The Effects of Weightage Values with Two Objective Functions in iPSO for Electro-Hydraulic Actuator System

Abstract

In this paper, the Proportional-Integral-Derivative (PID) controller with improved Particle Swarm Optimization (iPSO) algorithm is proposed for the positioning control of nonlinear Electro-Hydraulic Actuator (EHA) system. PID controller is chosen to control the EHA system due to its popularity in industrial applications. The PID controller parameters will be tuned by using the iPSO algorithm to get the lowest overshoot percentage and steady-state error. The conventional PSO algorithm has only one objective function to get the optimum parameters. However, this is not enough to increase the control performance of the EHA system. Therefore, an improved Particle Swarm Optimization (iPSO) that includes the mean error and overshoot percentage as the two objective functions is proposed in this paper. The most popular method in PSO that included two objective functions is Linear Weight Summation (LWS). In this method, the two objective functions are combined with certain weightage into one equation to give the best control performance. This paper focuses on determining the suitable weightage between these two objective functions so that the EHA system can produce the best control performance with less overshoot and less error. Overshoot percentage and steady-state error are used to indicate the best control performance. The results showed that EHA system can perform better by using suitable weightage between the mean error and overshoot percentage.

Fuzzy Feedback Control for Electro-Hydraulic Actuators

“…Therefore, Shern et al, conducted a study to compare PID parameter selection based on several techniques such as the Ziegler-Nichols tuning method, conventional Particle Swarm Optimization technique, and Priority-based Fitness Particle Swarm Optimization (PFPSO). The obtained results showed that all methods achieve specific effects, in which PFPSO outperformed the rest [23]. In another attempt to optimize PID parameters, Tajjdin et al, used the Nelder-Mead method to tune these parameters to their optimum values [24].…”

Investigation on dynamic mathematical model and control method of flue gas heat exchange system

“…The intelligent control technology was applied to the controller parameter tuning process to overcome the large time delay, strong coupling and large disturbance in the engineering. However, the model of the flue gas heat exchange system with phase change is complex, the control loop is long and there are many interference factors in that (Edaris and Abdul-Rahman, 2016;Shern et al, 2019;Veerasamy et al, 2019;Zhang and Yuan, 2020). Therefore, the cascade PID-P temperature control method is applied to control the flue Flue gas heat exchange system gas heat exchange system with phase change.…”

“…The particle swarm optimization algorithms is an intelligent optimization algorithm based on swarm and widely used in controller parameter optimization process (AlMa'aitah et al, 2019;Eltag et al, 2019;Hasan and Rashad, 2019). In this algorithm, the system is initialized to a set of random solutions and the optimal value will be searched iteratively (Edaris and Abdul-Rahman, 2016;Shern et al, 2019;Veerasamy et al, 2019;Zhang and Yuan, 2020). The fitness function in particle swarm optimization algorithm is an index to evaluate the performance of each particle which provides a basis for the selection and update of swarm extremum (Jaafar et al, 2018).…”

“…, 2019; Eltag et al ., 2019; Hasan and Rashad, 2019). In this algorithm, the system is initialized to a set of random solutions and the optimal value will be searched iteratively (Edaris and Abdul-Rahman, 2016; Shern et al. , 2019; Veerasamy et al.…”