Nonlinear Friction and Dynamical Identification for a Robot Manipulator with Improved Cuckoo Search Algorithm

Ding, Li; Li, Xingcheng; Li, Qilin; Chào, Y.

doi:10.1155/2018/8219123

Cited by 22 publications

(14 citation statements)

References 25 publications

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“…The TLRIP system can move in trajectories which have high and suddenly changing, position speed, acceleration and jerk. The LFM cannot cover these characteristics, especially at sudden motion reversal [21]. Therefore, the NLFM reflects a better description of the joint friction characteristics.…”

Section: Non-linear Friction Modelmentioning

confidence: 99%

“…Therefore, it is necessary to develop an accurate friction model to estimate the friction's coefficients in the joints in accordance with the dynamic behavior of positions, velocities and accelerations. NCFM, LFM, and NLFM estimation models were given in the papers [6], [17], [19]- [21]. To estimate the constant friction coefficients in the pendulum's joints of the TLRIP, different friction estimation models (NCFM, LFM, and NLFM) were examined in detail in [10].…”

Section: Friction Estimation Modelsmentioning

confidence: 99%

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A Comparative Study of the Joint Neuro-Fuzzy Friction Models for a Triple Link Rotary Inverted Pendulum

2020

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In this paper, Neuro-Fuzzy Friction Estimation Models (NFFEM) are developed to estimate the joint friction coefficients in a Triple Link Rotary Inverted Pendulum (TLRIP) system and compared with an Adaptive Friction Estimation Models (AFEM). The different versions of AFEMs and NFFEMs are generated based on each of the following friction estimation models: Non-Conservative Friction Model (NCFM), Linear Friction Model (LFM), and Non-Linear Friction Model (NLFM). The aim of this study is to obtain joint friction models which depend on both velocity and acceleration in a large range of motion trajectory that involves difficult and sudden large changes. In the proposed NFFEMs, joint velocities and accelerations of the TLRIP are used as the input variables of the Neuro-Fuzzy system trained by using a Radial Basis Function Artificial Neural Network (RBANN). Several experiments are conducted on TLRIP system to verify the NFFEMs. In order to determine the estimation performance of the friction models, total Root Mean Squared Errors (RMSE) between position simulation results obtained from each joint friction model and encoders in the experimental setup are computed. Based on the position RMSEs, the NFFEMs produces much better estimation results than the AFEMs. Among NFFEMs, the neuro-fuzzy nonlinear friction model (NFNLM) gives the best results. INDEX TERMS Triple link rotary inverted pendulum (TLRIP), neuro-fuzzy friction estimation and modelling.

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Section: Non-linear Friction Modelmentioning

confidence: 99%

Section: Friction Estimation Modelsmentioning

confidence: 99%

A Comparative Study of the Joint Neuro-Fuzzy Friction Models for a Triple Link Rotary Inverted Pendulum

2020

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“… M ( q ) ∈ ℜ n × n represents the manipulator inertia matrix which is positive definite; C ( q , q · ) q · ∈ ℜ n denotes the centrifugal and coriolis force; g ( q ) ∈ ℜ n is the gravity force vector. F ( q , q · ) is the vector of joint friction which comprises several friction terms as shown in following 35 :…”

Section: Preliminariesmentioning

confidence: 99%

Task-space regulation of rigid-link electrically-driven robots with uncertain kinematics using neural networks

Zhao

Wei

et al. 2021

Measurement and Control

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Extensive research efforts have been made to address the motion control of rigid-link electrically-driven (RLED) robots in literature. However, most existing results were designed in joint space and need to be converted to task space as more and more control tasks are defined in their operational space. In this work, the direct task-space regulation of RLED robots with uncertain kinematics is studied by using neural networks (NN) technique. Radial basis function (RBF) neural networks are used to estimate complicated and calibration heavy robot kinematics and dynamics. The NN weights are updated on-line through two adaptation laws without the necessity of off-line training. Compared with most existing NN-based robot control results, the novelty of the proposed method lies in that asymptotic stability of the overall system can be achieved instead of just uniformly ultimately bounded (UUB) stability. Moreover, the proposed control method can tolerate not only the actuator dynamics uncertainty but also the uncertainty in robot kinematics by adopting an adaptive Jacobian matrix. The asymptotic stability of the overall system is proven rigorously through Lyapunov analysis. Numerical studies have been carried out to verify efficiency of the proposed method.

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“…Regarding the joint torque, each joint is driven by an actuator (direct drive or gear drive). Indeed, the torque could be calculated through the current and torque constant [24]. With respect to the hexarotor, the forces and torques actuated by six propellers are expressed in the following form:…”

Section: System Descriptionmentioning

confidence: 99%

Dynamical Modelling and Robust Control for an Unmanned Aerial Robot Using Hexarotor with 2-DOF Manipulator

Ding

2019

International Journal of Aerospace Engineering

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The robust control issues in trajectory tracking of an unmanned aerial robot (UAR) are challenging tasks due to strong parametric uncertainties, large nonlinearities, and high couplings in robot dynamics. This paper investigates the dynamical modelling and robust control of an aerial robot using a hexarotor with a 2-degrees-of-freedom (DOF) manipulator in a complex aerial environment. Firstly, the kinematic model and dynamic model of the aerial robot are developed by the Euler-Lagrange method. Afterwards, a linear active disturbance rejection control is designed for the robot to achieve a high-accuracy trajectory tracking goal under heavy lumped disturbances. In this control scheme, the modelling uncertainties and external disturbances are estimated by a linear extended state observer, and the high tracking precision is guaranteed by a proportion-differentiation (PD) feedback control law. Meanwhile, an artificial intelligence algorithm is applied to adjust the control parameters and ensure that the state variables of the robot converge to the references smoothly. Furthermore, it requires no detailed knowledge of the bounds on unknown dynamical parameters. Lastly, numerical simulations and experiments validate the efficiency and advantages of the proposed method.

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Nonlinear Friction and Dynamical Identification for a Robot Manipulator with Improved Cuckoo Search Algorithm

Cited by 22 publications

References 25 publications

A Comparative Study of the Joint Neuro-Fuzzy Friction Models for a Triple Link Rotary Inverted Pendulum

A Comparative Study of the Joint Neuro-Fuzzy Friction Models for a Triple Link Rotary Inverted Pendulum

Task-space regulation of rigid-link electrically-driven robots with uncertain kinematics using neural networks

Dynamical Modelling and Robust Control for an Unmanned Aerial Robot Using Hexarotor with 2-DOF Manipulator

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