Optimization Design of LQR Controller Based on Improved Whale Optimization Algorithm

Zhai, Qianhao; Xia, Xiaoyu; Feng, Siling; Huang, Mengxing

doi:10.1109/icict50521.2020.00067

Cited by 9 publications

(3 citation statements)

References 6 publications

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“…Whale optimization algorithm (WOA) is inspired by the hunting tactic of the humpback whale. The hunting strategy of the humpback whale is separated into three parts: searching, encircling and bubble-net attacking [99] , [100] , [101] . During searching, the humpback whales exchange information about prey to each other.…”

Section: A Review Of Various Swarm-based Motmentioning

confidence: 99%

A review of swarm-based metaheuristic optimization techniques and their application to doubly fed induction generator

Reddy

Saha

2022

Heliyon

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Section: A Review Of Various Swarm-based Motmentioning

confidence: 99%

A review of swarm-based metaheuristic optimization techniques and their application to doubly fed induction generator

Reddy

Saha

2022

Heliyon

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“…Whale optimization algorithm (WOA) is inspired by the hunting tactic of the humpback whale. The hunting strategy of the humpback whale is separated into three parts: searching, encircling and bubble-net attacking [ 14 , 33 , 34 ]. During searching, the humpback whales exchange information about prey to each other.…”

Section: The Whale Optimization Algorithmmentioning

confidence: 99%

A modified Whale Optimization Algorithm for exploitation capability and stability enhancement

Reddy

Saha²

2022

Heliyon

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“…As an alternative to PSO, other optimization methods have been used for tuning controller parameters [18]. Qianhao et al [19] developed the whale optimization algorithm, which is a type of swarm intelligence optimization algorithm, to improve the performance of a linear-quadratic regulator controller. Pan et al [20] presented the fuzzy-PID algorithm for the nonlinear dynamics and uncertainties of the human exoskeleton robot.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Particle Swarm Optimization of PID Gain Tuning for Lower-Limb Human Exoskeleton in Virtual Environment

et al. 2020

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Tuning of a proportional-integral-derivative (PID) controller for a complex multi-joint structure, such as an exoskeleton, using conventional methods is difficult and imprecise. In this paper, an optimal PID tuning method for a 3-dimensional model of a lower-limb human exoskeleton in gait training condition is presented. The dynamic equation of the human-exoskeleton is determined using a Lagrangian approach, and its transfer function is established in a closed-loop control system. PID controller gains, initialized by the Ziegler–Nichols (Z-N) method, are used as the input to an adaptive particle swarm optimization (APSO) algorithm for minimizing the multi-joint trajectory error. The optimized controller is tested in the Gazebo virtual environment and compared with the Z-N and conventional optimization methods. The numerical analysis shows that the PID controller tuned by a combination of Z-N and APSO improves the performance of a lower-limb human exoskeleton in gait training.

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Optimization Design of LQR Controller Based on Improved Whale Optimization Algorithm

Cited by 9 publications

References 6 publications

A review of swarm-based metaheuristic optimization techniques and their application to doubly fed induction generator

A review of swarm-based metaheuristic optimization techniques and their application to doubly fed induction generator

A modified Whale Optimization Algorithm for exploitation capability and stability enhancement

Adaptive Particle Swarm Optimization of PID Gain Tuning for Lower-Limb Human Exoskeleton in Virtual Environment

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