Multi-objective robust PID controller tuning using two lbests multi-objective particle swarm optimization

Zhao, Shi-Zheng; Iruthayarajan, M. Willjuice; Baskar, S.; Suganthan, Ponnuthurai Nagaratnam

doi:10.1016/j.ins.2011.04.003

Cited by 105 publications

(37 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…However, instead of time-domain properties the integral of the absolute value of the derivative the control signal and integral of the absolute value of the error is considered as objectives. This idea is applied to twin rotor MIMO system (as a PI controller tuning algorithm similar study evaluated on [7] and for aircraft in [8]) and results also support the better performance of the multiobjective tuning methodology. Also in [9], two similar objectives which are integral time absolute error and control effort is taken to tune PID controller for the plastic injection molding process, and in [10] same objectives are evaluated on weighted sum scalarization function.…”

Section: Introductionmentioning

confidence: 66%

Multiobjective PID controller design for active suspension system: scalarization approach

Altınöz¹

2018

Int. J. Optim. Control, Theor. Appl. (IJOCTA)

View full text Add to dashboard Cite

In this study, the PID tuning method (controller design scheme) is proposed for a linear quarter model of active suspension system installed on the vehicles. The PID tuning scheme is considered as a multiobjective problem which is solved by converting this multiobjective problem into single objective problem with the aid of scalarization approaches. In the study, three different scalarization approaches are used and compared to each other. These approaches are called linear scalarization (weighted sum), epsilon-constraint and Bensons methods. The objectives of multiobjective optimization are selected from the time-domain properties of the transient response of the system which are overshoot, rise time, peak time and error (in total there are four objectives). The aim of each objective is to minimize the corresponding property of the time response of the system. First, these four objective is applied to the scalarization functions and then single objective problem is obtained. Finally, these single objective problems are solved with the aid of heuristic optimization algorithms. For this purpose, four optimization algorithms are selected, which are called Particle Swarm Optimization, Differential Evolution, Firefly, and Cultural Algorithms. In total, twelve implementations are evaluated with the same number of iterations. In this study, the aim is to compare the scalarization approaches and optimization algorithm on active suspension control problem. The performance of the corresponding cases (implementations) are numerically and graphically demonstrated on transient responses of the system.

show abstract

Section: Introductionmentioning

confidence: 66%

Multiobjective PID controller design for active suspension system: scalarization approach

Altınöz¹

2018

Int. J. Optim. Control, Theor. Appl. (IJOCTA)

View full text Add to dashboard Cite

show abstract

Section: Robustness With Respect To Model Uncertainties and Disturbanmentioning

confidence: 99%

“…Conventional methods like Ziegler Nichols and Cohen -Coon methods failed to produce robust controller designs with guaranteed robustness degree. Hence stability of the control system gets affected [31].where J 1 = robustness with respect to model uncertainties and disturbance attenuation J a = model uncertainty in ∞ H normsubjected to the constraints in eqns. (4) and (5).…”

mentioning

confidence: 99%

Tuning of Robust PID Controller with Filter for SISO System Using Evolutionary Algorithms

Muniraj¹,

Siluvairaj²,

Ramaveerapathiran³

2017

STUD INFORM CONTROL

View full text Add to dashboard Cite

This paper discusses the performance comparison of Evolutionary Algorithm techniques with a view to tuning the desired design parameters of robust PID controller with filter. The design parameters of robust PID controller such as proportional gain K P , integral time constant T i , derivative time constant T d and filter time constant t d /N for Single Input Single Output (SISO) system are computed using Real coded Genetic Algorithm (RGA), Differential Evolution (DE) algorithm and Particle Swarm Optimization (PSO) algorithm. The design parameters are optimized using the statistical measures in twenty independent simulation runs. The computed design parameters obtained using Evolutionary Algorithms are used to determine the performance specifications of the Robust PID controller. The performance specifications determined are robustness with respect to model uncertainties and disturbance attenuation, set point tracking, load disturbance rejection and control energy. The test systems used to ascertain the performance specifications are Phase Locked Loop (PLL) system with motor control and Magnetic Levitation system (MLS). For PLL system, the output response obtained using the computed design parameters by RGA algorithm has proved to be better than DE and PSO. For MLS system, the output response obtained using the computed design parameters by DE algorithm has been better than PSO and RGA. meet out the performance specifications of the given system mainly depends on the tuning of the controller .Since the system has nonlinear plant and various uncertainties the tuning of the robust PID controller becomes difficult. This leads to poor tuning. The reasons of the poor tuning is due to a) lack of knowledge among operators and commissioning personnel b) variety of PID structures that leads to error during tuning c) in some cases the nature of tuning method do not meet the requirement of the process involved [3]. Out of the installed PID controllers in industry, nearly one third controllers are tuned manually and remaining two third are tuned automatically. But the automatically tuned PID controllers are not effective [7].To overcome these limitations optimization algorithms are used for the tuning of robust PID controller. In the past many researchers have used different optimization algorithm for tuning the parameters of robust PID controller. But the parameter tuned to meet out the performance specifications of the system is different in each case. Many researchers have considered set In this paper, Evolutionary algorithm is used to determine the optimal parameters for the robust optimal controller. Evolutionary Algorithms used are Particle Swarm Optimization (PSO), Real coded Genetic Algorithm (RGA) and Differential Evolution (DE). The main focus of this paper is to determine the design parameters of robust PID controller using evolutionary algorithm such that it meet out the system performance specifications like robustness with respect to model uncertainties and disturbance attenuation, set point tracking,...

show abstract

“…To overcome this challenge, tuning of decentralized PI/PID controller by using intelligent optimization techniques such as PSO and GA [11], [12], [13], [14], [15], [16], [17], [18], [19] and [20] are used due to their less complexity, high performance and easy implementation. Genetic algorithms (GAs) belong to the larger class of evolutionary algorithms, which generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover [21].…”

Section: Controlling Of Chemical Processes (Which Are Basically Multimentioning

confidence: 99%

An Intelligent Control System Design for an Evaporator based on Particle Swarm Optimization

Abdel-Halim¹,

Othman²,

Sakr³

et al. 2017

IJCA

View full text Add to dashboard Cite

The main contribution of this paper is aimed to design and implementation of an intelligent level controller and intelligent 2×2 decentralized PI controller and a lead compensator for the forced circulation evaporator by using PSO strategy. The most important thing to guarantee the safe operation of the forced circulation evaporator, without damaging the installed equipment, is obtaining optimal controllers for the evaporator operating pressure and the level of liquid inside the separator part. Also the percent of the concentration of the non-volatile in the solution must be effectively controlled to required limits. PSO algorithm is implemented in MATLAB and is compared to GA strategy for design and implementation of optimal controllers for the evaporator system by minimizing the summation of the characteristics of unit step response. Also computer simulation results are compared to the different two cost functions methods by analyzing the performance, stability and robustness with respect to variation of the evaporator control system.

show abstract

Multi-objective robust PID controller tuning using two lbests multi-objective particle swarm optimization

Cited by 105 publications

References 36 publications

Multiobjective PID controller design for active suspension system: scalarization approach

Multiobjective PID controller design for active suspension system: scalarization approach

Tuning of Robust PID Controller with Filter for SISO System Using Evolutionary Algorithms

An Intelligent Control System Design for an Evaporator based on Particle Swarm Optimization

Contact Info

Product

Resources

About