Research on trajectory tracking control of 7-DOF picking manipulator

Sun, Cong; Cao, Liang

doi:10.1177/00368504211003383

Cited by 6 publications

(3 citation statements)

References 25 publications

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“…Te simulation results of robot arm 1 and robot arm 2 are similar to the simulation results of robot arm 3, and the simulation proof is not done here. Te simulation parameters are set as follows: the required manipulator 3 is θ [17][18][19][20]. It can be seen from Figures 3 and 4 that in the absence of external interference, adaptive neural network control and PID control can control the user's demand, and the input torque of PID control has a large change and vibration.…”

Section: Results Analysismentioning

confidence: 99%

Motion Trajectory Error of Robotic Arm Based on Neural Network Algorithm

Sem-Lin

2023

Journal of Control Science and Engineering

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In order to solve the problems of unstable motion and large trajectory tracking error of the manipulator when it is disturbed by the outside world, the author proposes an adaptive neural network manipulator motion trajectory error method. The author gives the dynamic equation of the manipulator and uses the positive feedback neural network to study the dynamic characteristics of the manipulator. An adaptive neural network control system is designed, and the stability and convergence of the closed-loop system are proved by the Lyapunov function. A schematic diagram of the manipulator model is established, and MATLAB/Simulink software is used to simulate the dynamic parameters of the manipulator. At the same time, it is compared and analyzed with the simulation results of the PID control system. Simulation results show that in robot arm 3, the expected motion trajectory is θ3 = 0.4cos(2πt), the initial condition θ(0) = [000]τ, the control parameter K = diag(40,40),40), the disturbance parameter τ’ = 20cos(πt), robot arm link parameters l1 = 0.62 m, l2 = 0.41 m, l3 = 0.34 m, m1 = 3.5, m2 = 2.5 kg, m3 = 2 kg, g = 9.82 m/s2, under t = 2s, the motion trajectory of the robotic arm is disturbed by the outside world, and the adaptive neural network is used to control the motion trajectory with a small tracking error, input torque ripple is small. Conclusion. The manipulator adopts the adaptive neural network control method, which can improve the control accuracy of the motion trajectory and weaken the jitter phenomenon of the manipulator motion.

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Section: Results Analysismentioning

confidence: 99%

Motion Trajectory Error of Robotic Arm Based on Neural Network Algorithm

Sem-Lin

2023

Journal of Control Science and Engineering

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“…In the design process of inertia parameter m d , the parameters b d and k d are kept unchanged, and the values of m d take 0.005, 0.05 and 0.15 respectively. [23][24][25][26] According to the above analysis, the contact force output by the picking robot under different inertia parameters can be obtained, as shown in Figure 8. It can be seen from Figure 8 that with the increase of inertia parameter m d , the contact force gradually increases, and the overshoot of contact force increases with the increase of inertia parameter.…”

Section: Parameter Self-tuning Of Picking Manipulator Control Systemmentioning

confidence: 99%

Research on the application of impedance control in flexible grasp of picking robot

Zhuang

Ding

et al. 2023

Advances in Mechanical Engineering

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Aiming at the damage of fruit picking robot during the picking process, an impedance control method of picking robot with parameter self-tuning is proposed. The rheological properties of fruit during picking by manipulator were studied by Burger’s viscoelasticity theory, and the variation characteristics of fruit deformation during picking, as well as the changing law between fruit contact force and deformation were obtained. On this basis, the impedance control system model of the picking manipulator is established, and the impedance controller is designed. Finally, by constructing the self-correcting change function of impedance parameters, the influence of different impedance parameters on the change of the contact force is studied, and then the control system is improved and designed. The simulation and experimental results show that the established impedance control system model can effectively realize the non-destructive grasping of fruit, and has high grasping accuracy. The expected grasping force is smoother, the overshoot of the contact force is greatly reduced, and the adjustment time is also shortened, which indicates that the control method proposed in this paper has certain application value in the control of picking robots.

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“…Fuzzy control is also often adopted to approximate non-linear uncertainties and suppress disturbance. In [23], a computing torque-fuzzy compensation controller is designed for a 7-DOF picking manipulator. This controller uses an adaptive fuzzy logic system to compensate for the uncertain part of the mechanical model, in which the parameters of the fuzzy logic system can be adjusted adaptively, which also makes it very robust to external disturbance.…”

Section: Introductionmentioning

confidence: 99%

Tele‐aiming control design for reconnaissance robot using a strong tracking multi‐model extended super‐twisting observer

Feng²,

Zhang

et al. 2022

IET Control Theory & Appl

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In this study, a tele‐aiming control system for reconnaissance robots is proposed, capable of enabling operators to quickly control the robot to aim at and track the manoeuvring target with random motion characteristics under measurement noise. To track such manoeuvring targets, a cascaded multi‐model super‐twisting observer is designed. First, the system uncertainty and the inertial impact force disturbance generated during rapid tele‐aiming are expanded into a lumped disturbance state. Subsequently, a strong tracking filter is employed to filter and estimate the system output and the lumped disturbance, and the output of the filter is input into the multi‐model extended state super‐twisting observer. The final second‐order state estimation and lumped disturbance estimation of the augmented system are conducted in accordance with the weighted sum of all super‐twisting observers. Lastly, a non‐singular terminal sliding mode controller is designed to compensate for the lumped disturbance and achieve the fast trajectory tracking. To verify the effectiveness of the proposed observer‐based controller, a series of manoeuvring target tracking experiments was performed. As revealed by the experimental results, the proposed controller can quickly control the reconnaissance robot to aim at and track the manoeuvring target with random abrupt motion characteristics under measurement noise disturbance.

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Research on trajectory tracking control of 7-DOF picking manipulator

Cited by 6 publications

References 25 publications

Motion Trajectory Error of Robotic Arm Based on Neural Network Algorithm

Motion Trajectory Error of Robotic Arm Based on Neural Network Algorithm

Research on the application of impedance control in flexible grasp of picking robot

Tele‐aiming control design for reconnaissance robot using a strong tracking multi‐model extended super‐twisting observer

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