Position control for a two-joint robot finger system driven by pneumatic artificial muscles

Liu, Zhao; Ge, Linlin; Wang, Tao

doi:10.1177/0142331216683246

Cited by 6 publications

(5 citation statements)

References 30 publications

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“…The control algorithm improves the dynamic response performance to a certain extent. To speed up convergence, a PD-type iterative learning control law was devised (Zhao et al, 2018). The gain matrix is modified in real time to shorten the correction interval and overcome the problem of slow convergence of system disturbances, but it is less stable.…”

Section: Related Workmentioning

confidence: 99%

Self-Tuning Control of Manipulator Positioning Based on Fuzzy PID and PSO Algorithm

Liu

Yun

et al. 2022

Front. Bioeng. Biotechnol.

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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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Section: Related Workmentioning

confidence: 99%

Self-Tuning Control of Manipulator Positioning Based on Fuzzy PID and PSO Algorithm

Liu

Yun

et al. 2022

Front. Bioeng. Biotechnol.

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“…The authors have achieved a high grip force but an extended hand closing time not physiologically relevant to a human hand. Zhao et al (2018) have addressed the position control of a robotic finger with two joints, utilizing the driving power produced by pneumatic artificial muscles. The response time of the closed-loop system is 3-4 seconds.…”

Section: Introductionmentioning

confidence: 99%

“…Zhao et al (2018) have addressed the position control of a robotic finger with two joints, utilizing the driving power produced by pneumatic artificial muscles. The response time of the closed-loop system is 3–4 seconds.…”

Section: Introductionmentioning

confidence: 99%

Bond graph modeling with linear quadratic integral control synthesis of a robotic digit in a human impaired hand for anthropomorphic coordination

Iqbal

Imtiaz

Mahmood

2022

Transactions of the Institute of Measurement and Control

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The movement coordination of the robotic digit(s) with the central nervous system (CNS) and the natural digit(s) is a complex task that needs to be executed successfully in an anthropomorphic hand. The task is challenging to resolve because of the CNS. We developed a theoretical framework for the biomechanical model of a partially impaired human hand utilizing the bond graph modeling technique by incorporating inertia, muscle, and visco-elastic dynamics. The research presents a partially impaired human hand model with a robotic digit and four natural digits having 21 degrees of freedom. We formulated a linear quadratic Gaussian (LQG) integral control technique for the 21st-order model to regulate the flexion and extension movement of the robotic digit while considering the disturbances. We have simulated the modeling scheme in MATLAB/Simulink. The flexion and extension movement and the angular velocity of the robotic finger are shown to be following all the physiological constraints of a natural finger. The settling time is achieved at 1.6 seconds, with a maximum flexion angle of 0.135 rad. The sensitivity analysis shows that the model is robust against disturbances. The simulation results exhibit the application of this scheme toward upper limb rehabilitation and improvement in prosthetic and exoskeleton designs.

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“…25 An ADRC was designed for at the position control of the finger of a two-joint robot driven by pneumatic artificial muscle, which solved the decoupling problem of double input and double output and achieved stable and high-precision control. 26 Furthermore, a nonlinear ADRC controller was designed for trajectory tracking of robotic manipulators. The robustness of this method to parameter uncertainties and disturbances was discussed.…”

Section: Introductionmentioning

confidence: 99%

Control system of the six-axis serial manipulator based on active disturbance rejection control

Liu

Wang

2020

International Journal of Advanced Robotic Systems

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This study considers the problems of manipulators with high coupling, parameter uncertainties, and external disturbances. A six-axis serial manipulator control system based on active disturbance rejection control strategy is proposed without the requirement of the exact dynamic model. First, the operating circuit of the manipulator joint motor is analyzed, and the mathematical model of the direct-current torque motor is established. Second, the components of active disturbance rejection control are designed, and a new nonlinear function is selected to construct the extended state observer and nonlinear state error feedback control law. Then, Kalman filter is introduced into an extended state observer to estimate the disturbance efficiently. Finally, the proportion–integration–differentiation control, traditional active disturbance rejection control, and improved active disturbance rejection control are simulated and compared under the same input signal. The results show that the proposed control strategy has good dynamic performance and uncertain disturbance robustness, which proves the effectiveness of the proposed method.

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Position control for a two-joint robot finger system driven by pneumatic artificial muscles

Cited by 6 publications

References 30 publications

Self-Tuning Control of Manipulator Positioning Based on Fuzzy PID and PSO Algorithm

Self-Tuning Control of Manipulator Positioning Based on Fuzzy PID and PSO Algorithm

Bond graph modeling with linear quadratic integral control synthesis of a robotic digit in a human impaired hand for anthropomorphic coordination

Control system of the six-axis serial manipulator based on active disturbance rejection control

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