An Integrator Backstepping Position Control of Electro-Hydraulic Servo System Based on Particle Swarm Optimization

2014 ◽  
Author(s):  
Ermiyas Assegu ◽  
Hamid Roozbahani ◽  
Heikki Handroos
Keyword(s):  
Particle Swarm Optimization ◽  
Position Control ◽  
Particle Swarm ◽  
Servo System ◽  
Test Bed ◽  
Hydraulic Actuator ◽  
Swarm Optimization ◽  
Integrator Backstepping ◽  
Hydraulic Servo ◽  
Hydraulic Servo System

This paper presents a position control of a single rod electro-hydraulic actuator based on an integrator backstepping approach and tuned controller gain parameters by using a Particle Swarm Optimization (PSO). The aim of this paper is to develop optimal Back Stepping Controller (BSC) architecture on a nonlinear Electro Hydraulic Servo System (EHSS) of asymmetric cylinder to improve the position control performance. The influences of getting the parameters on the control law are analyzed in order to establish some rules of optimization. This can be determined automatically and intelligently by minimizing Integral Square Error (ISE). The proposed controller is simulated and then implemented to the experimental test bed to track the desired signal and test the limits of its performance. The simulation and experimental results are given to demonstrate the effectiveness of the proposed controller.

Parameter Identification for Nonlinear Electro-Hydraulic Servo System Based on Multi-Particle Swarm Optimization

2013 ◽  
Vol 325-326 ◽  
pp. 1245-1248 ◽  
Author(s):  
Hong Quan Ming ◽  
Lei Luo ◽  
Zheng Ming Wang ◽  
Ying Jiang
Keyword(s):  
Particle Swarm Optimization ◽  
Parameter Identification ◽  
Steam Turbine ◽  
Dead Zone ◽  
Particle Swarm ◽  
Servo System ◽  
Identification Algorithm ◽  
Swarm Optimization ◽  
Hydraulic Servo ◽  
Hydraulic Servo System

In the electro-hydraulic servo system for steam turbine, there are many components with nonlinear behaviors. It is difficult to identify these nonlinear parameters with regular identification methods. Particle swarm optimization (PSO) is a relatively new optimization algorithm which has been applied to a variety of problems. However, it may easily get trapped in local optima when solving complex problems. In this study, a nonlinear model including dead zone is established first and a multi-particle swarm optimization (MPSO) method based on double-layer evolution is studied in detail. Then the parameter identification with this optimization method for the electro-hydraulic servo system of steam turbine are discussed in the paper. Moreover, numerical simulation demonstrates that the accuracy of the proposed parameter identification algorithm can be guaranteed.

scholarly journals Position Control of Ultrasonic Motor Using IMC-PID Combined with Tribes Type NN Algorithm

2011 ◽  
Vol 2-3 ◽  
pp. 12-17
Author(s):  
Sheng Lin Mu ◽  
Kanya Tanaka
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Pid Control ◽  
Position Control ◽  
Back Propagation ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Ultrasonic Motor ◽  
Swarm Optimization

In this paper, we propose a novel scheme of IMC-PID control combined with a tribes type neural network (NN) for the position control of ultrasonic motor (USM). In this method, the NN controller is employed for tuning the parameter in IMC-PID control. The weights of NN are designed to be updated by the tribes-particle swarm optimization (PSO) algorithm. This method makes it possible to compensate for the characteristic changes and nonlinearity of USM. The parameter-free tribes-PSO requires no information about the USM beforehand; hence its application overcomes the problem of Jacobian estimation in the conventional back propagation (BP) method of NN. The effectiveness of the proposed method is confirmed by experiments.

System identification and robust position control for electro-hydraulic servo system using hybrid model predictive control

2017 ◽  
Vol 24 (18) ◽  
pp. 4145-4159 ◽  
Author(s):  
Hai-Bo Yuan ◽  
Hong-Cheol Na ◽  
Young-Bae Kim
Keyword(s):  
System Identification ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Position Control ◽  
Servo System ◽  
Model Parameters ◽  
Integral Control ◽  
Hybrid Controller ◽  
Hydraulic Servo ◽  
Hydraulic Servo System

This paper examined system identification using grey-box model estimation and position-tracking control for an electro-hydraulic servo system (EHSS) using hybrid controller composed of proportional-integral control (PIC) and model predictive control (MPC). The nonlinear EHSS model is represented by differential equations. We identify model parameters and verify their accuracy against experimental data in MATLAB to evaluate the validity of this mathematical model. To guarantee improved performance of EHSS and precision of cylinder position, we propose a hybrid controller composed of PIC and MPC. The controller is designed using the Control Design and Simulation module in the Laboratory Virtual Instrumentation Engineering Workbench (LabVIEW). A LabVIEW-based experimental rig is developed to apply the proposed controller in real time. Then, the validity and performance superiority of the hybrid controller were confirmed by comparing them with the MPC and PIC results. Results of real-life experiments show improved robustness and dynamic and static properties of EHSS.

Optimum Setting of DC Drives PID Controller via Particle Swarm Optimization

2012 ◽  
Vol 157-158 ◽  
pp. 88-93 ◽  
Author(s):  
Guang Hui Chang ◽  
Jie Chang Wu ◽  
Chao Jie Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Pid Controller ◽  
Position Control ◽  
Particle Swarm ◽  
Dc Motor ◽  
Motor Drive ◽  
Proportional Integral Derivative ◽  
Stable Convergence ◽  
Swarm Optimization ◽  
Simulation Results

In this paper, an intelligent controller of PM DC Motor drive is designed using particle swarm optimization (PSO) method for tuning the optimal proportional-integral-derivative (PID) controller parameters. The proposed approach has superior feature, including easy implementation, stable convergence characteristics and very good computational performances efficiency.To show the validity of the PID-PSO controller, a DC motor position control case is considered and some simulation results are shown. The DC Motor Scheduling PID-PSO controller is modeled in MATLAB environment.. It can be easily seen from the simulation results that the proposed method will have better performance than those presented in other studies.

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