Improving robotic impedance control performance employing a cascaded controller based on virtual dynamics model

Liu, Guanghui; Han, Bing

doi:10.1177/09544062211023537

Cited by 5 publications

(4 citation statements)

References 18 publications

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“…In this paper, impedance control is used to improve the compliant motion of the end of the manipulator [17]. Impedance control is to regard the tool and environment at the end of the manipulator as a mass-spring-damping system, and the impedance AI, Computer Science and Robotics Technology 6/20 controller as a mechanical impedance.…”

Section: Force-based Impedance Controllermentioning

confidence: 99%

Force/Position Hybrid Control Technology of Heavy-duty Long Manipulator Based on IPSMC

Weicong

Zhang

et al. 2022

ACRT

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In order to solve the problems of low precision, disturbance and unsmooth movement of heavy-duty long manipulator in working conditions, an IPSMC (improved power sliding mode control) based on force impedance control is proposed. First, the robot is organized and an accurate kinematics model is established and the motion characteristics of special manipulator system are analyzed. Combined with the dynamic model, the deviation of the position and velocity feedback from the expected value is converted by the force impedance controller, which makes the manipulator more flexible under the condition of low damping. At the same time, according to the position and speed feedback, an IPSMC is proposed, which uses the sliding mode control (SMC) to reduce the disturbance and oscillation in the working condition, so as to realize the control in the position space and the control in the force space. Finally, through Adams-Simulink co-simulation, the designed control system is tested. The results show that the force/position hybrid control strategy has good anti-interference ability for the long manipulator with large working range. While improving the flexibility of the manipulator, it also weakens the end chattering problem to some extent, enhances the robustness of the control system, and meets the requirements of working conditions.

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Section: Force-based Impedance Controllermentioning

confidence: 99%

Force/Position Hybrid Control Technology of Heavy-duty Long Manipulator Based on IPSMC

Weicong

Zhang

et al. 2022

ACRT

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“…40,41 Compared with the single-feedback control method, the dual-loop method has better performance with stabilization time, maximum overshoot, etc. [42][43][44] Effective parameter identification of the hydraulic system applied to the control system can also lead to enhanced control effects. 45,46 Model predictive control (MPC) is an appropriate method for hydraulic system control.…”

Section: Introductionmentioning

confidence: 99%

A dual-loop feedback trajectory tracking control for rock drilling hydraulically driven manipulator

Chen,

Wu,

Liao

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part C:

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This paper is devoted to proposing a feedback trajectory tracking control method for hydraulically driven rock drilling robotic manipulators. The rock drilling robot has a large-scale long boom and multiple joints (6R-2P), so the deformation of the arm may not be neglected. A kinematic model with the flexible deformation of the boom is developed by adding a modified coordinate transformation matrix to the general model. Each joint motion is determined by solving the inverse kinematic model in an optimization algorithm, which aims at minimizing the positional error and smoothing the motion. A dual-loop feedback controller is proposed to track the end-effector’s position and orientation. Based on the model predictive control (MPC) method, the inner-loop controller controls the cylinder motion by controlling the cylinder motion. The outer-loop controller uses the fuzzy-PID control to correct the position error which is caused by the inner-loop controller. Several reference trajectories are designed to validate the effectiveness of the proposed dual-loop control strategy, which demonstrates it has higher accuracy and lower control fluctuation than single-loop and other dual-loop controllers.

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“…2 In addition, a cascaded neural network with genetic algorithm re-optimization is further introduced in this study with the aim of providing new ideas for the control of robots with high accuracy requirements in working scenarios such as handling robots. 3 The study makes corresponding work on the dynamics model of industrial handling robots. The minimum time trajectory planning is applied to effectively improve the robot control accuracy in the handling scenario.…”

Section: Introductionmentioning

confidence: 99%

“…Lagrangian dynamical equations are well established in this regard and have good applicability for the dynamic characteristics of robots 2 . In addition, a cascaded neural network with genetic algorithm re‐optimization is further introduced in this study with the aim of providing new ideas for the control of robots with high accuracy requirements in working scenarios such as handling robots 3 . The study makes corresponding work on the dynamics model of industrial handling robots.…”

Section: Introductionmentioning

confidence: 99%

Design of nonlinear control system for motion trajectory of industrial handling robot

Zhao,

Zhang

2023

Adv Control Appl

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Industrial robot is a and multi‐output complex system with strong coupling and high nonlinearity. The motion control accuracy of the system is affected by many factors. To solve the difficulty in establishing the input and output characteristics of robot dynamics modeling, the robot motion model is established through the Lagrangian energy function. At the same time, the nonlinear relationship between angular velocity, angular acceleration, and robot torque is accurately expressed through improved cascaded neural network. In addition, the optimal time planning of the robot's trajectory in joint space is studied using multinomial interpolation method and the particle swarm optimization (PSO). In the simulation experiment, the effect of the proposed dynamic model fitting was outstanding. Under the mixed multinomial difference calculation planning, the angular position trajectories of the three joints changed very smoothly. In the data set application test, the average error of the PSO algorithm was 0.4061 mm and the average task time was 9.101 s, which were lower than other planning algorithms. Experiments showed that the Lagrangian dynamic model analysis based on genetic algorithm cascaded neural network and PSO trajectory scheduling method under mixed multinomial difference had better trajectory planning performance in handling tasks.

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Improving robotic impedance control performance employing a cascaded controller based on virtual dynamics model

Cited by 5 publications

References 18 publications

Force/Position Hybrid Control Technology of Heavy-duty Long Manipulator Based on IPSMC

Force/Position Hybrid Control Technology of Heavy-duty Long Manipulator Based on IPSMC

A dual-loop feedback trajectory tracking control for rock drilling hydraulically driven manipulator

Design of nonlinear control system for motion trajectory of industrial handling robot

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