Adaptive neural PD controllers for mobile manipulator trajectory tracking

Hernández-Barragán, Jesús; Rios, Jorge D.; Gómez-Avila, Javier; Arana‐Daniel, Nancy; López-Franco, Carlos; Alanis, Alma Y.

doi:10.7717/peerj-cs.393

Cited by 4 publications

(1 citation statement)

References 32 publications

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“…Therefore, the accuracy, stability, and rapidity of the controller have stringent requirements [1][2][3]. The current control methods of the robotic arm have fractional-order PID control, particle swarm optimization fuzzy PID control, adaptive neural PD control, and other practices [4][5][6][7][8][9]. Due to the complexity of their algorithmic parameter design, computational costs, and other issues, they are mostly not applicable to the application of tea-picking machinery.…”

Section: Introductionmentioning

confidence: 99%

Design and Control Simulation Analysis of Tender Tea Bud Picking Manipulator

Xue,

Li,

2024

Applied Sciences

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Aiming at the current complex problem of the mechanized high-quality picking of tender tea buds, this paper designs a tender tea bud-picking manipulator. In the picking process, the quality of the petiole and leaf blade of the tender tea bud is crucial, as the traditional cutting picking method destroys the cell structure of the tender tea buds, resulting in rapid oxidation of the cuts, thus losing the bright green appearance and pure taste. For this reason, this paper draws on the quality requirements of tender tea buds and traditional manual picking technology, simulating the process of the manual picking action, putting forward a ‘rotary pull-up’ clamping and ripping picking method, and designing the corresponding actuating structure. Using PVDF material piezoelectric thin-film sensors to detect the clamping force of the tender tea bud picking, the corresponding sensor hardware circuit is designed. In addition, the finite element analysis method is also used to carry out stress analysis on the mechanical fingers to verify the rationality of the automatic mechanism to ensure the high-quality picking of tender tea buds. In terms of the control of the manipulator, an SMC-PID control method is designed by using MATLAB/Simulink 2021 and Adam 2020 software for joint simulation. The way to control the closed-loop system angle and angular velocity error feedback is by adjusting the PID parameters, which quickly converts the sliding mode control to the sliding mode surface. The simulation results show that the SMC-PID control method proposed in this paper can meet the demand in tender tea bud picking and simultaneously has high control accuracy, response speed, and stability.

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Section: Introductionmentioning

confidence: 99%

Design and Control Simulation Analysis of Tender Tea Bud Picking Manipulator

Xue,

Li,

2024

Applied Sciences

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Trajectory Planning and Attitude Control of Manipulator

Yan¹,

Hong²,

Zeng³

et al. 2022

2022 International Symposium on Control Engineering and Robotics (ISCER)

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Research on the Optimization of the PID Control Method for an EOD Robotic Manipulator Using the PSO Algorithm for BP Neural Networks

Zhou,

He,

Shao

et al. 2024

Actuators

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Large-scale explosive ordnance disposal (EOD) robotic manipulators can replace manual EOD tasks, offering higher efficiency and better safety. This study focuses on the control strategies and response speeds of EOD robotic manipulators. Using Adams to establish the dynamic model of an EOD robotic manipulator and constructing a hydraulic system model in AMEsim, a co-simulation model is integrated. This study proposes a PID control strategy optimized by the particle swarm optimization (PSO) algorithm for a backpropagation (BP) neural network and simulates the system’s step response for analysis. To address the vibration issues arising during the manipulator’s motion, B-spline curves are used for trajectory optimization to reduce vibrations. The PSO algorithm optimizes the connection weight matrix of the BP neural network, solving the potential problem of local minima during the training process of the BP neural network, thereby enhancing the global search capability, learning efficiency, and network performance. Simulation results indicate that compared to traditional BP+PID control, genetic algorithm (GA)+PID control, and whale optimization algorithm (WOA)-BP+PID control, the PSO-BP+PID algorithm control rapidly tunes the PID control parameters Kp, Ki, and Kd. Under the same step function conditions, the overshoot is only 1.37%, significantly lower than other methods, and the settling time is only 14 s. After stabilization, there is almost no error, demonstrating faster response speed, higher control accuracy, and stronger robustness. This research has theoretical value and reference significance for the control methods and improvements in EOD robotic manipulators.

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Adaptive neural PD controllers for mobile manipulator trajectory tracking

Cited by 4 publications

References 32 publications

Design and Control Simulation Analysis of Tender Tea Bud Picking Manipulator

Design and Control Simulation Analysis of Tender Tea Bud Picking Manipulator

Trajectory Planning and Attitude Control of Manipulator

Research on the Optimization of the PID Control Method for an EOD Robotic Manipulator Using the PSO Algorithm for BP Neural Networks

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