Research on Control Strategy Technology of Upper Limb Exoskeleton Robots: Review

Machines, Journal Year: 2025, Volume and Issue: 13(3), P. 207 - 207

Published: March 3, 2025

Upper limb exoskeleton robots, as highly integrated wearable devices with the human body structure, hold significant potential in rehabilitation medicine, performance enhancement, and occupational safety health. The rapid advancement of high-precision, low-noise acquisition intelligent motion intention recognition algorithms has led to a growing demand for more rational reliable control strategies. Consequently, systems strategies robots are becoming increasingly prominent. This paper innovatively takes hierarchical system entry point comprehensively compares current technologies upper analyzing their applicable scenarios limitations. research still faces challenges such insufficient real-time limited individualized adaptation capabilities. It is recognized that no single traditional algorithm can fully meet interaction requirements between exoskeletons body. integration many advanced artificial intelligence into remains restricted. Meanwhile, quality closely related perception decision-making system. Therefore, combination multi-source information fusion cooperative methods expected enhance efficient human–robot personalized rehabilitation. Transfer learning edge computing enable lightweight deployment, ultimately improving work efficiency life end-users.

Language: Английский

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Research on Lower Limb Exoskeleton Trajectory Tracking Control Based on the Dung Beetle Optimizer and Feedforward Proportional–Integral–Derivative Controller

Actuators, Journal Year: 2024, Volume and Issue: 13(9), P. 344 - 344

Published: Sept. 6, 2024

The lower limb exoskeleton (LLE) plays an important role in production activities requiring assistance and load bearing. One of the challenges is to propose a control strategy that can meet requirements LLE trajectory tracking different scenes. Therefore, this study proposes (DBO–FPID) combines dung beetle optimizer (DBO) with feedforward proportional–integral–derivative controller (FPID) improve performance Lagrange method used establish dynamic model rod, it combined equations motor obtain transfer function model. Based on target compensation, designed achieve To best controller, DBO utilized perform offline parameter tuning PID controller. proposed compared (DBO–PID), particle swarm (PSO) FPID (PSO–FPID), PSO (PSO–PID) simulation joint module experiments. results show DBO–FPID has accuracy robustness scenes, which smallest sum absolute error (IAE), mean (MEAE), maximum (MAE), root square (RMSE). In addition, MEAE than 1.5 degrees unloaded tests 3.6 hip tests, only few iterations, showing great practical potential.

Language: Английский

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Closed-Form Continuous-Time Neural Networks for Sliding Mode Control with Neural Gravity Compensation

Robotics, Journal Year: 2024, Volume and Issue: 13(9), P. 126 - 126

Published: Aug. 23, 2024

This study proposes the design of a robust controller based on Sliding Mode Control (SMC) structure. The proposed controller, called Closed-Form Continuous-Time Neural Networks with Gravity Compensation (SMC-CfC-G), includes development an inverse model UR5 industrial robot, which is widely used in various fields. It also gravity vector using neural networks, outperforms obtained through traditional robot modeling. To develop compensator, feedforward Multi-Layer Perceptron (MLP) network was implemented. use (CfC) networks for robot’s introduced, allowing efficient modeling robot. behavior verified under load and torque disturbances at end effector, demonstrating its robustness against variations operating conditions. adaptability ability to maintain superior performance dynamic environments are highlighted, outperforming classic SMC, Proportional-Integral-Derivative (PID), controllers. Consequently, high-precision maximum error rate approximately 1.57 mm obtained, making it useful applications requiring high accuracy.

Language: Английский

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A Linear Quadratic Regulation Controller Based on Radial Basis Function Network Approximation

Electronics, Journal Year: 2024, Volume and Issue: 13(21), P. 4279 - 4279

Published: Oct. 31, 2024

This paper proposes a linear quadratic regulation (LQR) tracking control method based on radial basis function (RBF) that successfully compensates for the shortcomings of LQR method. The depends linearity model. Specifically, an RBF neural network is used to approximate and compensate nonlinear part controlled object in PID type-I, type-II type-III loops improve performance system. Through simulation different industrial systems, such as underdamped, overdamped critically damped significantly improves dynamic response indices, rise time settling time,

Language: Английский

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