PID PARAMETERS OPTIMIZATION USING PSO TECHNIQUE FOR NONLINEAR ELECTRO HYDRAULIC ACTUATOR

2015 ◽  
Vol 77 (28) ◽  
Author(s):  
Siti Marhainis Othman ◽  
Mohd Fua’ad Rahmat ◽  
Sahazati Md. Rozali ◽  
Sazilah Salleh
Keyword(s):  
Particle Swarm Optimization ◽  
Simulation Study ◽  
Pid Control ◽  
Parameters Optimization ◽  
Model Parameters ◽  
Time Varying ◽  
Proportional Integral Derivative ◽  
Hydraulic Actuator ◽  
Swarm Optimization ◽  
Control Scheme

Electro-hydraulic actuator (EHA) system inherently suffers from uncertainties, nonlinearities and time- varying in its model parameters which cause the modeling and controller designs are more complicated. Proportional Integral Derivative (PID) control scheme has been proposed and the main problem with its application is to tune the parameters to its optimum values. This study will look into an optimization of PID parameters using particle swarm optimization (PSO). Simulation study has been done in Matlab and Simulink. 

Adaptive Self-Regulation PID Control of Course-Keeping for Ships

2020 ◽  
Vol 27 (1) ◽  
pp. 39-45
Author(s):  
Qiang Zhang ◽  
Zhongyu Ding ◽  
Meijuan Zhang
Keyword(s):  
Pid Control ◽  
Direct Method ◽  
Self Regulation ◽  
Model Parameters ◽  
Control Problems ◽  
Time Varying ◽  
Lyapunov Direct Method ◽  
Environmental Disturbances ◽  
Nonlinear Control Problems ◽  
Control Scheme

AbstractTo solve the nonlinear control problems of the unknown time-varying environmental disturbances and parametric uncertainties for ship course-keeping control, this paper presents an adaptive self-regulation PID (APID) scheme which can ensure the boundedness of all signals in the ship course-keeping control system by using the Lyapunov direct method. Compared with the traditional PID control scheme, the APID control scheme not only is independent of the model parameters and the unknown input, but also can regulate the gain of PID adaptively and resist time-varying disturbances well. Simulation results illustrate the effectiveness and the robustness of the proposed control scheme.

scholarly journals PID speed control of DC motor using meta-heuristic algorithms

2021 ◽  
Vol 12 (2) ◽  
pp. 822
Author(s):  
Bishwa Babu Acharya ◽  
Sandeep Dhakal ◽  
Aayush Bhattarai ◽  
Nawraj Bhattarai
Keyword(s):  
Particle Swarm Optimization ◽  
Transient Response ◽  
Pid Control ◽  
Heuristic Algorithms ◽  
Fitness Function ◽  
Optimal Solution ◽  
Absolute Error ◽  
Swarm Optimization ◽  
Unit Step ◽  
Control Scheme

This paper presents archimedes optimization algorithm(AOA) and dispersive flies optimization(DFO) to optimally tune gain parameters of PID control scheme in order to regulate DC motor’s speed. These suggested techniques tune the controller by the minimization of the fitness function represented by the integral of time multiplied by absolute error (ITAE). The modelling and simulation are carried out in MATLAB/Simulink. The transient response of unit step input obtained from AOA-PID-ITAE andDFO-PID-ITAE controllers were compared to those obtained from Ziegler-Nichols (ZN) method and particle swarm optimization(PSO). The results indicate that AOA-PID-ITAE and DFO-PID-ITAE are more efficient than ZN method and PSO in reducing rise time and settling time. Likewise, DFOconverge faster to the optimal solution with lower overshoot than AOA and PSO.

Models of a hybrid wheeled hopping self-balanced robot

2021 ◽  
pp. 095440622098419
Author(s):  
Qimin Li ◽  
Haibing Zeng ◽  
Long Bai ◽  
Zijian An
Keyword(s):  
Pid Control ◽  
Motion Pattern ◽  
System Dynamics Model ◽  
Dynamics Model ◽  
Swarm Optimization ◽  
Hopping Robot ◽  
Control Scheme ◽  
Hopping Robots ◽  
Hybrid Motion ◽  
Classical Control

Combining wheeled structure with hopping mechanism, this paper purposes a self-balanced hopping robot with hybrid motion pattern. The main actuator which is the cylindrical cam, optimized by particle swarm optimization (PSO), is equipped with the motor to control the hopping motion. Robotic system dynamics model is established and solved by Lagrangian method. After linearization, control characteristics of the system is obtained by classical control theory based on dynamics equations. By applying Adams and Matlab to simulate the system, hopping locomotion and self-balanced capability are validated respectively, and result shows that jump height can reach 750 mm theoretically. Then PID control scheme is developed and specific models of hardware and software are settled down accordingly. Finally, prototype is implemented and series of hopping experiments are conducted, showing that with different projectile angle, prototype can jump 550 mm in height and 460 mm in length, transcending majority of other existing hopping robots.

Method of Optimization for Target Localization Model Parameters Based on LSSVR

2011 ◽  
Vol 268-270 ◽  
pp. 934-939
Author(s):  
Xue Wen He ◽  
Gui Xiong Liu ◽  
Hai Bing Zhu ◽  
Xiao Ping Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Fitness Function ◽  
Particle Swarm ◽  
Parameters Optimization ◽  
Target Localization ◽  
Likelihood Method ◽  
Support Vector ◽  
Swarm Optimization ◽  
Localization Accuracy ◽  
Localization Method

Aiming at improving localization accuracy in Wireless Sensor Networks (WSN) based on Least Square Support Vector Regression (LSSVR), making LSSVR localization method more practicable, the mechanism of effects of the kernel function for target localization based on LSSVR is discussed based on the mathematical solution process of LSSVR localization method. A novel method of modeling parameters optimization for LSSVR model using particle swarm optimization is proposed. Construction method of fitness function for modeling parameters optimization is researched. In addition, the characteristics of particle swarm parameters optimization are analyzed. The computational complexity of parameters optimization is taken into consideration comprehensively. Experiments of target localization based on CC2430 show that localization accuracy using LSSVR method with modeling parameters optimization increased by 23%~36% in compare with the maximum likelihood method(MLE) and the localization error is close to the minimum with different LSSVR modeling parameters. Experimental results show that adapting a reasonable fitness function for modeling parameters optimization using particle swarm optimization could enhance the anti-noise ability significantly and improve the LSSVR localization performance.

Sign in / Sign up

Export Citation Format

Share Document