scholarly journals Design of Decentralized Fractional Order PIʎ Controller for Pilot Plant Binary Didtillation Column

2020 ◽  
Vol 5 (1) ◽  
pp. 33-39
Author(s):  
KHALFA BETTOU ◽  
ABDELFATEH CHAREF
Keyword(s):  
Fractional Order ◽  
Fractional Integration ◽  
Pso Algorithm ◽  
Pi Controller ◽  
Integral Criterion ◽  
Control Loops ◽  
Tuning Method ◽  
Control Quality ◽  
Basic Ideas ◽  
Tuning Methods

This paper presents the application of fractional order operators to improve the control quality of multivariable systems. The basic ideas of this tuning method are based, in the first place, on the existed tuning methods for setting the parameters of the decentralized fractional order PIʎ controller for ʎ=1, which means setting the parameters of the classical decentralized PI controller, and the minimum integral criterion by using Particle Swarm Optimization (PSO) algorithm for setting the fractional integration action order ʎ. The integral criterion is formulated to improve the dynamic response of the system, while causing a good decoupling between control loops. The Distillation Column, which is a multivariable system with two inputs and two outputs (TITO), in a decentralized control structure, is analyzed. Simulation results are presented to show the control quality improvement of this proposed decentralized fractional order PIʎ controller tuning method compared to the decentralized PI controller tuned using any existed tuning method.

scholarly journals Comparison of cascade P-PI controller tuning methods for PMDC motor based on intelligence techniques

2022 ◽  
Vol 12 (1) ◽  
pp. 1
Author(s):  
Kareem Ghazi Abdulhussein ◽  
Naseer Majeed Yasin ◽  
Ihsan Jabbar Hasan
Keyword(s):  
Classical Method ◽  
Pso Algorithm ◽  
Pi Controller ◽  
Load Effect ◽  
Ga Algorithm ◽  
Tuning Methods ◽  
Parameter Values ◽  
Permanent Magnet Dc Motor ◽  
Tracking Trajectory ◽  
Best Parameters

In this paper, there are two contributions: The first contribution is to design a robust cascade P-PI controller to control the speed and position of the permanent magnet DC motor (PMDC). The second contribution is to use three methods to tuning the parameter values for this cascade controller by making a comparison between them to obtain the best results to ensure accurate tracking trajectory on the axis to reach the desired position. These methods are the classical method (CM) and it requires some assumptions, the genetic algorithm (GA), and the particle swarm optimization algorithm (PSO). The simulation results show the system becomes unstable after applying the load when using the classical method because it assumes cancellation of the load effect. Also, an overshoot of about 3.763% is observed, and a deviation from the desired position of about 12.03 degrees is observed when using the GA algorithm, while no deviation or overshoot is observed when using the PSO algorithm. Therefore, the PSO algorithm has superiority as compared to the other two methods in improving the performance of the PMDC motor by extracting the best parameters for the cascade P-PI controller to reach the desired position at a regular speed.

Optimal PI Controller Tuning Based on ITAE Criterion for Low-Order Systems with Large Time Delay

2013 ◽  
Vol 411-414 ◽  
pp. 1716-1719
Author(s):  
Feng Ping Pan ◽  
Hong Kai Liao ◽  
Jia Luo ◽  
Xi Zhang
Keyword(s):  
Genetic Algorithm ◽  
Time Delay ◽  
Large Time ◽  
Pi Controller ◽  
Stable Region ◽  
Controller Tuning ◽  
Nonlinear Fitting ◽  
Tuning Method ◽  
Tuning Methods ◽  
Low Order

For low order process with large time delay, a kind of optimal PI controller tuning method is proposed based on generalized Hermite-Biehler theorem and Genetic Algorithm. Firstly, the stable region of PI controller is obtained by using the generalized Hermite-Biehler theorem. Then the optimum parameters are selected from this region based on ITAE criterion and genetic algorithm. A tuning formula is obtained by nonlinear fitting of optimization result, which has the capability to cover the variety of normalized time delays up to 100. Simulation of Monte-Carlo stochastic experiment indicates that the proposed method has good performance robustness when parameter uncertainty occurs, compared with other four PI tuning methods.

scholarly journals Experimental Validation of a Novel Auto-Tuning Method for a Fractional Order PI Controller on an UR10 Robot

