Model-Free Adaptive Control of Direct Drive Servo Valve of Electromagnetic Linear Actuator

Zhu, Jianhui; Dai, Jufeng; Wang, Cheng

doi:10.1155/2018/7105872

Cited by 4 publications

(6 citation statements)

References 18 publications

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“…The electromagnetic linear actuator system consists of three subsystems, the circuit, the magnetic circuit and the mechanical subsystem. Figure 2 describes the coupling relationship between the sub-systems [19].…”

Section: Structure and Modelmentioning

confidence: 99%

“…The MFAC control algorithm has strong robustness and can uniformly deal with the control problems of nonlinear systems with time-varying structure, time-varying parameters and time-varying order, and its control performance is little affected by parameter variations, and the PPD as slow time-varying parameters are insensitive to parameter variations and system structure, so that the control system can get good control effects by choosing different parameter values within a certain range. Some of the controller parameters were selected for multi-objective optimization and combined with the [24], The set of parameters obtained after optimization of the MFAC and MFASMC control scheme parameters are: weighting factorµ = 1, λ = 1.2 × 10 −6 , initial value of Φf,Ly,Lu (k) is Φf,Ly,Lu (1) = [0.03, 0.05, 0.03, 0.03] T , step factor ρ 1 = 0.4, ρ 2 = 0.1, ρ 3 = 0.7, ρ 4 = 0.7, ε = 10 −5 . The simulation sampling period is chosen as 0.000 01 s.…”

Section: Control Performance Analysismentioning

confidence: 99%

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Data-driven model-free adaptive sliding mode control for electromagnetic linear actuator

Gu¹,

Tan²,

Li³

et al. 2022

J. Micromech. Microeng.

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A data-driven model-free adaptive sliding mode control (MFASMC) method is proposed to tackle the problems of inaccurate model and time-varying parameters of a micro electro-magnetic linear actuator system, considering the dependence of existing model-based control methods on the system dynamics model and the impact of unmodeled dynamics on the control performance. Firstly, the pseudo-gradient (PG) concept in the model-free adaptive control (MFAC) framework is used to transform the electromagnetic linear actuator dynamics model, which is difficult to obtain parameters accurately, into a full-format dynamic linearized data model, the dependence of the controller on the electromagnetic linear actuator model is reduced. To compensate for the effects of unknown perturbations, an improved discrete sliding mode exponential convergence law is introduced to derive a new composite control algorithm based on the pseudo partial derivative estimator, which improves the robustness of the control system. Then, the convergence of the control error and the stability of the system are demonstrated by theoretical analysis. The results show that the proposed MFASMC reduces the 10mm step response time of the electromagnetic linear actuator by 46.3% and 25.3% compared with PID and MFAC. The phase lag time and root-mean-square error of MFASMC under sinusoidal conditions are 0.8ms, 0.178mm respectively, and the steady-state error does not exceed 0.05mm. The experimental and simulation results keep the error within 5%, the proposed control algorithm has good trajectory tracking response speed and control accuracy, and has good robustness to system uncertainty and external force disturbance.

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Section: Structure and Modelmentioning

confidence: 99%

Section: Control Performance Analysismentioning

confidence: 99%

Data-driven model-free adaptive sliding mode control for electromagnetic linear actuator

Gu¹,

Tan²,

Li³

et al. 2022

J. Micromech. Microeng.

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show abstract

“…A miniature EMLA with self-alignment and self-attachment features is proposed (Zhi et al , 2020). A model-free self-applicable control strategy with full morphological dynamic linearization was used and the robustness and effectiveness of the algorithm on EMLA were verified (Zhu et al , 2018). Single-discipline optimization often uses a key output as the objective function for optimization, and other outputs are considered as constraints.…”

Section: Introductionmentioning

confidence: 99%

Multidisciplinary optimization of electromagnetic linear actuators for direct-drive systems with a combination of static and dynamic performance

