Adaptive position control of electrohydraulic servo systems using ANN

Knohl, Torsten; Unbehauen, H.

doi:10.1016/s0957-4158(99)00054-9

Cited by 107 publications

(58 citation statements)

References 7 publications

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“…New approaches, in which intelligence is not given to the system from outside but is acquired by the system through learning, have proven much more successful [11]. Because of their selflearning, self-organizing and self-adapting capability, neural networks have become a powerful tool for many complex applications, such as EHS systems [12], [13]. However neural network controllers require a predefined structure which may lead to additional time consuming computations during the control process.…”

Section: Introductionmentioning

confidence: 99%

Implementation of an Intelligent Adaptive Controller for an Electrohydraulic Servo System Based on a Brain Mechanism of Emotional Learning

Sadeghieh

Roshanian

Najafi

2012

International Journal of Advanced Robotic Systems

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In this paper, an experimental analysis of identification and an online intelligent adaptive position tracking control based on an emotional learning model of the human brain (BELBIC) for an electrohydraulic servo (EHS) system is presented. A mathematical model of the system is derived and the parameters of the model are identified. The BELBIC is designed based upon this dynamic model and utilized to control the real laboratorial EHS system. The experimental results are compared to those obtained from an optimal PID controller to prove that classic linear controllers fail to achieve good tracking of the desired output, especially when the hydraulic actuator operates at various frequencies and pressures. The results demonstrate an excellent improvement in control action, without any increase in control effort, for the proposed approach. Finally, it can be concluded from the experimental results that the BELBIC is able to respond quickly to any disturbance and variation in the system parameters, showing a high degree of adaptability and robustness due to its online learning ability.

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Section: Introductionmentioning

confidence: 99%

Implementation of an Intelligent Adaptive Controller for an Electrohydraulic Servo System Based on a Brain Mechanism of Emotional Learning

Sadeghieh

Roshanian

Najafi

2012

International Journal of Advanced Robotic Systems

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“…Asymmetric hydraulic cylinders play very important roles in linear position control systems owing to advantages such as low cost, higher carrying capacity, simple structure and less workspace (2) . There are many works focusing on these types of systems, where the fluid power sent to the asymmetric hydraulic cylinder is regulated in various ways, i.e., with one four-way, three-position servo valve, one three-way, two-position servo valve (3)(4)(5) , or with digital modulated valves (6) . Generally, the ports of hydraulic cylinders are connected with valves.…”

Section: Introductionmentioning

confidence: 99%

Position Control of Asymmetric Hydraulic Cylinder with Two Chambers Connected

Lei

Wang

2011

JSDD

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This paper is concerned with position control of asymmetric hydraulic cylinder system. The hydraulic system consists of an asymmetric hydraulic cylinder with two chambers connected by an orifice, a two-position, two-way proportional valve and a load force. The system structure and the control principle were introduced in this paper. The influence of some structural parameters on the system dynamic performance was analyzed. The analysis and simulation results indicate that a two-position, two-way proportional valve can achieve position control of the asymmetric hydraulic cylinder system. A good dynamic performance can be achieved after using the control strategy proposed by this paper.

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“…The increasing numbers of works dealing with EHA system over the past decades involved a linear control, intelligent control, and nonlinear control approaches such as neural network (NN) [12], self-tuning fuzzy PID [13,14], model reference adaptive control (MRAC) [15,16], generalized predictive control (GPC) [17], and sliding mode control [18]. Most of tracking control system is typically necessary in minimizing gain and phase error from the output to the desired trajectory.…”

Section: Introductionmentioning

confidence: 99%

Simulation and Experimental Studies on Perfect Tracking Optimal Control of an Electrohydraulic Actuator System

Ghazali

Sam

Rahmat

et al. 2012

Journal of Control Science and Engineering

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This paper presents a perfect tracking optimal control for discrete-time nonminimum phase of electrohydraulic actuator (EHA) system by adopting a combination of feedback and feedforward controller. A linear-quadratic regulator (LQR) is firstly designed as a feedback controller, and a feedforward controller is then proposed to eliminate the phase error emerged by the LQR controller during the tracking control. The feedforward controller is developed by implementing the zero phase error tracking control (ZPETC) technique in which the main difficulty arises from the nonminimum phase system with no stable inverse. Subsequently, the proposed controller is performed in simulation and experimental studies where the EHA system is represented in discrete-time model that has been obtained using system identification technique. It also shows that the controller offers better performance as compared to conventional PID controller in reducing phase and gain error that typically occurred in positioning or tracking systems.

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Adaptive position control of electrohydraulic servo systems using ANN

Cited by 107 publications

References 7 publications

Implementation of an Intelligent Adaptive Controller for an Electrohydraulic Servo System Based on a Brain Mechanism of Emotional Learning

Implementation of an Intelligent Adaptive Controller for an Electrohydraulic Servo System Based on a Brain Mechanism of Emotional Learning

Position Control of Asymmetric Hydraulic Cylinder with Two Chambers Connected

Simulation and Experimental Studies on Perfect Tracking Optimal Control of an Electrohydraulic Actuator System

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