Opening and Closing Positioning Control of the Seedling Picking-Up Mechanism Based on Fuzzy-PID Control Algorithm

Han, Shuai; Wang, Weibing; Wang, Yu; Liu, Ganghui

doi:10.3390/pr10071349

Cited by 6 publications

(2 citation statements)

References 6 publications

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“…The rule base is a language expression that imitates human thinking and reasoning based on experience and knowledge. The database stores the membership vector values of all fuzzy subsets of all input and output variables and provides data to the inference engine during rule reasoning [16]. An inference engine is a group of programs that control and coordinate the whole system.…”

Section: Fcamentioning

confidence: 99%

Embedded Navigation System of Mechatronics Robot by Fuzzy Control Algorithm

2023

IJOMAM

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With the development of mechatronics robot intelligence and autonomy, automatic obstacle avoidance and path planning has become the core of the current robot navigation system research. This research aims to solve the problems of complex path planning and poor parameter strain in ultrasonic array navigation systems. Firstly, the research status of the mobile robot is introduced, and a new embedded navigation system based on ultrasonic array technology is designed. Then, the fuzzy control algorithm is introduced, and a new Positive/Negative (P/N) local path planning algorithm based on fuzzy control is proposed. The normal rule is used to make the robot move toward the target point, in which the robot avoids obstacles through negative rules. The complexity of the fuzzy system is reduced through binary negative rules. Then, the precise output is obtained through the path decision formula to realize the local path planning system of the mechatronic robot. Finally, the simulation results verify the effectiveness of the improved algorithm. Based on the joint simulation experiment platform of Simulink and Robot Operating System (ROS), the excellent performance of the proposed embedded navigation system has been verified. The new P/N local path planning algorithm of fuzzy control can make the running track more accurate and smoother by optimizing the path. The new embedded navigation system can provide theoretical analysis and practical reference for developing an electromechanical robot navigation system.

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Section: Fcamentioning

confidence: 99%

Embedded Navigation System of Mechatronics Robot by Fuzzy Control Algorithm

2023

IJOMAM

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“…The control parameters are synthesized using linear relationships with the initial parameters of the PID controller. Finally, the control parameters are inputted to the PID controller for control, and the final parameter self-tuning formula [23] is shown in Equation ( 7):…”

Section: Design Of the Horizontal Adaptive Fuzzy Pid Controllermentioning

confidence: 99%

Research and Implementation of Intelligent Control System for Grassland Grazing Robotic Vehicle

Zhang

Mei

et al. 2023

Electronics

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In response to the complex animal husbandry environment, wide livestock range, and labor shortage in Inner Mongolia grasslands, this study designed an intelligent control system for robotic vehicles used in natural grazing grassland environments. The control system consists of software and hardware components and motion control algorithms. Based on the application characteristics of different mechanisms of the robotic vehicle, the motion control of the robotic vehicle is decomposed into longitudinal speed control and lateral steering control. The longitudinal speed control adopts the traditional Proportional-Integral-Differential (PID) control method. Since the hardware composition and internal angle calculation method of the steering system are complex and prone to deviations caused by external terrain, a lateral adaptive fuzzy PID controller was constructed with lateral deviation and deviation change rate as input variables and parameters Kp, Ki, and Kd as outputs. The field test results show that when the longitudinal speed was set to 0.5 m/s, the adjustment time was 1.95 s, and the steady-state error was about 0. When the longitudinal speed was the same, the lateral adaptive fuzzy PID controller reduced the rise time by 0.77 s compared to traditional control methods, reduced overshoot and steady-state error by 2%, and quickly recovered to a steady state after being disturbed. The following test results show that the designed intelligent control system can achieve real-time tracking of targets and that the motion controllers can effectively control speed and steering angle. Therefore, the intelligent control system designed in this paper can effectively achieve stable and real-time control of the robotic vehicle.

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Phase Shift Strategy of CPT System Based on Three-inputs Fuzzy PID Control

Wang

Liu

2023

2023 IEEE 6th International Electrical and Energy Conference (CIEEC)

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Opening and Closing Positioning Control of the Seedling Picking-Up Mechanism Based on Fuzzy-PID Control Algorithm

Cited by 6 publications

References 6 publications

Embedded Navigation System of Mechatronics Robot by Fuzzy Control Algorithm

Embedded Navigation System of Mechatronics Robot by Fuzzy Control Algorithm

Research and Implementation of Intelligent Control System for Grassland Grazing Robotic Vehicle

Phase Shift Strategy of CPT System Based on Three-inputs Fuzzy PID Control

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