Dynamic parameter identification of upper‐limb rehabilitation robot system based on variable parameter particle swarm optimisation

Wang, Jin Lei; Li, Yafeng; An, Aimin

doi:10.1049/iet-csr.2020.0023

Cited by 6 publications

(3 citation statements)

References 16 publications

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“…But the accuracy is closely related to trajectory, and the polynomial fitting accuracy has a great influence on identification accuracy. Aiming at system uncertainty and unknown dynamic parameters of the rehabilitation robot, Jinlei Wang [16] used an improved particle swarm optimization algorithm to identify the model and adjusted the parameters of optimization algorithm, which improved the accuracy. However, improper parameter adjustment will affect the convergence speed and stability of the algorithm.…”

Section: Introductionmentioning

confidence: 99%

Parameters identification and contact interaction control of redundant robot based on dynamic model

Li,

Bian,

Wang

2024

Phys. Scr.

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Human-redundant robot contact interaction control based on joint current is conducted. This is a sensorless control method. The key issues involved are dynamic parameters identification and interaction control strategy of robot. Firstly, kinematics model and linear dynamic model of robot are established and dynamic parameter matrix to be identified is obtained. Pattern search method is adopted to optimize motion trajectory of robot. The least square method is used to obtain dynamic parameters. The results indicate that the calculated current has a consistent trend with actual measured value, and the current error is small. In order to achieve independent control in null space, motion decoupling model of robot is established, and impedance control is applied to null space. When the force is applied to robot, based on impedance control and joint current changes, robot can move along the force direction, so the motion in null space can be controlled. Aiming at null space contact control during end force control task, comprehensive interaction control based on force sensor and joint current is conducted. The results show that robot can also move in compliance with human hand and achieve compliant interaction of null space when force is applied to the joint space.

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Section: Introductionmentioning

confidence: 99%

Parameters identification and contact interaction control of redundant robot based on dynamic model

Li,

Bian,

Wang

2024

Phys. Scr.

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“…Wang et al. designed a variable‐parameter particle swarm optimisation algorithm to identify the parameters of the dynamic model, which improved the identification accuracy [22]. Neural network has the characteristics of infinite approximation to any non‐linear function, and has a fast convergence speed, which is very suitable for the control of robotic system with highly non‐linear characteristics [23–25].…”

Section: Introductionmentioning

confidence: 99%

A new control for the pneumatic muscle bionic legged robot based on neural network

Qin

Zhou

et al. 2022

IET Cyber-Syst and Robotics

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The bionic joints composed of pneumatic muscles (PMs) can simulate the motion of biological joints. However, the PMs themselves have non-linear characteristics such as hysteresis and creep, which make it difficult to achieve high-precision trajectory tracking control of PM-driven robots. In order to effectively suppress the adverse effects of nonlinearity on control performance and improve the dynamic performance of PM-driven legged robot, this study designs a double closed-loop control structure based on neural network. First, according to the motion model of the bionic joint, a mapping model between PM contraction force and joint torque is proposed. Second, a control strategy is designed for the inner loop of PM contraction force and the outer loop of bionic joint angle. In the inner control loop, a feedforward neuron Proportional-Integral-Derivative controller is designed based on the PM three-element model. In the control outer loop, a sliding mode robust controller with local model approximation is designed by using the radial basis function neural network approximation capability. Finally, it is verified by simulation and physical experiments that the designed control strategy is suitable for humanoid motion control of antagonistic PM joints, and it can satisfy the requirements of reliability, flexibility, and bionics during human-robot collaboration.

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“…(iii) Most improved PSO algorithms used the maximum number of iterations as the algorithm termination condition [25,26], and the effect of online identification cannot be achieved because only the parameter values of some points are identified. This paper adopts the method of resetting the number of iterations to make the algorithm termination condition change with the change of the system running time, so that it can identify each sampling point and meet the requirements of online identification.…”

Section: Introductionmentioning

confidence: 99%

Fast Backfire Double Annealing Particle Swarm Optimization Algorithm for Parameter Identification of Permanent Magnet Synchronous Motor

Wen¹,

Shi²,

Liao³

et al. 2021

PIER M

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When particle swarm optimization (PSO) is used to identify the parameters of permanent magnet synchronous motor (PMSM), the movement of particles is not selective, which makes the algorithm easy to fall into the local optimum, and the accuracy is poor. The simulated annealing particle swarm optimization (SAPSO) improves the accuracy and evolution speed, but SAPSO has redundant iteration problems. To solve these problems, a motor parameter identification method based on fast backfire double anneal particle swarm optimization (FBDAPSO) is proposed. By reducing the optimization time and quickly tempering and annealing the "misunderstood" difference, the motor adjustable model and fitness function are designed, and the number of iterations is constantly reset to achieve the effect of online identification. Under different working conditions, simulated and experimental results show that the proposed method can quickly and accurately identify the four parameters of the motor's stator, winding resistance, stator winding d-axis inductance, stator winding q-axis inductance, and permanent magnet flux linkage at the same time, compared with the traditional method of parameter identification, and it has better accuracy, rapidity, and robustness.

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Dynamic parameter identification of upper‐limb rehabilitation robot system based on variable parameter particle swarm optimisation

Cited by 6 publications

References 16 publications

Parameters identification and contact interaction control of redundant robot based on dynamic model

Parameters identification and contact interaction control of redundant robot based on dynamic model

A new control for the pneumatic muscle bionic legged robot based on neural network

Fast Backfire Double Annealing Particle Swarm Optimization Algorithm for Parameter Identification of Permanent Magnet Synchronous Motor

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