Model reference adaptive tracking control for hydraulic servo systems with nonlinear neural-networks

Yao, Zhikai; Yao, Jianyong; Yao, Feiyu; Xu, Qiang; Xu, Minrui; Deng, Wenxiang

doi:10.1016/j.isatra.2019.11.027

Cited by 44 publications

(25 citation statements)

References 25 publications

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“…Research is still in progress to mitigate the deficiencies. Control strategy is one such field, with recent publications highlighting the significance of adaptive control (AC) [15], fuzzy control (FC) [16], feedback linearization control (FLC) [17], sliding mode control (SMC), and its improved extension, e.g., merging with proportion integration differentiation (PID) control [18,19], cascade control (CC) [20], etc.…”

Section: Introductionmentioning

confidence: 99%

A Sliding Mode Control Strategy for an ElectroHydrostatic Actuator with Damping Variable Sliding Surface

Wang

Yang

et al. 2020

Actuators

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Electro-hydrostatic actuator (EHA) has significance in a variety of industrial tasks. For the purpose of elevating the working performance, we put forward a sliding mode control strategy for EHA operation with a damping variable sliding surface. To start with, a novel sliding mode controller and an extended state observer (ESO) are established to perform the proposed control strategy. Furthermore, based on the modeling of the EHA, simulations are carried out to analyze the working properties of the controller. More importantly, experiments are conducted for performance evaluation based on the simulation results. In comparison to the widely used control strategies, the experimental results establish strong evidence of both overshoot suppression and system rapidity.

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Section: Introductionmentioning

confidence: 99%

A Sliding Mode Control Strategy for an ElectroHydrostatic Actuator with Damping Variable Sliding Surface

Wang

Yang

et al. 2020

Actuators

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“…The feedback gains values are K p = 420, K i = 2018, K d = 0.9, K fp = 14 and K fn = 40. All gains values were obtained according the methodology used in [19]. Experimental results are shown in Figs.…”

Section: Experimental Evaluationmentioning

confidence: 99%

“…Intelligent strategies, such as fuzzy logic or neural networks, have been widely used in many different applications, mainly because of their ability of "learning by themselves" how to adapt to their working environment. In the control of hydraulic actuators, such methods form the core of many algorithms [16][17][18][19][20][21][22], and many of them rely on fully online learning procedures to compensate for the unknown disturbances and unmodeled dynamics. Some works are based on radial basis function (RBF) neural networks [20,21], others use simplified feedforward multi-layer perceptron neural networks linearizated by means of Taylor's series expansion [19,23], an approach based on [24] and similar to that of several works in other areas, such as [25,26].…”

Section: Introductionmentioning

confidence: 99%

“…In the control of hydraulic actuators, such methods form the core of many algorithms [16][17][18][19][20][21][22], and many of them rely on fully online learning procedures to compensate for the unknown disturbances and unmodeled dynamics. Some works are based on radial basis function (RBF) neural networks [20,21], others use simplified feedforward multi-layer perceptron neural networks linearizated by means of Taylor's series expansion [19,23], an approach based on [24] and similar to that of several works in other areas, such as [25,26]. These approaches are suitable for deriving Lyapunov-based stability proofs, but they are also prone to the same problems discussed in the last paragraph: Since hydraulic actuators have several highly nonlinear effects and uncertain parameters, these algorithms are difficult to tune unless the available control hardware is very powerful.…”

Section: Introductionmentioning

confidence: 99%

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A neural network-based inversion method of a feedback linearization controller applied to a hydraulic actuator

Borges

Perondi

Cunha

et al. 2021

J Braz. Soc. Mech. Sci. Eng.

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In this work, we use a neural network as a substitute for the traditional analytic functions employed as an inversion set in feedback linearization control algorithms applied to hydraulic actuators. Although very effective and with strong stability guarantees, feedback linearization control depends on parameters that are difficult to determine, requiring large amounts of experimental effort to be identified accurately. On the other hands, neural networks require little effort regarding parameter identification, but pose significant hindrances to the development of solid stability analyses and/or to the processing capabilities of the control hardware. Here, we combine these techniques to control the positioning of a hydraulic actuator, without requiring extensive identification procedures nor losing stability guarantees for the closed-loop system, at reasonable computing demands. The effectiveness of the proposed method is verified both theoretically and by means of experimental results.

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“…Yao, Z.K. et al [19] proposed a tracking control combining MRAC and neural-networks for hydraulic systems. Ma, J. et al [20] designed a neural network sliding mode variable structure decoupling controller based on model reference adaptive.…”

Section: Introductionmentioning

confidence: 99%

Application of a New Model Reference Adaptive Control Based on PID Control in CNC Machine Tools

Gai

Jiao

et al. 2021

Machines

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To improve the control performance of the CNC machine tool feed servo system with nonlinear disturbances, a model reference adaptive control (MRAC) based on PID control is proposed. The method is based on the traditional three-loop control, the output signal of the speed loop controller is used as the input signal of the model reference adaptive controller, and the adaptive law is derived on the basis of Lyapunov stability theory. To verify the effectiveness, this paper analyzed the method through simulation and experiment. Results showed that the following error caused by nonlinear friction was significantly reduced when the speed was reversed, and the influence of noise and mechanical resonance was effectively suppressed. The proposed control method in this paper improves the motion-control accuracy and anti-interference ability of the control system.

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Model reference adaptive tracking control for hydraulic servo systems with nonlinear neural-networks

Cited by 44 publications

References 25 publications

A Sliding Mode Control Strategy for an ElectroHydrostatic Actuator with Damping Variable Sliding Surface

A Sliding Mode Control Strategy for an ElectroHydrostatic Actuator with Damping Variable Sliding Surface

A neural network-based inversion method of a feedback linearization controller applied to a hydraulic actuator

Application of a New Model Reference Adaptive Control Based on PID Control in CNC Machine Tools

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