A two-stage model for rate-dependent inverse hysteresis in reluctance actuators

Xu, Yujie; Li, Xiaoping; Yang, Xiaofeng; Yang, Zhile; Wu, Liwei; Chen, Qingsheng

doi:10.1016/j.ymssp.2019.106427

Cited by 37 publications

(13 citation statements)

References 33 publications

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“…4. The controller is the algorithm (34) with Γ η and Θ s being replaced by Γ η,ex in (30) and Θ s,ex in (15), respectively. The controller's sampling interval is set as T = 0.01 s unless otherwise noted, the time constant for the sliding surface is set as H = 1 s, and the desired position is fixed as p d ≡ 1 m. Here, notice that the sampling interval T of the controller is set much larger than the timestep size h of the plant simulation.…”

Section: Numerical Examplesmentioning

confidence: 99%

“…This approach has been employed mainly for actuators with hysteresis [12]- [14]. Previous studies have investigated applications to piezoactuators [12], magnetostrictive actuators [14], shape memory alloys [14], reluctance motors [15], and electro-hydraulic systems [16]. Other types of nonlinearity, such as dead-zone and backlash [13] and creep [12], have also been considered.…”

Section: Introductionmentioning

confidence: 99%

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Implicit Implementation of Nonsmooth Controllers to Nonsmooth Actuators

Kikuuwe

Yamamoto

Brogliato

2022

IEEE Trans. Automat. Contr.

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This paper presents an approach to implement a sliding-mode position controller to a plant equipped with a nonsmooth actuator. The actuator is modeled as a set-valued function from the control input and the velocity to the actuator force, which is motivated by quasistatic characteristics of hydraulic actuators shown in a previous study. The implementation of the sliding-mode controller is performed with the implicit discretization of the nominal plant model and the controller, which copes with the difficulties caused by set-valuedness, such as numerical chattering. Stability analyses both in the continuous-time and discrete-time domains are presented. Simulation results illustrate the theoretical findings.

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Section: Numerical Examplesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Implicit Implementation of Nonsmooth Controllers to Nonsmooth Actuators

Kikuuwe

Yamamoto

Brogliato

2022

IEEE Trans. Automat. Contr.

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“…The first one is the magnetic equivalent circuit (MEC) approach, which results in low-order dynamical models well suited for control or estimation, as well as for computationally inexpensive simulations [5], [6], [12]. Although these models only capture the dynamics of a small number of variables, they can be made very accurate by including phenomena such as eddy currents or magnetic saturation and hysteresis [13], [14]. The second approach is the use of high-order numerical models, e.g., those based on the finite element method [4], [15], which provide much more detailed results, but are generally computationally expensive and not appropriate for some classes of analysis.…”

Section: Coilmentioning

confidence: 99%

On the Stability of Electromechanical Switching Devices: An Analytic Study of Hysteretic Switching Behaviors

Ramirez-Laboreo¹,

Sagüés²

2022

Preprint

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Electromagnetic relays and solenoid actuators are commercial devices that generally exhibit bistable behavior. In fact, this is the reason why they are extensively used to switch between two possible configurations in electrical, pneumatic, or hydraulic circuits, among others. Although the state of the art is extensive on modeling, estimation, and control of these electromechanical systems, there are very few works that focus on analysis aspects. In this paper, we present an equilibrium and stability analysis whose main goal is to provide insight into such bistable behavior. The study is based on a hybrid dynamical model of the system also presented in the paper. This model is used to obtain analytic expressions that relate the physical parameters to the switching conditions. The results are extensively discussed and possible practical applications are also proposed.

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“…Instead, the data‐driven models describe hysteresis based on the measured data of PEAs without considering the physical mechanism, 22 some typical data‐driven hysteresis models include Bouc–Wen model, 23 Preisach model, 24 Prandtl–Ishlinskii model, 25,26 Krasnoselskii–Pokrovskii model, 27 fuzzy model, 28 neural‐network‐based model 29‐31 . In addition, apart from the above classic models, many improved composite models have also been reported in latest literatures 15,32‐35 …”

Section: Introductionmentioning

confidence: 99%

“…[29][30][31] In addition, apart from the above classic models, many improved composite models have also been reported in latest literatures. 15,[32][33][34][35] Although compensation for hysteresis can improve the tracking performance of the PEA, unknown external disturbance still easily causes the PEA to deviate from the desired displacement. 36 To address this issue, researchers have designed lots of advanced controllers to further improve the tracking accuracy.…”

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confidence: 99%

Neural network based adaptive control for a piezoelectric actuator with model uncertainty and unknown external disturbance

Chen

Sun

et al. 2022

Intl J Robust & Nonlinear

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To lessen the positioning error of the piezoelectric actuator (PEA) caused by hysteresis nonlinearity and unknown external disturbance, a neural network based adaptive controller is designed to realize the accurate trajectory tracking of the PEA. Specifically, a more universal model, consisting of a hysteresis submodel and a dynamics submodel, is first built for the PEA without the requirement of parameter identification. On this basis, a sliding mode adaptive controller capable of handling unknown parameters of the dynamics submodel is designed to weaken the damage of external disturbance to the system stability. Furthermore, to deal with the hysteresis submodel with unknown structure and parameters, a neural network based self‐tuning control scheme is developed to enable the PEA to accurately track the desired trajectory. Moreover, Lyapunov stability analysis is performed to strictly prove that the tracking error of the system can asymptotically converge to zero. Finally, the performance of the designed controller is verified via sufficient comparative simulations and experiments.

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A two-stage model for rate-dependent inverse hysteresis in reluctance actuators

Cited by 37 publications

References 33 publications

Implicit Implementation of Nonsmooth Controllers to Nonsmooth Actuators

Implicit Implementation of Nonsmooth Controllers to Nonsmooth Actuators

On the Stability of Electromechanical Switching Devices: An Analytic Study of Hysteretic Switching Behaviors

Neural network based adaptive control for a piezoelectric actuator with model uncertainty and unknown external disturbance

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