Inverse Dynamics of Multilink Cable-Driven Manipulators With the Consideration of Joint Interaction Forces and Moments

Lau, Darwin; Oetomo, Denny; Halgamuge, Saman K.

doi:10.1109/tro.2015.2394498

Cited by 41 publications

(21 citation statements)

References 28 publications

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“…Unlike the multi-body dynamics [15], [16], [43] our approach releases the necessity of 3D motion capture and force plate which makes it possible to predict joint moments while getting rid of the restrictions of space and equipment.…”

Section: Discussionmentioning

confidence: 99%

Intelligent Prediction of Human Lower Extremity Joint Moment: An Artificial Neural Network Approach

Xiong

Zeng

et al. 2019

IEEE Access

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Human joint moment plays an important role in quantitative rehabilitation assessment and exoskeleton robot control. However, the existing moment prediction methods require kinematic and kinetic data of human body as input, and the measurement of them needs special equipment, which makes them unable to be used in an unconstrained environment. According to the situation, this paper develops a novel method where a small number of input variables selected by Elastic Net are used as the input of artificial neural network (ANN) to predict joint moments, which makes the prediction in daily life possible. The method is tested on the experimental data collected from eight healthy subjects that are running on a treadmill at a speed of 2, 3, 4, and 5 m/s, respectively. Taking the right lower limb's 10 electromyography (EMG) and 5 joints angle data as candidate variable sets, Elastic Net is used to obtain the variable coefficients of the right lower limb's four joint moments. The inputs of the ANN determined by the variable coefficients are used to train and predict the joint moments. Prediction accuracy is evaluated by using the normalized root-meansquare error (NRMSE %) and cross correlation coefficient (ρ) between the predicted joint moment and multibody dynamics moment. Results of our study suggest that the method can accurately predict joint moment (NRMSE < 7.89%, ρ > 0.9633) with only 5-6 EMG signals. In conclusion, this method can effectively reduce the input variables while keeping a certain precision, which makes the joint moment prediction simple and out of equipment limitation. This method may facilitate the researches on real-time gait analysis and exoskeleton robot control in motor rehabilitation. INDEX TERMS Joint moment prediction, artificial neural network, elastic net.

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Section: Discussionmentioning

confidence: 99%

Intelligent Prediction of Human Lower Extremity Joint Moment: An Artificial Neural Network Approach

Xiong

Zeng

et al. 2019

IEEE Access

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“…The trajectories of the CDSM are described by the model in Equation (14) under the control law in Equations (15) and (17), the robust control law described by Equation (18), the parameter adaptive law described by Equations (19) and (16) and the following sufficient condition (23) globally asymptotically converges to and , which implies convergence of and to zero and the boundedness of . 14under the control law in Equations (15) and (17), the robust control law described by Equation 21, the parameter adaptive law described by Equations (21), (16) and (22) [19], which depends on the upper bound of uncertainties of the system, the control gains described by Equation (24) are independent of this upper bound. Hence, the highlevel controller described in Theorem 2 eliminates the requirement of knowing the upper bound of uncertainties.…”

Section: Adaptive Robust Control Of Cable Tensionsmentioning

confidence: 99%

“…Proof of Theorem 1. Substituting Equation (15) into the dynamics model of the actuating cables and robot body described by Equation 6gives…”

Section: Theorem 2 the Tracking Error Trajectories Of The Cdsm Descrmentioning

confidence: 99%

See 1 more Smart Citation

Robust Adaptive Control of Fully Constrained Cable-Driven Serial Manipulator with Multi-Segment Cables Using Cable Tension Sensor Measurements

Lou

Lin

Quan

et al. 2021

Sensors

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The structure of the cable-driven serial manipulator (CDSM) is more complex than that of the cable-driven parallel manipulator (CDPM), resulting in higher model complexity and stronger structural and parametric uncertainties. These drawbacks challenge the stable trajectory-tracking control of a CDSM. To circumvent these drawbacks, this paper proposes a robust adaptive controller for an n-degree-of-freedom (DOF) CDSM actuated by m cables. First, two high-level controllers are designed to track the joint trajectory under two scenarios, namely known and unknown upper bounds of uncertainties. The controllers include an adaptive feedforward term based on inverse dynamics and a robust control term compensating for the uncertainties. Second, the independence of control gains from the upper bound of uncertainties and the inclusion of the joint viscous friction coefficient into the dynamic parameter vector are realised. Then, a low-level controller is designed for the task of tracking the cable tension trajectory. The system stability is analysed using the Lyapunov method. Finally, the validity and effectiveness of the proposed controllers are verified by experimenting with a three-DOF six-cable CDSM. In addition, a comparative experiment with the classical proportional–integral–derivative (PID) controller is carried out.

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“…1: Roboy cable-driven musculoskeletal robots the bone structure. Consequently, this creates a range of difficulties and challenges in the modelling [13], analysis [14]- [16] and control [17] of such systems.…”

Section: Introductionmentioning

confidence: 99%

CARDSFlow: An End-to-End Open-Source Physics Environment for the Design, Simulation and Control of Musculoskeletal Robots

Trendel¹,

Chan

Харченко³

et al. 2018

2018 IEEE-RAS 18th International Conference on Humanoid Robots (Humanoids)

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Motivated by the similar structure and actuation mechanism to humans and animals, the study of musculoskeletal robots has gained attention in recent years. However, the unilateral actuation property of muscles and high complexity of the mechanical system imposes great challenges on the design and control of such robots. An open-source realistic simulation platform for theoretical testing would therefore be advantageous for the research community of musculoskeletal robots. In this paper, an end-to-end open-source framework for the design, simulation and control of the general class of musculoskeletal robots (CARDSFlow) is presented. The framework consists of three advantageous features: 1) 3D computer-aided designs of musculoskeletal robots can be automatically converted for use with Gazebo and CASPR; 2) realistic physics simulation of musculoskeletal robots within Gazebo; and 3) integration of CASPR cable-driven robot controllers with CARDSFlow through the ROS platform. Simulation results on a two-link planar robot and the Roboy robot arm are presented to demonstrate the convenience to design and simulate musculoskeletal robots using CARDSFlow.

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Inverse Dynamics of Multilink Cable-Driven Manipulators With the Consideration of Joint Interaction Forces and Moments

Cited by 41 publications

References 28 publications

Intelligent Prediction of Human Lower Extremity Joint Moment: An Artificial Neural Network Approach

Intelligent Prediction of Human Lower Extremity Joint Moment: An Artificial Neural Network Approach

Robust Adaptive Control of Fully Constrained Cable-Driven Serial Manipulator with Multi-Segment Cables Using Cable Tension Sensor Measurements

CARDSFlow: An End-to-End Open-Source Physics Environment for the Design, Simulation and Control of Musculoskeletal Robots

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