Algorithms ◽  
2018 ◽  
Vol 11 (7) ◽  
pp. 95 ◽  
Author(s):  
Cristina Muresan ◽  
Cosmin Copot ◽  
Isabela Birs ◽  
Robin De Keyser ◽  
Steve Vanlanduit ◽  
...  
Keyword(s):  
Fractional Order ◽  
Experimental Validation ◽  
Pi Controller ◽  
Tuning Method ◽  
Auto Tuning ◽  
Fractional Order Pi Controller

scholarly journals A fractional order parallel control structure tuned with meta-heuristic optimization algorithms for enhanced robustness

2019 ◽  
Vol 70 (1) ◽  
pp. 16-24 ◽  
Author(s):  
Vishal Goyal ◽  
Puneet Mishra ◽  
Aasheesh Shukla ◽  
Vinay Kumar Deolia ◽  
Aarti Varshney
Keyword(s):  
Fractional Order ◽  
Control Structure ◽  
Search Algorithm ◽  
Absolute Error ◽  
Industrial Control ◽  
Control Loops ◽  
Parallel Control ◽  
Tuning Methods ◽  
Regulatory Problems ◽  
Order Parallel

Abstract This paper studies an improved fractional order parallel control structure (FOPCS) for enhancing the robustness in an industrial control loop having a first order process with dead time along with its tuning aspects. Since inclusion of fractional order calculus also increase the number of parameters to be determined for a particular control loops, tuning becomes an essential task. Four different tuning methods are considered to optimize the gains of parallel control structure (PCS) and FOPCS. Integral of time weighted absolute error for servo and regulatory problems along with overshoot value have been considered for performance evaluation. Extensive simulation studies including change in setpoint and mismatch in processmodel parameters have been carried out. On the basis of these studies, it was observed that FOPCS tuned by backtracking search algorithm, outperformed all other controllers in terms of considered performance measures.

Fractional-order PI controller with relay auto-tuning method

2013 ◽  
Author(s):  
Mazidah Tajjudin ◽  
Siti Fatehah Tahir ◽  
Mohd Hezri Fazalul Rahiman ◽  
Norhashim Mohd Arshad ◽  
Ramli Adnan
Keyword(s):  
Fractional Order ◽  
Pi Controller ◽  
Tuning Method ◽  
Auto Tuning ◽  
Fractional Order Pi Controller

Using Swarm Intelligence for Optimization of Parameters in Approximations of Fractional-Order Operators

2012 ◽  
pp. 194-222 ◽  
Author(s):  
Guido Maione ◽  
Antonio Punzi ◽  
Kang Li
Keyword(s):  
Fractional Order ◽  
Rational Approximation ◽  
Building Blocks ◽  
Pso Algorithm ◽  
Control Loops ◽  
Phase Plot ◽  
Pole Location ◽  
Zeros And Poles

This chapter applies Particle Swarm Optimization (PSO) to rational approximation of fractional order differential or integral operators. These operators are the building blocks of Fractional Order Controllers, that often can improve performance and robustness of control loops. However, the implementation of fractional order operators requires a rational approximation specified by a transfer function, i.e. by a set of zeros and poles. Since the quality of the approximation in the frequency domain can be measured by the linearity of the Bode magnitude plot and by the “flatness” of the Bode phase plot in a given frequency range, the zeros and poles must be properly set. Namely, they must guarantee stability and minimum-phase properties, while enforcing zero-pole interlacing. Hence, the PSO must satisfy these requirements in optimizing the zero-pole location. Finally, to enlighten the crucial role of the zero-pole distribution, the outputs of the PSO optimization are compared with the results of classical schemes. The comparison shows that the PSO algorithm improves the quality of the approximation, especially in the Bode phase plot.

scholarly journals Parametric and Nonparametric PI Controller Tuning Method for Integrating Processes Based on Magnitude Optimum

2020 ◽  
Vol 10 (4) ◽  
pp. 1443 ◽  
Author(s):  
Tomaž Kos ◽  
Mikuláš Huba ◽  
Damir Vrančić
Keyword(s):  
Time Domain ◽  
Power Plants ◽  
Closed Loop ◽  
Pi Controller ◽  
Weighting Factor ◽  
Closed Loop Control ◽  
Controller Tuning ◽  
Loop Control ◽  
Tuning Method ◽  
Tuning Methods

