ArticleFrontiers in bioengineering and biotechnology2021
Self-Tuning Control of Manipulator Positioning Based on Fuzzy PID and PSO Algorithm.
Article in Frontiers in bioengineering and biotechnology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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33 citing papers in PubMed.
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- Multi-Objective Location and Mapping Based on Deep Learning and Visual Slam.Sensors (Basel, Switzerland) · 2022Article
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- Image Classification and Recognition of Rice Diseases: A Hybrid DBN and Particle Swarm Optimization Algorithm.Frontiers in bioengineering and biotechnology · 2022Article
- A Tandem Robotic Arm Inverse Kinematic Solution Based on an Improved Particle Swarm Algorithm.Frontiers in bioengineering and biotechnology · 2022Article
- Multi-Scale Feature Fusion Convolutional Neural Network for Indoor Small Target Detection.Frontiers in neurorobotics · 2022Article
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- Improved Multi-Stream Convolutional Block Attention Module for sEMG-Based Gesture Recognition.Frontiers in bioengineering and biotechnology · 2022Article
- Multi-Objective Optimization Design of Ladle Refractory Lining Based on Genetic Algorithm.Frontiers in bioengineering and biotechnology · 2022Article
- A Transendothelial Leukocyte Transmigration Model Based on Computational Fluid Dynamics and BP Neural Network.Frontiers in bioengineering and biotechnology · 2022Article
- Path Planning Optimization of Intelligent Vehicle Based on Improved Genetic and Ant Colony Hybrid Algorithm.Frontiers in bioengineering and biotechnology · 2022Article
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9 authors.
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Abstract
With the manipulator performs fixed-point tasks, it becomes adversely affected by external disturbances, parameter variations, and random noise. Therefore, it is essential to improve the robust and accuracy of the controller. In this article, a self-tuning particle swarm optimization (PSO) fuzzy PID positioning controller is designed based on fuzzy PID control. The quantization and scaling factors in the fuzzy PID algorithm are optimized by PSO in order to achieve high robustness and high accuracy of the manipulator. First of all, a mathematical model of the manipulator is developed, and the manipulator positioning controller is designed. A PD control strategy with compensation for gravity is used for the positioning control system. Then, the PID controller parameters dynamically are minute-tuned by the fuzzy controller 1. Through a closed-loop control loop to adjust the magnitude of the quantization factors-proportionality factors online. Correction values are outputted by the modified fuzzy controller 2. A quantization factor-proportion factor online self-tuning strategy is achieved to find the optimal parameters for the controller. Finally, the control performance of the improved controller is verified by the simulation environment. The results show that the transient response speed, tracking accuracy, and follower characteristics of the system are significantly improved.
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