Particle swarm optimization-based fixed-structure ℋ ∞ control design

Bouallègue, Soufiene; Haggège, Joseph; Benrejeb, Mohamed

doi:10.1007/s12555-011-0207-2

Cited by 23 publications

(11 citation statements)

References 24 publications

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“…The cost functions of problem (23) are chosen, relative to each controlled variable ϕ , θ and ψ , as the integral absolute error (IAE) criteria (Bouallègue et al, 2011, 2012a, 2012b), which are defined with the generic form as follows:…”

Section: Optimization-based Control Problem Formulationmentioning

confidence: 99%

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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

Khoud

Ayadi

2018

Transactions of the Institute of Measurement and Control

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This paper deals with the systematic design and hardware co-simulation of a fuzzy gain-scheduled proportional–integral–derivative (GS-PID) controller for a quad tilt wing (QTW) type of unmanned aerial vehicles (UAVs) based on different variants of the particle swarm optimization (PSO) algorithm. The fuzzy PID gains scheduling problem for the stabilization of the roll, pitch and yaw dynamics of the QTW vehicle is formulated as a constrained optimization problem and solved thanks to improved PSO algorithms. PSO algorithms with variable inertia weight (PSO-In), PSO with constriction factor (PSO-Co) and PSO with possibility updating strategies (PSO-gbest) are proposed. Such variants of the PSO algorithm aim further to improve the exploration and exploitation capabilities of such a stochastic algorithm as well as its convergence fastness. The robustness of the designed PSO-based fuzzy GS-PID controllers under actuators faults is shown on the non-linear model of the QTW. All optimized fuzzy GS-PID controllers are then co-simulated within a processor-in-the-loop (PIL) framework based on an embedded NI myRIO-1900 board and a host PC. Such a proposed software (SW) and hardware (HW) computer aided design (CAD) platform is based on the Control Design and Simulation (CDSim) module of the LabVIEW environment as well as a set-up Network Streams-based data communication protocol. Demonstrative simulation results are presented, compared and discussed in order to improve the effectiveness of the proposed PSO-based fuzzy gains scheduled PID controllers for the QTW’s attitude flight stabilization.

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Section: Optimization-based Control Problem Formulationmentioning

confidence: 99%

“…The convergence and parameters selection of the PSO algorithm are proved (Clerc and Kennedy, 2002). The PSO theory has been enormously successful in various industrial domains, but more significantly in control engineering design as shown in Bouallègue et al (2011, 2012a, 2012b).…”

Section: Proposed Particle Swarm Optimization Algorithmsmentioning

confidence: 99%

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

Ayadi

2018

Transactions of the Institute of Measurement and Control

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“…It has been used across a wide range of engineering applications. These applications can be summarized around domains of robotics, image and signal processing, electronic circuits design, communication networks, but more especially the domain of plant control design, as shown in [2][3][4][5][6].…”

Section: Overviewmentioning

confidence: 99%

A New Method for Tuning PID-Type Fuzzy Controllers Using Particle Swarm Optimization

Haggège¹,

Benrejeb²

2012

Fuzzy Controllers- Recent Advances in Theory and Applications

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“…Michalewicz, Dasgupta, Riche and Schoenauer [12] augmented the cost function with penalty functions to handle the optimization problems subject to a set of constraints. Soufiene Bouall egue, Joseph Hagg ege and Mohamed Benrejeb [13] applied the above method to fixed-structure H 1 controller design. However, the values of the penalty parameters are usually difficult to choose.…”

Section: Introductionmentioning

confidence: 99%

Missile fixed-structure μ controller design based on constrained PSO algorithm

Min

Chen

et al. 2017

Automatika

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This paper provides a method of fixed-structure m controller design with a novel particle swarm optimization (PSO) technique. The structured synthesis problem based on optimization is formulated and solved by a kind of constrained PSO algorithm which is relatively simple and without any new parameters added in the objective function. The experimental results tested on a set of 12 benchmark functions show that the proposed algorithm can outperform others in most cases. What is more, an air-to-air missile longitudinal channel control structure based on the proposed algorithm is studied and the simulation results are compared with other algorithms. The final comparison results also show the superior performance improvement over other algorithms.

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Particle swarm optimization-based fixed-structure ℋ ∞ control design

Cited by 23 publications

References 24 publications

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

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

A New Method for Tuning PID-Type Fuzzy Controllers Using Particle Swarm Optimization

Missile fixed-structure μ controller design based on constrained PSO algorithm

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