Chi

Tan

et al. 2022

COMPEL

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Purpose The purpose of this paper is to solve the common problems that traditional optimization methods cannot fully improve the performance of electromagnetic linear actuators (EMLAs). Design/methodology/approach In this paper, a multidisciplinary optimization (MDO) method based on the non-dominated sorting genetic algorithm-II (NSGA-II) algorithm was proposed. An electromagnetic-mechanical coupled actuator analysis model of EMLAs was established, and the coupling relationship between static/dynamic performance of the actuator was analyzed. Suitable optimization variables were designed based on fuzzy grayscale theory to address the incompleteness of the actuator data and the uncertainty of the coupling relationship. A multiobjective genetic algorithm was used to obtain the optimal solution set of Pareto with the maximum electromagnetic force, electromagnetic force fluctuation rate, time constant and efficiency as the optimization objectives, the final optimization results were then obtained through a multicriteria decision-making method. Findings The experimental results show that the maximum electromagnetic force, electromagnetic force fluctuation rate, time constants and efficiency are improved by 18.1%, 38.5%, 8.5% and 12%, respectively. Compared with single-discipline optimization, the effectiveness of the multidiscipline optimization method was verified. Originality/value This paper proposes a MDO method for EMLAs that takes into account static/dynamic performance, the proposed method is also applicable to the design and analysis of various electromagnetic actuators.

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“…In MFAC, dynamic linear time-varying model has three main forms, namely, compact form dynamic linearization, partial form dynamic linearization, and full form dynamic linearization [5]. Partial-form dynamic linearization and full form dynamic linearization have more parameters [5]- [7]; thus, they have more adjustable freedom and controller design flexibility [6]. However, they also substantially increase the complexity and difficulty during the controller design.…”

mentioning

confidence: 99%

“…The linearization model structure of the compact form is simple with a minimum number of controller parameters; hence, the stability and parameter selection of the compact form [5], [8]- [10] are explored in depth, and comparative studies [10], [11] are widely reported. Given that the compact form of the MFAC structure is simple and computation is small, many applications are obtained, such as, multi-agent system and formation control [12], [13], variable polarity plasma arc welding [14], interlinked AC/DC microgrids [15], autonomous cars [16], gas collector pressure system of coke ovens [17], data dropout compensation for networked nonlinear systems network [18], direct drive servo valve of electromagnetic linear actuator, and position sensorless drive for high speed BLDC motors [7], [19]. However, the following problems in the existing MFAC need further examination.…”

mentioning

confidence: 99%

A New Data-Driven Model-Free Adaptive Control for Discrete-Time Nonlinear Systems

Deng

Yang

2019

IEEE Access

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The existing model-free adaptive control encounters problems, such as too many parameters that need to be determined, some of which with unclear physical significance and whose selection depend entirely on trial and error. Aiming at this problem, a new dynamic linearized model is established by using Taylor series expansion of discrete-time nonlinear systems and the differential mean value theorem. Then, a new data-driven model-free adaptive control is proposed, which reduces the required parameters from six in the existing model-free adaptive control to four in the new model-free adaptive control. All the parameters have clear physical significance, and the selections of the initial values of the parameters are based on the stability conditions of the closed-loop system. Therefore, the selection of the parameters in the new modelfree adaptive control does not depend entirely on trial and error but on regularity. By introducing the idea of internal model control in the new model-free adaptive control, the anti-disturbance performance of the closed-loop system is enhanced. Finally, simulation results for three complicated nonlinear systems show that the proposed model-free adaptive control is superior to the existing model-free adaptive control.INDEX TERMS Model-free adaptive control, data-driven, discrete-time systems, nonlinear control. I. INTRODUCTIONCHUNHUA YANG received the M.Eng. degree in automatic control engineering and the Ph.D. degree in control science and engineering from

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Model-Free Adaptive Control of Direct Drive Servo Valve of Electromagnetic Linear Actuator

Cited by 4 publications

References 18 publications

Data-driven model-free adaptive sliding mode control for electromagnetic linear actuator

Data-driven model-free adaptive sliding mode control for electromagnetic linear actuator

Multidisciplinary optimization of electromagnetic linear actuators for direct-drive systems with a combination of static and dynamic performance

A New Data-Driven Model-Free Adaptive Control for Discrete-Time Nonlinear Systems

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