Key Takeaways & Executive Findings
- •• Proposes a novel RBFNNA-HSMC method that integrates radial basis function neural networks with hierarchical sliding mode control to address trajectory tracking of elastic tendon-driven manipulators under model uncertainty and disturbances. • Demonstrates superior tracking accuracy compared to traditional model-based HSM control, with experimental maximum tracking errors below 2.593×10-3 rad and 1.624×10-3 rad for double-joint trajectory tracking. • Establishes closed-loop stability via Lyapunov stability theory, ensuring robustness against model inaccuracies and external disturbances. • Validates the effectiveness and adaptability of the proposed control method through simulations and experiments on a two-DOF ETDM, highlighting its potential for applications in human-robot collaboration and flexible robotics.
Abstract
Tracking control of tendon-driven manipulators has become a prevalent research area. However, the existence of flexible elastic tendons generates substantial residual vibrations, resulting in difficulties for trajectory tracking control of the manipulator. This paper proposes the radial basis function neural network adaptive hierarchical sliding mode control (RBFNNA-HSMC) method, which combines the dynamic model of the elastic tendon-driven manipulator (ETDM) with radial basis neural network adaptive control and hierarchical sliding mode control technology. The aim is to achieve trajectory tracking control of ETDM even under conditions of model inaccuracy and disturbance. The Lyapunov stability theory demonstrates the stability of the proposed RBFNNA-HSM controller. In order to assess the effectiveness and adaptability of the proposed control method, simulations and experiments were performed on a two-DOF ETDM. The RBFNNA-HSM method shows superior tracking accuracy compared to traditional model-based HSM control. The experiment shows that the maximum tracking error for ETDM double-joint trajectory tracking is below 2.593×10-3 rad and 1.624×10-3 rad, respectively.
1. Introduction
The concept of human-computer collaboration and human-computer fusion robots has ushered in a new generation of robots that prioritize human-robot interaction and integration, establishing a trend in the field of robotics [1, 2]. Today’s robots are required to function in semi-structured or unstructured environments, such as in medical care, space exploration, special equipment inspection and home services. The development of flexible joint manipulators (FJMs) has become a priority in the design of robotic arms to optimize characteristics like lightness, flexibility, safety, and energy efficiency [3–6]. Cable-driven manipulators, a type of FJMs, exhibit notable advantages, including motor location on the base, leading to reduced mass and motion inertia at the manipulator’s end.
The elastic tendon-driven manipulator (ETDM) configuration, introduced by Lens and Von Stryk et al., includes a spring as a preloading device for the cable in a cable-driven manipulator, providing backdrive capability, eliminating backlash, achieving a high load-to-weight ratio and promoting collaboration with mobile robots [7]. Its structure, with the spring and cable connected in series, closely resembles that of human tendons. The spring, which has a lower stiffness than the cable, converts kinetic energy into elastic potential energy quickly during accidental collisions, ensuring the safety of the operator.
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Yudong Zhang, Leiying He, Jianneng Chen, Bo Yan, Chuanyu Wu (2025). Neural Network Adaptive Hierarchical Sliding Mode Control for the Trajectory Tracking of a Tendon-Driven Manipulator. Chinese Journal of Mechanical Engineering. https://doi.org/10.1186/s10033-024-01172-9
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Frequently Asked Questions
What is the main contribution of this paper?
The paper proposes a novel control method, RBFNNA-HSMC, that combines radial basis function neural networks with hierarchical sliding mode control to achieve precise trajectory tracking for elastic tendon-driven manipulators, even under model uncertainties and disturbances.
How does the proposed method improve tracking accuracy?
The RBFNNA-HSMC method leverages neural networks to approximate model uncertainties and disturbances, while hierarchical sliding mode control ensures robust tracking. Experimental results show maximum tracking errors below 2.593×10-3 rad and 1.624×10-3 rad for double-joint trajectory tracking, outperforming traditional model-based HSM control.
What is the significance of the Lyapunov stability analysis?
The Lyapunov stability analysis rigorously proves that the proposed control law guarantees closed-loop stability, ensuring that the tracking errors converge to zero or a bounded region despite model inaccuracies and external disturbances.
What are the practical applications of this research?
The findings are applicable to various fields including human-robot collaboration, medical care, space exploration, and service robotics, where flexible and safe manipulators are required. The improved tracking control enhances the performance of tendon-driven manipulators in tasks like pick-and-place, rehabilitation, and inspection.
How was the proposed method validated?
The method was validated through both simulations and experiments on a two-DOF elastic tendon-driven manipulator. The results demonstrated superior tracking accuracy and adaptability compared to traditional model-based hierarchical sliding mode control.
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