation results expressing desired control performance. The direct target of this method is the control performance, and the estimated result is the inverse function of the nonlinear characteristics of the plant. The inverse model is connected in series with the real object, so that the dynamic performance of the actual system is consistent with the designed performance. Comparative experiment results show the effectiveness of the proposed method.
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New MIT Control Strategy Combined with Iterative Learning Control
Xiao Song,Jingzhuo Shi 제어·로봇·시스템학회 2022 International Journal of Control, Automation, and Vol.20 No.7
As the pioneer of model reference adaptive control (MRAC) method, MIT control strategy is still used in various practical applications. In this paper, MIT is applied to the speed control of ultrasonic motor, trying to use a relatively simple control method to obtain good control performance. However, MIT control strategy only adjusts the gain, so it is difficult to achieve a large correction of the system’s dynamic characteristics, which limits the actual performance. To solve this problem, two improved MIT control strategies based on iterative learning are proposed in this paper to enhance the control performance. Both methods adopt the P-type iterative learning control (P-ILC) strategy with simplest structure. One is to connect the P-ILC controller with the MIT controller in series to adjust the given value of the MIT controller in real time. The other is to use the P-ILC controller to adjust the adaptive gain of the MIT controller in real time, so as to enhance its control freedom and adaptive ability to deal with complex objects. The experimental results show that the proposed control strategies have their own advantages and can significantly improve the control performance after finite iterative learning processes.
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