Adaptive sliding mode robust control of manipulator driven by tendon-sheath based on HJI theory

Zhang, Shixuan; Wang, Wanqi; Xu, Zhigang; Shang, Dongyang; Yin, Meng

doi:10.1177/00202940211067176

Cited by 3 publications

(1 citation statement)

References 25 publications

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“…Concurrent learning and stabilization is always a critical ability of nonlinear systems. [22][23][24] As a model learning methodology, the DAEM can improve the performance of control systems by adjusting the feedback control law. However, this will bring the same weaknesses as adaptive control, poor transient performance, and vulnerability to external disturbances.…”

Section: Introductionmentioning

confidence: 99%

A dual adaptive robust control for nonlinear systems with parameter and state estimation

Chen,

Tao,

Yao

2023

Measurement and Control

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Stabilization and learning are imperative to the high-performance feedback control of nonlinear systems. A dual adaptive robust control (DARC) scheme is proposed for nonlinear systems with model uncertainties to achieve a desired level of performance. Only the output of the nonlinear system is accessible in this work, all the states and parameters are learned online. Firstly, the DARC uses the prior physical bounds of systems to design a discontinuous projection with update rate limits which confines the bounds of parameter and state estimation. Then robustness of the nonlinear system can be guaranteed by the deterministic robust control (DRC) method. Secondly, a dual adaptive estimation mechanism (DAEM) is developed to learn the unknown parameters and states of systems. One part of the DAEM is the bounded gain forgetting (BGF) estimator, which is developed to handle inaccurate parameters and parametric variations. The other is the adaptive unscented Kalman filter (AUKF) synthesized for state estimation. The AUKF contains a statistic estimator based on the maximum a posterior (MAP) rule to estimate the unknown covariance matrix. Finally, simulation results illustrate the effectiveness of the suggested method.

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Section: Introductionmentioning

confidence: 99%

A dual adaptive robust control for nonlinear systems with parameter and state estimation

Chen,

Tao,

Yao

2023

Measurement and Control

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Hierarchical Sliding Mode Control for the Trajectory Tracking of a Tendon-Driven Manipulator

Zhang

Yan

et al. 2023

Journal of Mechanisms and Robotics

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The tracking control of tendon-driven manipulators has recently become a hot topic. However, the flexible elastic tendon introduces greater residual vibration, making it more difficult to control the trajectory tracking of the manipulator. In this paper, a dynamics model of the elastic tendon-driven manipulator (ETDM) that considers motion coupling is established. A hierarchical sliding mode control (HSMC) method is proposed to realize the trajectory tracking control of the ETDM. On the basis of the Lyapunov design method, the actuator sub-sliding manifold is defined as the first sliding manifold. The first sliding manifold is then used to construct the joint side sub-sliding manifold. Furthermore, the total sliding manifold is established based on the joint side sliding manifold and the actuator's sliding manifold. The stability of the proposed HSMC is proved using Lyapunov stability theory. Finally, simulations and experiments are performed on a two-degree-of-freedom ETDM tracking desired trajectories to demonstrate the effectiveness of the proposed HSMC method. The proposed HSMC exhibits higher tracking accuracy compared with proportionalintegralderivative, and adaptive second-order fast nonsingular terminal sliding mode (SOFNTSM) controls in the simulations. The introduction of different disturbances reveals that HSMC has better robustness than proportionalintegralderivative control. Experimental results show that the maximum error of trajectory tracking is less than 0.025 rad.

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Robust dynamic control algorithm for uncertain powered wheelchairs based on sliding neural network approach

Bakouri,

Alqarni,

Alanazi

et al. 2023

MATH

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<abstract><p>The dynamic model of mobile wheelchair technology requires developing and implementing an intelligent control system to improve protection, increasing performance efficiency, and creating precise maneuvering in indoor and outdoor spaces. This work aims to design a robust tracking control algorithm based on a reference model for operating the kinematic model of powered wheelchairs under the variation of system parameters and unknown disturbance signals. The control algorithm was implemented using the pole placement method in combination with the sliding mode control (PP-SMC) approach. The design also adopted a neural network approach to eliminate system uncertainties from perturbations. The designed method utilized the sinewave signal as an essential input signal to the reference model. The stability of a closed-loop control system was achieved by adopting the Goa reaching law. The performance of the proposed tracking control system was evaluated in three scenarios under different conditions. These included assessing the tracking under normal operation conditions, considering the tracking performance by changing the dynamic system's parameters and evaluating the control system in the presence of uncertainties and external disturbances. The findings demonstrated that the proposed control method efficiently tracked the reference signal within a small error based on mean absolute error (MAE) measurements, where the range of MAE was between 0.08 and 0.12 in the presence of uncertainties or perturbations.</p></abstract>

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Adaptive sliding mode robust control of manipulator driven by tendon-sheath based on HJI theory

Cited by 3 publications

References 25 publications

A dual adaptive robust control for nonlinear systems with parameter and state estimation

A dual adaptive robust control for nonlinear systems with parameter and state estimation

Hierarchical Sliding Mode Control for the Trajectory Tracking of a Tendon-Driven Manipulator

Robust dynamic control algorithm for uncertain powered wheelchairs based on sliding neural network approach

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