The Optimization of the PID Controller Using the Least Squares Method and the Improved Lbest PSO Algorithm

2013 ◽  
Vol 397-400 ◽  
pp. 1296-1303 ◽  
Author(s):  
Chuan Gui Yang ◽  
Zhao Jun Yang ◽  
Fei Chen ◽  
Yan Zhu ◽  
Ying Nan Kan ◽  
...  
Keyword(s):  
Least Squares ◽  
Pid Controller ◽  
Least Squares Method ◽  
Dynamic Performance ◽  
Pso Algorithm ◽  
Pid Tuning ◽  
Loading System ◽  
Hydraulic Servo ◽  
Tuning Scheme ◽  
Better Than

A self-adaptive PID tuning scheme is presented for the electro-hydraulic servo loading system. It requires the least squares method to identify the parameters of the transfer function of the electro-hydraulic servo loading system and utilizes the improved lbest PSO algorithm to optimize the PID controller. The scheme can provide the optimal PID parameters so that the dynamic performance and stability of the electro-hydraulic servo loading system are improved. Results show the fact that the dynamic performance and stability of the system are improved by the scheme. And in terms of optimization of PID controller, the improved lbest PSO algorithm is better than the lbest PSO algorithm and Ziegler-Nichols method.

scholarly journals GM(1,1;λ) with Constrained Linear Least Squares

Axioms ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 278
Author(s):  
Ming-Feng Yeh ◽  
Ming-Hung Chang
Keyword(s):  
Least Squares ◽  
Least Squares Method ◽  
Model Fitting ◽  
Ordinary Least Squares ◽  
Linear Least Squares ◽  
Unknown Parameters ◽  
Constrained Problems ◽  
Linear Least Squares Problems ◽  
Ordinary Least Squares Method ◽  
Better Than

The only parameters of the original GM(1,1) that are generally estimated by the ordinary least squares method are the development coefficient a and the grey input b. However, the weight of the background value, denoted as λ, cannot be obtained simultaneously by such a method. This study, therefore, proposes two simple transformation formulations such that the unknown parameters, and can be simultaneously estimated by the least squares method. Therefore, such a grey model is termed the GM(1,1;λ). On the other hand, because the permission zone of the development coefficient is bounded, the parameter estimation of the GM(1,1) could be regarded as a bound-constrained least squares problem. Since constrained linear least squares problems generally can be solved by an iterative approach, this study applies the Matlab function lsqlin to solve such constrained problems. Numerical results show that the proposed GM(1,1;λ) performs better than the GM(1,1) in terms of its model fitting accuracy and its forecasting precision.

Landsman Converter Based Particle Swarm Optimization Technique

2017 ◽  
Vol 8 (3) ◽  
pp. 619
Author(s):  
Ramesh P. ◽  
V. Mathivanan
Keyword(s):  
Particle Swarm Optimization ◽  
Pid Controller ◽  
Dynamic Performance ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Control Technique ◽  
Swarm Optimization ◽  
Particle Swarm Optimization Technique ◽  
Comparative Results

This paper proposes a novel control technique for landsman converter using particle swarm optimization. The controller parameters are optimized by pso algorithm,the proposed algorithm is compared with pid controller and the comparative results are presented. Simulation results shows the dynamic performance of pso controller. landsman converter reduction in output voltage ripple in the order of mV along with reduced settling time as compared to the conventional pid controller . The simulated results are executed in MATLAB/SIMULINK.

Graphic IMC-PID tuning based on maximum sensitivity for uncertain systems

2018 ◽  
Vol 41 (8) ◽  
pp. 2196-2204 ◽  
Author(s):  
Minghui Chu ◽  
Chi Xu ◽  
Jizheng Chu
Keyword(s):  
Pid Controller ◽  
Uncertain Systems ◽  
Dynamic Performance ◽  
Uncertain System ◽  
Internal Model Control ◽  
Maximum Sensitivity ◽  
Tuning Method ◽  
Pid Tuning ◽  
Gain Margin ◽  
Model Control

Obtaining all feasible parameters of the proportional-integral-differential (PID) controller is the key goal in uncertain systems. This paper proposes a graphical tuning method based on an internal model control (IMC) strategy for uncertain systems with time delay. Specifically, the Kharitonov theorem is introduced first to simplify the uncertain system into 32 polynomials. Then, for each polynomial, the IMC structure is applied to reduce the tuning parameters of the PID controller in order to rapidly determine the controller parameters. Finally, the maximum sensitivity (Ms) is used to further guarantee the controlled system with a certain robustness and dynamic performance, which can portray constant gain margin and phase margin boundaries, and can even determine the range of parameters of the proposed IMC filter. Three example results from simulations are presented to demonstrate the effectiveness and applicability of the proposed method.

