Adaptive PID and Model Reference Adaptive Control Switch Controller for Nonlinear Hydraulic Actuator

Zuo, Xin; Liu, Jian-wei; Wang, Xin; Liang, Huaqing

doi:10.1155/2017/6970146

Cited by 18 publications

(14 citation statements)

References 30 publications

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“…For superior ABS performance, the slip ratio should be kept close to the ideal value 15. Tracking performance of the braking force using mean square error is expressed in the following equation 10: (14) The differential equation of PID controlled is [10]: (15) Next, the transfer function to be added to MATLAB/Simulink is as follows [30]: (16) The tracking error is calculated as follows [30]: (17) Modern controllers require signals calculated in digital or discrete time 23. In the Proportional-Integral-Derivative controller (PID) P is related to the error that happens in the present, I show the error collected from the past, and D indicates the predicted upcoming error 1.…”

Section: Fig 5 Brake Dynamic In Simulink For Braking System With Abmentioning

confidence: 99%

Anti-Lock Braking System Components Modelling

Alaboodi*¹,

Algadah²

2019

IJITEE

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Anti-lock braking systems are widely used in modern vehicles and provide safe driving for many different road conditions. Tire skidding occurs unexpectedly as a result of non-linearity in the system. The system behavior can be modelled and simulated using simulation software, which would help to visualize the system behavior. It would lead to obtaining optimum brake performance as well as safe driving. Modelling and simulation methods that can be used with every component of the system are presented. A variety of simulation software has been discussed.

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Section: Fig 5 Brake Dynamic In Simulink For Braking System With Abmentioning

confidence: 99%

Anti-Lock Braking System Components Modelling

Alaboodi*¹,

Algadah²

2019

IJITEE

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“…Jacobian identification information where the incremental change of the output weights Δw 1 (k) and the hidden layer weights Δw 0 (k) of the neural network model are given as follows [13,[16][17][18][19][20][21][22][23][24]:…”

Section: Neural Network Indirect Adaptive Controlmentioning

confidence: 99%

“…However, hybrid control has received considerable attention during the past two decades, and the class of switching systems is specifically employed in many industrial applications such as in [17][18][19][20][21][22][23][24] and [25]. In fact, in [17] and [21], respectively, the PID is mixed with the model reference adaptive control and the model predictive controller.…”

Section: Introductionmentioning

confidence: 99%

“…In fact, in [17] and [21], respectively, the PID is mixed with the model reference adaptive control and the model predictive controller. In [19,20], a mixed method between the PID controller and model reference control is proposed for the nonlinear hydraulic actuator and for an aerial inertially stabilized platform. ese methods are proposed to suppress the effect of nonlinear and time-varying parameters and to get faster response time and better tracking performance.…”

Section: Introductionmentioning

confidence: 99%

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Neural Adaptive PID and Neural Indirect Adaptive Control Switch Controller for Nonlinear MIMO Systems

Slama

Errachdi

Benrejeb

2019

Mathematical Problems in Engineering

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This paper proposes an adaptive switch controller (ASC) design for the nonlinear multi-input multi-output system (MIMO). In fact, the proposed method is an online switch between the neural network adaptive PID (APID) controller and the neural network indirect adaptive controller (IAC). According to the design of the neural network IAC scheme, the adaptation law has been developed by the gradient descent (GD) method. However, the adaptive PID controller is built based on the neural network combining the PID control and explicit neural structure. The strategy of training consists of online tuning of the neural controller weights using the backpropagation algorithm to select the suitable combination of PID gains such that the error between the reference signal and the actual system output converges to zero. The stability and tracking performance of the neural network ASC, the neural network APID, and the neural network IAC are analyzed and evaluated by the Lyapunov function. Then, the controller results are compared between APID, IAC, and ASC, in this paper, applying to a nonlinear system. From simulations, the proposed adaptive switch controller has better effects both on response time and on tracking performance with smallest MSE.

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“…There are two types of hydraulic actuators: valvecontrolled cylinder [6][7][8] and pump-controlled cylinder [9,10]. The valve-controlled hydraulic actuator with hydraulic servo valve as control unit is gradually replaced by the pumpcontrolled hydraulic actuator because of its characteristics of large throttling loss, low efficiency, high requirement of oil cleanliness, and high price.…”

Section: Introductionmentioning

confidence: 99%

Theoretical and Experimental Analysis of the Hydraulic Actuator Used in the Active Reflector System

Xiao

Liu

Wang

et al. 2018

Mathematical Problems in Engineering

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In order to improve the control quality of the active reflector system which is used in the 500 m aperture spherical radio telescope (FAST) project, a study on hydraulic actuator is proposed. The dynamic performance of hydraulic actuator is due to the interaction of several physical phenomena. Modeling of such a system needs a unified approach to represent the nonlinear behavior characteristics. Bond graph model is ideal for this task. In addition, conventional PID control strategy cannot achieve the required accuracy and precision, so we combine grey prediction with PID control. Furthermore, a new adjustment mechanism is presented to change the grey step size, which is simple, automatic, less time-consuming, and efficient. Simulations and experiments on the actuator are carried out to evaluate the effectiveness of the proposed control method. In experiments, the velocity error is less than 0.05mm/s and the position error is less than 0.2mm. It shows the actuator meets the astronomical observation requirement totally.

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Adaptive PID and Model Reference Adaptive Control Switch Controller for Nonlinear Hydraulic Actuator

Cited by 18 publications

References 30 publications

Anti-Lock Braking System Components Modelling

Anti-Lock Braking System Components Modelling

Neural Adaptive PID and Neural Indirect Adaptive Control Switch Controller for Nonlinear MIMO Systems

Theoretical and Experimental Analysis of the Hydraulic Actuator Used in the Active Reflector System

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