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

PSO and GA tuned conventional and fractional order PID controllers for quadrotor control 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/approachIn 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).FindingsAccording 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/valueWhile 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Aircraft Engineering and Aerospace Technology Emerald Publishing

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

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Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
1748-8842
DOI
10.1108/aeat-08-2020-0185
Publisher site
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Abstract

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/approachIn 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).FindingsAccording 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/valueWhile 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.

Journal

Aircraft Engineering and Aerospace Technology – Emerald Publishing

Published: Sep 6, 2021

Keywords: PSO; GA; Quadrotor control; Fractional order PID controller

References

  • A simple approach on implementing IMU sensor fusion in PID controller for stabilizing quadrotor flight control
    Azfar, A.Z.; Hazry, D.
  • Power law and fractional calculus model of viscoelasticity
    Bagley, R.L.
  • A survey of recent advances in fractional order control for time delay systems
    Birs, I.; Muresan, C.; Nascu, I.; Ionescu, C.
  • Chapter 1 - Evolution and evolutinary algorithms
    Câmara, D.
  • Design of fractional order controller based on particle swarm optimization
    Cao, J.Y.; Cao, B.G.
  • Optimization of fractional order PID controllers based on genetic algorithms
    Cao, J.Y.; Liang, J.; Cao, B.G.
  • Stability and performance analysis of fractional order control systems
    Choudhary, S.K.
  • Fractional order controllers versus integer order controllers
    Dulau, M.; Gligor, A.; Dulau, T.M.
  • The fractional curl operator in electromagnetic, microwave opt
    Engheta, N.
  • Chapter 13 – on fractional-order PID design
    Faieghi, M.R.; Nemati, A.; Michalowski, T.
  • Evolutionary based tuning approach of PlλDµ fractional-order speed controller for multirotor UAV
    Giernacki, W.; Coelho, J.P.
  • Chaos in fractional chua’s circuit
    Hartley, T.; Lorenzo, C.F.; Qammer, H.K.
  • Estimation and control of a quadrotor attitude
    Henriques, B.S.M.
  • Unfalsified control design using a generalized cost function for a quadrotor
    Hokmabadi, A.; Khodabandeh, M.
  • Particle swarm optimization
    Kennedy, J.; Eberhart, R.C.
  • Synchronization on the adaptive sliding mode controller for fractional order complex chaotic systems with uncertainty and disturbances
    Khan, A.; Jahanzaib, L.S.
  • Novel morphing wing actuator control-based particle swarm optimisation
    Khan, S.; Grigorie, T.L.; Botez, R.M.; Mamou, M.; Mebarki, Y.
  • Investigation of the effect of differential morphing on lateral flight by using PID algorithm in quadrotors
    Kose, O.; Oktay, T.
  • Investigation of the effect of differential morphing on forward flight by using PID algorithm in quadrotors
    Kose, O.; Oktay, T.
  • Simultaneous quadrotor autopilot system and collective morphing system design
    Kose, O.; Oktay, T.
  • Displacement control of piezoelectric actuator based on fuzzy fractional order PID
    Liu, W.; Lu, S.; Zhu, C.; Ren, C.; Zheng, H.
  • New tuning rules for fractional PIλcontrollers
    Maione, G.; Lino, P.
  • Design of a fractional order PID controller using particle swarm optimization technique
    Maiti, D.; Biswas, S.; Konar, A.
  • Some noises with 1/f spectrum, a bridge between direct current and white noise
    Mandelbrot, B.
  • Stability results for fractional differential equations with applications to control processing
    Matignon, D.
  • Design and flight testing of an autonomous variable-pitch quadrotor
    Michini, B.; Redding, J.; Ure, K.; Cutler, M.; How, J.P.
  • First generation CRONE control
    Oustaloup, A.; Bansard, M.
  • Second generation CRONE control
    Oustaloup, A.; Lanusse, P.; Mathieu, B.
  • Third generation CRONE control
    Oustaloup, A.; Lanusse, P.; Mathieu, B.
  • Frequency-band complex noninteger differentiator: characterization and synthesis
    Outslaup, A.; Levron, F.; Mathieu, B.; Nanot, F.M.
  • PID controller design for fractional-order systems with time delays
    Özbay, H.; Bonnet, C.; Fioravanti, A.
  • Fractional-order systems and fractional-order controllers
    Podlubny, I.
  • Application of fractional order derivatives to calculation of heat load intensity change in blast furnace walls
    Podlubny, I.; Dorcak, L.; Misanek, J.
  • Design of a fractional order PID controller with application to an induction motor drive
    Saleem, A.I.; Soliman, H.M.; Al-Ratrout, S.; Mesbah, M.
  • A modified particle swarm optimizer
    Shi, Y.; Eberhart, R.C.
  • Linear approximation of transfer function with a pole of fractional power
    Sun, H.H.; Abdelwahab, A.A.; Onaral, B.
  • Back to basis: meaning of the parameters of fractional order PID controllers
    Tejado, I.; Vinagre, B.M.; Traver, J.E.; Prieto-Arranz, J.; Nuevo-Gallardo, C.
  • FOMCON: fractional- order modeling and control toolbox for MATLAB
    Tepljakov, A.A.; Petlenkov, E.; Belikov, J.
  • Ninteger v. 2.3 fractional control toolbox for MATLAB
    Valerio, D.
  • Tuning of fractional PID controllers with Ziegler-Nichols type rules
    Valerio, D.; Costa, J.S.
  • Chapter 1 - Bio-inspired computation and optimization: an overview
    Yang, X.S.; Chien, S.F.; Ting, T.O.; Yang