Integrating systems are frequently encountered in the oil industry (oil–water–gas separators, distillation columns), power plants, paper-production plants, polymerisation processes, and in storage tanks. Due to the non-self-regulating character of the processes, any disturbance can cause a drift of the process output signal. Therefore, efficient closed-loop control of such processes is required. There are many PI and PID controller tuning methods for integrating processes. However, it is hard to find one requiring only a simple tuning procedure on the process, while the tuning method is based either on time-domain measurements or on a process transfer function of arbitrary order, which are the advantages of the magnitude optimum multiple integration (MOMI) tuning method. In this paper, we propose the extension of the MOMI tuning method to integrating processes. Besides the mentioned advantages, the extension provides efficient closed-loop control, while PI controller parameters calculation is still based on simple algebraic expressions, making it suitable for less-demanding hardware, like simpler programmable logic controllers (PLC). Additionally, the proposed method incorporates reference weighting factor b that allows users to emphasize either the disturbance-rejection or reference-following response. The proposed extension of the MOMI method (time-domain approach) was also tested on a charge-amplifier drift-compensation system, a laboratory hydraulic plant, on an industrial autoclave, and on a solid-oxide fuel-cell temperature control. All closed-loop responses were relatively stable and fast, all in accordance with the magnitude optimum criteria.

scholarly journals Optimal tuning of proportional integral controller for fixed-speed wind turbine using grey wolf optimizer

2020 ◽  
Vol 10 (5) ◽  
pp. 5251
Author(s):  
Aliyu Hamza Sule ◽  
Ahmad Safawi Mokhtar ◽  
Jasrul Jamani Bin Jamian ◽  
Attaullah Khidrani ◽  
Raja Masood Larik
Keyword(s):  
Wind Turbine ◽  
Objective Function ◽  
Pi Controller ◽  
Grey Wolf Optimizer ◽  
Local Optimum ◽  
Grey Wolf ◽  
Proportional Integral ◽  
Tuning Method ◽  
Integral Controller ◽  
Tuning Methods

The need for tuning the PI controller is to improve its performance metrics such as rise time, settling time and overshoot. This paper proposed the Grey Wolf Optimizer (GWO) tuning method of a Proportional Integral (PI) controller for fixed speed Wind Turbine. The objective is to overcome the limitations in using the PSO and GA tuning methods for tuning the PI controller, such as quick convergence occurring too soon into a local optimum, and the controller step input response. The GWO, the Particle Swarm Optimization (PSO), and the Genetic Algorithm (GA) tuning methods were implemented in the Matlab 2016b to search the optimal gains of the Proportional and Integral controller through minimization of the objective function. A comparison was made between the results obtained from the GWO tuning method against PSO and GA tuning techniques. The GWO computed the smallest value of the objective function minimized. It exhibited faster convergence and better time response specification compared to other methods. These and more performance indicators show the superiority of the GWO tuning method.

Practical Tuning Rule Development for Fractional Order Proportional and Integral Controllers

2008 ◽  
Vol 3 (2) ◽  
Author(s):  
YangQuan Chen ◽  
Tripti Bhaskaran ◽  
Dingyü Xue
Keyword(s):  
Fractional Order ◽  
Dynamic Systems ◽  
Disturbance Rejection ◽  
Dead Time ◽  
Pi Controller ◽  
First Order ◽  
Tuning Method ◽  
Peak Sensitivity ◽  
Practical Nature ◽  
Tuning Rules

This paper presents a new practical tuning method for fractional order proportional and integral (FO-PI) controller. The plant to be controlled is mainly first order plus delay time (FOPDT). The tuning is optimum in the sense that the load disturbance rejection is optimized yet with a constraint on the maximum or peak sensitivity. We generalized Ms constrained integral (MIGO) based controller tuning method to handle the FO-PI case, called F-MIGO, given the fractional order α. The F-MIGO method is then used to develop tuning rules for the FOPDT class of dynamic systems. The final developed tuning rules only apply the relative dead time τ of the FOPDT model to determine the best fractional order α and at the same time to determine the best FO-PI gains. Extensive simulation results are included to illustrate the simple yet practical nature of the developed new tuning rules. The tuning rule development procedure for FO-PI is not only valid for FOPDT but also applicable for other general class of plants.

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