PSO optimization of Integral Backstepping Controller for Quadrotor attitude stabilization

Yacef, Fouad; Bouhali, Omar; Hamerlain, Mustapha; Rezoug, Amar

doi:10.1109/icosc.2013.6750900

Cited by 19 publications

(10 citation statements)

References 12 publications

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“…Particle in a swarm adjusts its position in search space using its present velocity, own previous experience, and that of neighboring particles. Therefore, a particle uses the best position encountered by itself and that of its neighbors to steer toward an optimal solution [24]. The PSO algorithm formulation adopted in this study is given by the following equations [25]…”

Section: A Particle Swarm Optimizationmentioning

confidence: 99%

Optimal Adaptive Control of a Knee Joint Exoskeleton for Lower Limb Functional Rehabilitation

Sadoun

Yacef

2021

2021 International Conference on Recent Advances in Mathematics and Informatics (ICRAMI)

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Lower limb exoskeleton robots hold great potential for rehabilitation, movement assistance, and strength augmentation. Design control to guarantee optimal needed assistance is still a challenge considering the pathological variances between patients. In this paper, we proposed an optimal adaptive control scheme based on Particle Swarm Optimization (PSO) Algorithm. The proposed controller is based on a wellknown dynamic model of the knee joint exoskeleton, and the optimization algorithm is used to minimize a square error fitness function, which quantifies tracking performances. Control parameters are tuned respecting some nonlinear constraints for step response of the system and boundaries constraints. Numerical simulation results are presented to show the validity and the high performances of the proposed approach.

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Section: A Particle Swarm Optimizationmentioning

confidence: 99%

Optimal Adaptive Control of a Knee Joint Exoskeleton for Lower Limb Functional Rehabilitation

Sadoun

Yacef

2021

2021 International Conference on Recent Advances in Mathematics and Informatics (ICRAMI)

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“…In the literature, various optimization algorithms have been developed to improve the quality of the closed-loop system such as Artificial Bee Colony (ABC) algorithm [29], Ant Colony Optimization (ACO) algorithm [30], Bacterial Foraging Optimization (BFO) algorithm [31], Multi-Verse Optimization (MVO) algorithm [32] and Gravitational Search Algorithm (GSA) [33]. Particle Swarm Optimization (PSO) is one of the effective optimization methods that has been successfully used in tuning PID controllers as in [34], for quadrotor stabilization based on integral backstepping control design [35], for tuning the parameters of fuzzy PD controller with minimization of integral square error cost function in [36], to improve the performance of an ultrasonic transducer [28]. For the control of DP systems, authors in [21] have deployed the PSO method to optimize the fuzzy function/parameters to enhance the quality and performance of the DP system of the ship.…”

Section: Introductionmentioning

confidence: 99%

Particle Swarm Optimization of a Passivity-Based Controller for Dynamic Positioning of Ships

2020

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Dynamic positioning (DP) control system is an essential module used in offshore ships for accurate maneuvering and maintaining of ship’s position and heading (fixed location or pre-determined track) by means of thruster forces being generated by controllers. In this paper, an interconnection and damping assignment-passivity based control (IDA-PBC) controller is developed for DP of surface ships. The design of the IDA-PBC controller involves a dynamic extension utilizing the coordinate transformation which adds damping to some coordinates to ensure asymptotic stability and adds integral action to enhance the robustness of the system against disturbances. The particle swarm optimization (PSO) technique is one of the the population-based optimization methods that has gained the attention of the control research communities and used to solve various engineering problems. The PSO algorithm is proposed for the optimization of the IDA-PBC controller. Numerical simulations results with comparisons illustrate the effectiveness of the new PSO-tuned dynamic IDA-PBC controller.

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“…Usually, these QR controllers present coupled design parameters with inherent trade-off and, therefore, need to be optimally tuned to achieve simultaneous performance requirements, which is a non-trivial trial-and-error task. Although some statistic-based [10] and bio-inspired [3,11,12], optimization algorithms have been used for PID and backstepping based flight control systems, a solution for the SMC-based approach is still an open and necessary research issue [13]. This has motivated the novel work reported here on the proposal and evaluation of hunting-based search algorithms as optimization tools, compared to the known and classical bio-inspired Particle Swarm Optimization (PSO) [14].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Hunting-Based Optimizers for a Quadrotor Sliding Mode Flight Controller

Oliveira

Boaventura‐Cunha

et al. 2020

Robotics

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The design of Multi-Input Multi-Output nonlinear control systems for a quadrotor can be a difficult task. Nature inspired optimization techniques can greatly improve the design of non-linear control systems. Two recently proposed hunting-based swarm intelligence inspired techniques are the Grey Wolf Optimizer (GWO) and the Ant Lion Optimizer (ALO). This paper proposes the use of both GWO and ALO techniques to design a Sliding Mode Control (SMC) flight system for tracking improvement of altitude and attitude in a quadrotor dynamic model. SMC is a nonlinear technique which requires that its strictly coupled parameters related to continuous and discontinuous components be correctly adjusted for proper operation. This requires minimizing the tracking error while keeping the chattering effect and control signal magnitude within suitable limits. The performance achieved with both GWO and ALO, considering realistic disturbed flight scenarios are presented and compared to the classical Particle Swarm Optimization (PSO) algorithm. Simulated results are presented showing that GWO and ALO outperformed PSO in terms of precise tracking, for ideal and disturbed conditions. It is shown that the higher stochastic nature of these hunting-based algorithms provided more confidence in local optima avoidance, suggesting feasibility of getting a more precise tracking for practical use.

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PSO optimization of Integral Backstepping Controller for Quadrotor attitude stabilization

Cited by 19 publications

References 12 publications

Optimal Adaptive Control of a Knee Joint Exoskeleton for Lower Limb Functional Rehabilitation

Optimal Adaptive Control of a Knee Joint Exoskeleton for Lower Limb Functional Rehabilitation

Particle Swarm Optimization of a Passivity-Based Controller for Dynamic Positioning of Ships

Evaluation of Hunting-Based Optimizers for a Quadrotor Sliding Mode Flight Controller

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