Design and co-simulation of a fuzzy gain-scheduled PID controller based on particle swarm optimization algorithms for a quad tilt wing unmanned aerial vehicle

Khoud, Khaled Ben; Ayadi, Mounir

doi:10.1177/0142331217740947

Cited by 14 publications

(7 citation statements)

References 47 publications

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“…Integral square error (ISE), integral absolute error (IAE), integral time absolute error (ITAE) and integral time square error (ITSE) are often used to evaluate error performance index. IAE is selected as the fitness function in Khoud et al (2018)…”

Section: Pir-ipso-based Uav Control Systemmentioning

confidence: 99%

A non-overshooting controller for vehicle path following

Wang

Zhang

2021

Transactions of the Institute of Measurement and Control

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Unmanned pavement construction is of great significance in China, and one of the most important issues is how to follow the designed path near the boundary of the pavement construction area to avoid curbs or railings. In this paper, we raise a simple yet effective controller, named the proportional-integral-radius and improved particle swarm optimization (PIR-IPSO) controller, for fast non-overshooting path-following control of an unmanned articulated vehicle (UAV). Firstly, UAV kinematics model is introduced and segmented UAV steering dynamics model is built through field experiments; then, the raw data collected by differential global positioning system (DGPS) is used to build the measurement error distribution model that simulates positioning errors. Next, line of sight (LOS) guidance law is introduced and the LOS initial parameter is assigned based on human driving behavior. Besides, the initial control parameters tuned by the Ziegler-Nichols (ZN) method are used as the initial iterative parameters of the PSO controller. An improved PSO fitness function is also designed to achieve fast non-overshoot control performance. Experiments show that compared with the PSO, ZN and ZN-PSO controller, the PIR-PSO-based controller has significantly less settling time and almost no overshoot in various UAV initial states. Furthermore, compared with other controllers, the proposed PIR-IPSO-based controller achieves precise non-overshoot control, relatively less settling time and centimeter-level positioning error in various initial deviations.

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Section: Pir-ipso-based Uav Control Systemmentioning

confidence: 99%

A non-overshooting controller for vehicle path following

Wang

Zhang

2021

Transactions of the Institute of Measurement and Control

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“…where pdc K and idc K are the proportional and integral factors of the PI regulator for the DC-link regulation, var p K and var i K are the ones for the reactive power loop, pspe K and ispe K and denote the control gains for the speed control dynamics. These considered variables are adopted to reduce defined performance criteria under operational constraints like the maximum overshoot [6,7].…”

Section: Pi Controllers Tuning Problem Formulationmentioning

confidence: 99%

Thermal exchange optimization based control of a doubly fed induction generator in wind energy conversion systems

Alhato

2020

IJEECS

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<span lang="EN-US">This paper presents a novel design method to attain the optimal parameters of Proportional-Integral (PI) controllers for a 1.5 MW Doubly Fed Induction Generator (DFIG) in wind energy based on a Thermal Exchange Optimization (TEO) algorithm. Since the gains of PI controllers are usually selected by classical and tedious trials-errors based procedures, their tuning for such a wind energy converter is formulated as a constrained nonlinear optimization problem. Inspired from the Newton’s law of cooling, the TEO algorithm is successfully applied to solve such a control problem under time-domain performances and operational constraints in order to extract the maximum available power. In order to evaluate the performances of the proposed TEO algorithm, the genetic algorithm, particle swarm optimization, harmony search algorithm and water cycle algorithm methods have been utilized for making a comparative study. Moreover, a statistical analysis using non-parametric Friedman and Bonferroni-Dunn’s tests demonstrates that the TEO algorithm gives very competitive results in comparison to the other reported metaheuristic algorithms.</span>

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“…One useful approach is penalties-based handling concept. In this study, the following external static penalty technique is investigated (Bouallègue et al, 2012; Ben Khoud and Bouallègue, 2017; Ben Khoud et al, 2018; M’zoughi et al, 2018)…”

Section: Proposed ε-Mopso-based Approachmentioning

confidence: 99%

“…To deal with these complexities and hard mathematical backgrounds, the recourse to the metaheuristics-based optimization theory is a promising solution (Ben Khoud and Bouallègue, 2017; Ben Khoud et al, 2018; Bouallègue et al, 2012; M’zoughi et al, 2018; Cheng et al, 2015). Proposing a systematic and less time-consuming method for the polynomials coefficients’ tuning of digital RST controllers is the main contribution of this paper.…”

Section: Introductionmentioning

confidence: 99%

Epsilon-multiobjective particle swarm optimization-based tuning of sensitivity functions for polynomial control design

Madiouni

Haggège

Siarry

2019

Transactions of the Institute of Measurement and Control

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In this paper, a novel systematic method for the polynomial controllers (denoted as RST controllers) design and tuning is proposed and successfully implemented based on an epsilon-multiobjective particle swarm optimization (ε-MOPSO) algorithm. The ε-domination concept is used to further improve both properties of the non-premature convergence towards Pareto-optimal sets and the diversity among the found solutions. The RST polynomials coefficients’ computation is formulated as a constrained multiobjective optimization problem under operational frequency-domain constraints. The proposed methodology aims to optimize a desired output sensitivity function satisfying nominal performance and H∞ robustness specifications of the closed-loop system. The use of a suitable fixed parts of the optimized output sensitivity function will provide partial poles placement of the closed-loop dynamics. The inverse of such an optimized desired sensitivity function defines the performance and robustness H∞ weighting filter. Such a proposed ε-MOPSO algorithm is firstly compared with several homologous MOPSO, non-dominated sorting genetic algorithm II (NSGA-II) and multiobjective differential evolution (MODE) algorithms for a benchmark of multiobjective test functions. The algorithms’ performances are evaluated in terms of several metrics in order to show the superiority and the effectiveness of the proposed method. An application to the axis position control of a flexible transmission system with varying loads is then performed. Demonstrative simulations are carried out to show the remarkable superiority and effectiveness of the proposed ε-MOPSO-tuned digital RST controllers compared with several well-known methods available in the literature.

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Design and co-simulation of a fuzzy gain-scheduled PID controller based on particle swarm optimization algorithms for a quad tilt wing unmanned aerial vehicle

Cited by 14 publications

References 47 publications

A non-overshooting controller for vehicle path following

A non-overshooting controller for vehicle path following

Thermal exchange optimization based control of a doubly fed induction generator in wind energy conversion systems

Epsilon-multiobjective particle swarm optimization-based tuning of sensitivity functions for polynomial control design

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