Fuzzy Adaptive Whale Optimization Control Algorithm for Trajectory Tracking of a Cable-Driven Parallel Robot

This paper proposes a fuzzy proportion integration differentiation (PID) control strategy based on an adaptive whale optimization algorithm (FPID-AWOA) for trajectory tracking of a cable-driven parallel robot (CDPR). A mechanical prototype, and kinematic and dynamic models of the CDPR are establishe...

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Published in:IEEE transactions on automation science and engineering Vol. 21; no. 4; pp. 5149 - 5160
Main Authors: Zhou, Bin, Wang, Yuhang, Zi, Bin, Zhu, Weidong
Format: Journal Article
Language:English
Published: IEEE 01.10.2024
Subjects:
Cable-driven parallel robot
Fuzzy control
Kinematics
Optimization
Particle swarm optimization
particle swarm optimization algorithm
PD control
PI control
Robots
Trajectory tracking
whale optimization algorithm
Whale optimization algorithms
ISSN:1545-5955, 1558-3783
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Abstract This paper proposes a fuzzy proportion integration differentiation (PID) control strategy based on an adaptive whale optimization algorithm (FPID-AWOA) for trajectory tracking of a cable-driven parallel robot (CDPR). A mechanical prototype, and kinematic and dynamic models of the CDPR are established. Thus, new fuzzy rules are developed and a new fuzzy PID controller is designed. Subsequently, the AWOA is introduced to optimize quantization and scale factors of the fuzzy PID controller to obtain the optimal solution. Among them, AWOA is an improvement on WOA. Numerical examples show that the fuzzy PID control strategy based on adaptive whale optimization algorithm (FPID-AWOA) has higher CDPR trajectory tracking accuracy than the traditional fuzzy PID control strategy, the fuzzy PID control strategy based on whale optimization algorithm (FPID-WOA), and the fuzzy PID control strategy based on particle swarm optimization (FPID-PSOA). In comparison with the FPID and FPID-PSOA, the experimental results show that the trajectory tracking error of the proposed FPID-AWOA is reduced by 51.2% and 19.5% in the <inline-formula> <tex-math notation="LaTeX">X </tex-math></inline-formula>-axis direction, respectively, 64.2% and 49.7% in the <inline-formula> <tex-math notation="LaTeX">Y </tex-math></inline-formula>-axis direction, respectively, and 29.1% and 12.2% in the <inline-formula> <tex-math notation="LaTeX">Z </tex-math></inline-formula>-axis direction, respectively. Note to Practitioners-The motivation of this article stems from the need to develop efficient trajectory tracking control algorithms for practical applications of CDPRs. Fuzzy PID control is widely used in CDPRs because of its good robustness and fast response speed. However, the fuzzy parameter selection depends on experience, and the efficiency is low. In order to obtain high quality quantization and scale factors quickly, we propose FPID-AWOA. It uses AWOA to find the optimal quantization and scale factors, which makes the fuzzy PID control get better performance. FPID-AWOA can also be applied to control other robots. In future research, we will extend the proposed approach to multiple CDPRs working collaboratively as well as to mobile operational requirements.
AbstractList This paper proposes a fuzzy proportion integration differentiation (PID) control strategy based on an adaptive whale optimization algorithm (FPID-AWOA) for trajectory tracking of a cable-driven parallel robot (CDPR). A mechanical prototype, and kinematic and dynamic models of the CDPR are established. Thus, new fuzzy rules are developed and a new fuzzy PID controller is designed. Subsequently, the AWOA is introduced to optimize quantization and scale factors of the fuzzy PID controller to obtain the optimal solution. Among them, AWOA is an improvement on WOA. Numerical examples show that the fuzzy PID control strategy based on adaptive whale optimization algorithm (FPID-AWOA) has higher CDPR trajectory tracking accuracy than the traditional fuzzy PID control strategy, the fuzzy PID control strategy based on whale optimization algorithm (FPID-WOA), and the fuzzy PID control strategy based on particle swarm optimization (FPID-PSOA). In comparison with the FPID and FPID-PSOA, the experimental results show that the trajectory tracking error of the proposed FPID-AWOA is reduced by 51.2% and 19.5% in the <inline-formula> <tex-math notation="LaTeX">X </tex-math></inline-formula>-axis direction, respectively, 64.2% and 49.7% in the <inline-formula> <tex-math notation="LaTeX">Y </tex-math></inline-formula>-axis direction, respectively, and 29.1% and 12.2% in the <inline-formula> <tex-math notation="LaTeX">Z </tex-math></inline-formula>-axis direction, respectively. Note to Practitioners-The motivation of this article stems from the need to develop efficient trajectory tracking control algorithms for practical applications of CDPRs. Fuzzy PID control is widely used in CDPRs because of its good robustness and fast response speed. However, the fuzzy parameter selection depends on experience, and the efficiency is low. In order to obtain high quality quantization and scale factors quickly, we propose FPID-AWOA. It uses AWOA to find the optimal quantization and scale factors, which makes the fuzzy PID control get better performance. FPID-AWOA can also be applied to control other robots. In future research, we will extend the proposed approach to multiple CDPRs working collaboratively as well as to mobile operational requirements.
Author Zhou, Bin
Zhu, Weidong
Zi, Bin
Wang, Yuhang
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Snippet This paper proposes a fuzzy proportion integration differentiation (PID) control strategy based on an adaptive whale optimization algorithm (FPID-AWOA) for...
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SubjectTerms Cable-driven parallel robot
Fuzzy control
Kinematics
Optimization
Particle swarm optimization
particle swarm optimization algorithm
PD control
PI control
Robots
Trajectory tracking
whale optimization algorithm
Whale optimization algorithms
Title Fuzzy Adaptive Whale Optimization Control Algorithm for Trajectory Tracking of a Cable-Driven Parallel Robot
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