An Adaptive Control Using Multiple Neural Networks for the Position Control in Hydraulic Servo System

Yuan, Kun; Chu, Minghui; Liu, Yuanliang; Chang, Chuan-Wei; Chien, Shu-Yen

doi:10.1007/11539117_44

Cited by 11 publications

(6 citation statements)

References 6 publications

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“…Tong [5] proposed a method of neural network based adaptive control for an electrohydraulic servo system with complex nonlinearities and uncertainties. Kang [6] gave a model following adaptive control based on neural network for electrohydraulic servo system subjected to varied load. The proposed control method utilized multiple neural networks including a neural network controller, a neural network emulator and a neural tuner.…”

Section: Introductionmentioning

confidence: 99%

Research on neural networks based modelling and control of electrohydraulic system

Pang

Yuan

Xing

et al. 2010

2010 2nd International Conference on Advanced Computer Control

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The electrohydraulic servo system of a certain type of mines weeping plough is a complex and nonlinear system. It is difficult to construct its accurate model by first principle method and to achieve satisfactory control performance by traditional PID controller. In this paper, the radial basis function neural network with orthogonal least square learning algorithm is used to model the electrohydraulic system and the neural network based direct inverse is adopted to control the system. The experimental results and comparisons with other techniques clearly show the validity of the proposed methods.

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Section: Introductionmentioning

confidence: 99%

Research on neural networks based modelling and control of electrohydraulic system

Pang

Yuan

Xing

et al. 2010

2010 2nd International Conference on Advanced Computer Control

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“…The objectives using NN are stability robustness, good tracking performance, and robustness against plant modeling uncertainty and environmental uncertainty. [8][9][10][11][12][13] New methods for proportional-integral-derivative (PID) controller using single neuron 11 is proposed and tested in speed control system with DC servo motor. Furthermore, the single-neuron proportional-integral (SNPI) controller has properties such as self-learning, self-adaptive, and strong robustness.…”

Section: Introductionmentioning

confidence: 99%

Adaptive neural network–based synchronized control of dual-axis linear actuators

Mao

Suprapto

Hung

2016

Advances in Mechanical Engineering

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Synchronized motion control with high accuracy becomes very essential part in industry. Due to some possible effect such as unknown disturbance or unmatched system model, it is difficult to obtain the precision of synchronous control using the conventional proportional-integral control method with parallel architecture. The adaptive compensator must be employed to eliminate tracking errors. The objective of this research is to propose the modified cross-coupling architecture using single-neuron proportional-integral controller and a synchronous compensator for dual-axis linear actuator. The single-neuron proportional-integral control strategy with delta learning algorithm can adjust the weighting coefficients of controllers to provide the robustness for each single-axis DC linear actuator system. A back-propagation neural network compensator is designed to adaptively reduce position and velocity errors between the two-axis servo systems. Both simulation and experimental results are developed to demonstrate that the synchronous position tracking performances in terms of root mean square error and sum of absolute error can be substantially improved, and the robustness to linear actuator uncertainties can be obtained as well. The proposed coupling strategy which uses the microchip platform and pulse-width modulation control technique is realized and implemented, and the synchronization performances to external disturbance load are illustrated by several experimental results.

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“…He [5] demonstrated experimental modelling of dynamic behaviors of an industrial hydraulic actuator with neural networks for the first time, and the results showed that neural networks are capable of modelling and predicting characteristics of the highly nonlinear hydraulic actuator. Kang [6] proposed a model-following adaptive algorithm using multiple neural networks to control the angle of variable displacement pump, and the constructed multiple neural networks were used for numerical simulation and parameters adjustment. However, all these papers mentioned above focus on the usage of the multi-layer perceptron neural networks which have some disadvantages such as slow learning speed, local minimal convergence behavior and sensitivity to the randomly selected initial weight values [7].…”

Section: Introductionmentioning

confidence: 99%

Hierarchical genetic algorithm based RBF neural networks and application for modelling of the automatic depth control electrohydraulic system

Xing

Pang

et al. 2011

Int. J. Control Autom. Syst.

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The paper presents an approach to model nonlinear dynamic behaviors of the Automatic Depth Control Electrohydraulic System (ADCES) of a certain minesweeping weapon with Radial Basis Function (RBF) neural networks trained by hierarchical genetic algorithm. In the proposed hierarchical genetic algorithm, the control genes are used to determine the number of hidden units, and the parameter genes are used to identify center parameters of hidden units. In order to speed up convergence of the proposed algorithm, width and weight parameters of RBF neural network are calculated by linear algebra methods. The proposed approach is applied to the modelling of the ADCES, and experimental results clearly indicate that the obtained RBF neural network can emulate complex dynamic characteristics of the ADCES satisfactorily. The comparison results also show that the proposed approach performs better than the traditional clustering-based method.

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An Adaptive Control Using Multiple Neural Networks for the Position Control in Hydraulic Servo System

Cited by 11 publications

References 6 publications

Research on neural networks based modelling and control of electrohydraulic system

Research on neural networks based modelling and control of electrohydraulic system

Adaptive neural network–based synchronized control of dual-axis linear actuators

Hierarchical genetic algorithm based RBF neural networks and application for modelling of the automatic depth control electrohydraulic system

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