Research and Control for Inertia of Automobile Steering Test System

2014 ◽  
Vol 541-542 ◽  
pp. 737-741
Author(s):  
Xiang Lu ◽  
Yan Jie Luo ◽  
Yun Fei Mai
Keyword(s):  
Pid Controller ◽  
Dynamic Performance ◽  
Technical Specification ◽  
Test System ◽  
Fuzzy Pid Controller ◽  
Technical Requirements ◽  
Loading System ◽  
Negative Effect ◽  
And Control ◽  
System Transfer Function

This paper is focused on the study of pressure shocks caused by inertia of automobile steering gear. The system transfer function mathematical model was established. In order to meet the system technical specification, a feed-forward compensator was designed to eliminate the interference force; a fuzzy PID controller was devised to accelerate responding speed, eliminating oscillation and improve the dynamic performance. The simulation results indicate that the negative effect of inertia can be well overcome and passive loading system works steadily and satisfies technical requirements.

scholarly journals A Least Squares Method for Variance Estimation in Heteroscedastic Nonparametric Regression

2014 ◽  
Vol 2014 ◽  
pp. 1-14
Author(s):  
Yuejin Zhou ◽  
Yebin Cheng ◽  
Tiejun Tong
Keyword(s):  
Least Squares ◽  
Nonparametric Regression ◽  
Variance Estimation ◽  
Least Squares Method ◽  
Asymptotic Properties ◽  
Error Variance ◽  
Simulation Studies ◽  
Least Squares Estimators ◽  
Slope Estimator ◽  
Better Than

Interest in variance estimation in nonparametric regression has grown greatly in the past several decades. Among the existing methods, the least squares estimator in Tong and Wang (2005) is shown to have nice statistical properties and is also easy to implement. Nevertheless, their method only applies to regression models with homoscedastic errors. In this paper, we propose two least squares estimators for the error variance in heteroscedastic nonparametric regression: the intercept estimator and the slope estimator. Both estimators are shown to be consistent and their asymptotic properties are investigated. Finally, we demonstrate through simulation studies that the proposed estimators perform better than the existing competitor in various settings.

scholarly journals Asymptotic normality of a simple linear EV regression model with martingale difference errors

Filomat ◽  
2014 ◽  
Vol 28 (9) ◽  
pp. 1817-1825
Author(s):  
Guo-Liang Fan ◽  
Tian-Heng Chen
Keyword(s):  
Regression Model ◽  
Least Squares ◽  
Measurement Errors ◽  
Least Squares Method ◽  
Error Variance ◽  
Martingale Difference ◽  
Errors In Variables ◽  
Ev Regression Model ◽  
Biased Estimators ◽  
Better Than

This paper considers the estimation of a linear EV (errors-in-variables) regression model under martingale difference errors. The usual least squares estimations lead to biased estimators of the unknown parametric when measurement errors are ignored. By correcting the attenuation we propose a modified least squares estimator for a parametric component and construct the estimators of another parameter component and error variance. The asymptotic normalities are also obtained for these estimators. The simulation study indicates that the modified least squares method performs better than the usual least squares method.

