External force estimation for robot manipulator based on a LuGre-linear-hybrid friction model and an improved square root cubature Kalman filter

Wang, Jiacai; Chen, Jiaoliao; Zhang, Libin; Xu, Fang; Zhi, Lewei

doi:10.1108/ir-03-2022-0057

Cited by 8 publications

(7 citation statements)

References 48 publications

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“…After substituting the 2,000 sets of test data for each joint into the trained torque prediction model based on PSO-LSTM deep learning, we obtained the torque predictions for the 6 joints of the IR based on PSO-LSTM deep learning. To validate the accuracy of the proposed method in predicting the joint torque of IR, we applied the classic LS methods (Lee et al , 2020; Bingül and Karahan, 2011; Wang et al , 2023) to identify the parameters of the dynamic model in equation (1) for linear regression problem-solving. After obtaining the minimal inertia parameters, we calculated the torque for the six joints of the IR and performed a comparative validation.…”

Section: Resultsmentioning

confidence: 99%

“…Figure 6 The ABB robot and its link coordinate system (Lee et al, 2020;Bingül and Karahan, 2011;Wang et al, 2023) to identify the parameters of the dynamic model in equation ( 1) for linear regression problem-solving. After obtaining the minimal inertia parameters, we calculated the torque for the six joints of the IR and performed a comparative validation.…”

Section: Data Acquisition and Filteringmentioning

confidence: 99%

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Joint torque prediction of industrial robots based on PSO-LSTM deep learning

Xiao,

Fu,

Wang

et al. 2024

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Purpose Because of the key role of joint torque in industrial robots (IRs) motion performance control and energy consumption calculation and efficiency optimization, the purpose of this paper is to propose a deep learning torque prediction method based on long short-term memory (LSTM) recurrent neural networks optimized by particle swarm optimization (PSO), which can accurately predict the the joint torque. Design/methodology/approach The proposed model optimized the LSTM with PSO algorithm to accurately predict the IRs joint torque. The authors design an excitation trajectory for ABB 1600–10/145 experimental robot and collect its relative dynamic data. The LSTM model was trained with the experimental data, and PSO was used to find optimal number of LSTM nodes and learning rate, then a torque prediction model is established based on PSO-LSTM deep learning method. The novel model is used to predict the robot’s six joint torque and the root mean error squares of the predicted data together with least squares (LS) method were comparably studied. Findings The predicted joint torque value by PSO-LSTM deep learning approach is highly overlapped with those from real experiment robot, and the error is quite small. The average square error between the predicted joint torque data and experiment data is 2.31 N.m smaller than that with the LS method. The accuracy of the novel PSO-LSTM learning method for joint torque prediction of IR is proved. Originality/value PSO and LSTM model are deeply integrated for the first time to predict the joint torque of IR and the prediction accuracy is verified.

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Section: Resultsmentioning

confidence: 99%

Section: Data Acquisition and Filteringmentioning

confidence: 99%

Joint torque prediction of industrial robots based on PSO-LSTM deep learning

Xiao,

Fu,

Wang

et al. 2024

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“…The traditional model-based control methods usually require an exact system model. [31][32][33] However, robotic manipulator is a complicated dynamic system containing uncertainties and most of them are impossible to be modeled. Techniques that are able to reduce the dependence on the model parameters are continuously explored.…”

Section: Introductionmentioning

confidence: 99%

“…Unlike Zaare et al 20,25,26 that only rely on SMC to keep consistent and eventually bounded, we add PPC in SMC to promote steady-state and transient performance of the system. Unlike Zhou et al [31][32][33] whose controller highly depends on the model parameters, we adopt model-free control method to eliminate the dependency on model parameters during the control design process as well as decrease the complexity and difficulty in controller design.…”

Section: Introductionmentioning

confidence: 99%

Model-free sliding mode prescribed performance control of robotic manipulator based on new reaching law

Deng,

Wen,

Yang

2023

International Journal of Advanced Robotic Systems

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The article proposes a model-free sliding mode prescribed performance control method for robotic manipulator. For accelerating the errors converging rate as well as bringing down the real-time control torque, an error-driven nonsingular fast terminal sliding mode is employed in this control method. Besides, a modified power reaching law is newly designed, which is able to offer better chattering elimination performance than traditional power reaching law. To solve the problem that the controller’s performance highly relies on the accuracy of robotic model, a model-free control idea is introduced where an ultra-local model is established and the estimation of time delay is used to approximate the unknown part of the ultra-local model. Considering the transient performance is a significant element that effects the using security and system stability, the technique of prescribed performance control is introduced to limit the state errors in a prescribed region. The closed-loop stability proof of the whole system is achieved according to Lyapunov’s stability theorem. The proposed controller’s superiority and feasibility compared with other controllers are demonstrated by numerical simulations.

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“…To avoid the use of the additional sensors, external interaction forces can be estimated using an observer-based approach such as the momentum observer [5], [6], the disturbance observer (DOB) [7], the modified version of the extended state observer [8], the extended sliding mode observer [9], and the finite-time high-order observer [10]. Recently, the Kalman filter [11], [12] has been applied to improve noise rejection. The observer can be chosen by considering the complexity of the algorithm and the difficulty of parameter tuning.…”

Section: Introductionmentioning

confidence: 99%

Safety and Performance of Industrial Collaborative Robots: Bounded Compensation in Precision Motion Control

Ko¹,

Lee²,

Lee³

et al. 2023

Preprint

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<p>The safety and control performance of industrial collaborative robots must be ensured while they are executing repetitive and precise tasks alongside human workers. This study proposes a new motion control structure to enhance the safety by demonstrating full-body compliance while achieving control performance as accurate as that of conventional industrial controllers. For precise motion, the proposed approach introduces a nominal model and compensates for disturbances caused by model uncertainty while bounding the compensation input for the safe interaction. Furthermore, to reduce the interaction forces without compromising the control performances, a method for designing the compensation bound based on the Gaussian Process (GP) is proposed. The proposed approach can achieve a uniformly ultimately boundedness (UUB) in the tracking scenario and asymptotic stability in the regulation scenario. The effectiveness of the proposed approach is validated under various conditions through experiments conducted using a 6-DOF industrial collaborative robot.</p>

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External force estimation for robot manipulator based on a LuGre-linear-hybrid friction model and an improved square root cubature Kalman filter

Cited by 8 publications

References 48 publications

Joint torque prediction of industrial robots based on PSO-LSTM deep learning

Joint torque prediction of industrial robots based on PSO-LSTM deep learning

Model-free sliding mode prescribed performance control of robotic manipulator based on new reaching law

Safety and Performance of Industrial Collaborative Robots: Bounded Compensation in Precision Motion Control

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