Multiobjective Optimization of Multiloop Fractional Order PID Controller Tuned Using Bat Algorithm for Two Interacting Conical Tank Process

2014 ◽  
Vol 704 ◽  
pp. 373-379
Author(s):  
S.K. Lakshmanaprabu ◽  
U. Sabura Banu
Keyword(s):  
Fractional Order ◽  
Pid Controller ◽  
Controller Design ◽  
Optimization Problems ◽  
Linear Optimization ◽  
Optimal Solution ◽  
Bat Algorithm ◽  
Absolute Error ◽  
Fractional Order Pid ◽  
Fractional Order Pid Controller

Multiloop fractional order PID controller is tuned using Bat algorithm for two interacting conical tank process. Two interacting conical tank process is modelled using mass balance equations. Two Interacting Conical Tank process is a complex system involving tedious interaction. Straight forward multiloop PID controller design involves various steps to design the controller. Due to easy implementation and quick convergence, Bat algorithm is used in recent past for solving continuous non-linear optimization problems to achieve global optimal solution. Bat algorithm, a swarm intelligence technique will be attempted to tune the multiloop fractional order PID controller for two interacting conical tank process. The multi objective optimized multiloop fractional PID controller is tested for tracking, disturbance rejection for minimum Integral time absolute error.

PSO and GA tuned conventional and fractional order PID controllers for quadrotor control

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Hasan Saribas ◽  
Sinem Kahvecioglu
Keyword(s):  
Fractional Order ◽  
Pid Controller ◽  
Controller Design ◽  
Optimization Problems ◽  
Parameter Tuning ◽  
Absolute Error ◽  
Cost Functions ◽  
Pid Controllers ◽  
Content Type ◽  
Quadrotor Control

Purpose This study aims to compare the performance of the conventional and fractional order proportional-integral-derivative (PID and FOPID) controllers tuned with a particle swarm optimization (PSO) and genetic algorithm (GA) for quadrotor control. Design/methodology/approach In this study, the gains of the controllers were tuned using PSO and GA, which are included in the heuristic optimization methods. The tuning processes of the controller’s gains were formulated as optimization problems. While generating the objective functions (cost functions), four different decision criteria were considered separately: integrated summation error (ISE), integrated absolute error, integrated time absolute error and integrated time summation error (ITSE). Findings According to the simulation results and comparison tables that were created, FOPID controllers tuned with PSO performed better performances than PID controllers. In addition, the ITSE criterion returned better results in control of all axes except for altitude control when compared to the other cost functions. In the control of altitude with the PID controller, the ISE criterion showed better performance. Originality/value While a conventional PID controller has three parameters (Kp, Ki, Kd) that need to be tuned, FOPID controllers have two additional parameters (µ). The inclusion of these two extra parameters means more flexibility in the controller design but much more complexity for parameter tuning. This study reveals the potential and effectiveness of PSO and GA in tuning the controller despite the increased number of parameters and complexity.

scholarly journals Optimal fractional order PID controller design using Ant Lion Optimizer

2020 ◽  
Vol 11 (2) ◽  
pp. 281-291 ◽  
Author(s):  
Rosy Pradhan ◽  
Santosh Kumar Majhi ◽  
Jatin Kumar Pradhan ◽  
Bibhuti Bhusan Pati
Keyword(s):  
Fractional Order ◽  
Pid Controller ◽  
Controller Design ◽  
Ant Lion Optimizer ◽  
Pid Controller Design ◽  
Ant Lion ◽  
Fractional Order Pid ◽  
Fractional Order Pid Controller

Multi-Objective Optimal Fuzzy Fractional-Order PID Controller Design

2014 ◽  
Vol 18 (3) ◽  
pp. 262-270 ◽  
Author(s):  
Amir Hajiloo ◽  
◽  
Wen-Fang Xie
Keyword(s):  
Fractional Order ◽  
Pid Controller ◽  
Controller Design ◽  
Design Rule ◽  
Rule Base ◽  
Fuzzy Logic Systems ◽  
Multi Objective ◽  
Pid Controller Design ◽  
Fractional Order Pid ◽  
Fractional Order Pid Controller

The design of the optimal fuzzy fractional-order PID controller is addressed in this work. A multi-objective genetic algorithm is proposed to design rule base and membership functions of the fuzzy logic systems. Three conflicting objective functions in both time and frequency domains have been used in Pareto design of the fuzzy fractional-order PID controller. The simulation results reveal the effectiveness of the proposed method in comparison with the results produced by the fractional-order PID controllers.

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