A Nonlinear PID Controller for Electro-Hydraulic Servo System Based on PSO Algorithm

2011 ◽  
Vol 141 ◽  
pp. 157-161 ◽  
Author(s):  
Ji Peng Chen ◽  
Bao Chun Lu ◽  
Fan Fan ◽  
Shi Chun Zhu ◽  
Jian Xin Wu
Keyword(s):  
Pid Control ◽  
Pid Controller ◽  
Servo System ◽  
Pso Algorithm ◽  
Nonlinear Functions ◽  
Proportional Integral ◽  
Nonlinear Pid Controller ◽  
Hydraulic Servo ◽  
Nonlinear Pid Control ◽  
Hydraulic Servo System

Linear PID controller is adopted by electro-hydraulic servo system widely. The proportional, integral and derivative coefficients of a linear controller are fixed which brings the contradiction in speediness and overshoot. This paper designs a series of nonlinear functions which are used for nonlinear PID control for electro-hydraulic servo system. The proportional, integral and derivative parts of this nonlinear PID controller adjust themselves according to the error. In order to determine parameters of the nonlinear functions, PSO Algorithm and ITAE guideline are applied in the paper. Results of simulation indicate that the nonlinear PSO-PID controller using the functions proposed in this paper presents a better dynamic response than the linear PID controller.

The Design of Expert PID Parameter Optimized by Genetic Algorithm

2011 ◽  
Vol 130-134 ◽  
pp. 3091-3094
Author(s):  
Jia Tang Cheng ◽  
Wei Xiong ◽  
Li Ai
Keyword(s):  
Genetic Algorithm ◽  
Parameter Optimization ◽  
Pid Controller ◽  
Dynamic Performance ◽  
Parameter Tuning ◽  
Expert Pid ◽  
Parameter Values ◽  
Better Than

Aiming at the problems Expert PID parameter tuning for time-consuming, and the results are not necessarily the best. In this paper, genetic algorithm is introduced to the parameter optimization, finally get a set of optimal PID parameter values. In comparison with simulated experiments, the results show that the performance of the Designed to optimize the performance of optimization expert PID controller is better than conventional controller, can achieve good dynamic performance.

Fuzzy Self-Tuning PID Controller in SEHS

2012 ◽  
Vol 214 ◽  
pp. 765-770
Author(s):  
Lan Sun
Keyword(s):  
Pid Controller ◽  
Fuzzy Controller ◽  
Fuzzy Pid ◽  
Motor Speed ◽  
Fuzzy Pid Controller ◽  
Control Scheme ◽  
Hydraulic Servo ◽  
Self Tuning ◽  
Hybrid Fuzzy Pid ◽  
Better Than

Because of the existing hybrid fuzzy PID controller does not perform, using electric hydraulic servo system application (SEHS). Therefore, when the system parameters change will require a new adjustment of PID controller variable. Therefore, a hybrid fuzzy and fuzzy self-tuning PID control was put forward. With this control scheme was divided into two parts, and the fuzzy controller and fuzzy self-tuning PID controller. Fuzzy controller is used to control the output of the system of the values of the system away from target value. We proved that the performance of the control scheme through the experiment of the motor speed control SEHS. The experimental results show that the proposed a hybrid fuzzy PID controller and fuzzy self-tuning effect is better than that of a hybrid fuzzy and PID controller.

Research on Modeling and Parameter Identification of Electro-Hydraulic Servo Loading System

2014 ◽  
Vol 889-890 ◽  
pp. 970-977
Author(s):  
Song Wang ◽  
Chuan Gui Yang ◽  
Fei Chen ◽  
Zhao Jun Yang ◽  
Zhuang Tan ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Parameter Identification ◽  
Test Bench ◽  
Least Square Method ◽  
Pso Algorithm ◽  
Least Square ◽  
Identification Accuracy ◽  
Loading System ◽  
Hydraulic Servo ◽  
The Mathematical Model

In order to improve the mathematical models accuracy of the electro-hydraulic servo loading system from the high-speed motorized spindle reliability test bench. This paper establishes the mathematical model based on the dynamic characteristics of the test bench, then establishes discrete mathematical model of the system based on the Z-transform, and finally uses particle swarm optimization (PSO) algorithm to identify the parameters of the discrete model. Additionally, the least square method is applied to identify the parameters of the model for measuring the PSO algorithm parameter identification capability in our paper. The experimental results show that the mathematical model, identified by the PSO algorithm, can simulate the loading process very well under the strong interference signals, and the result is better than that gotten by the least square method, which proves that the PSO algorithm has high identification accuracy and better capability in parameters identification .

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