Neural-Learning-Based Telerobot Control With Guaranteed Performance

Yang, Chenguang; Wang, Xinyu; Cheng, Long; Ma, Hongbin

doi:10.1109/tcyb.2016.2573837

Cited by 282 publications

(153 citation statements)

References 30 publications

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“…Then, there exists the unknown approximation errors ( ) which has supremum ; that is, | ( )| ≤ . Equation (12) will be rewritten in the approximation equations as follows:…”

Section: Complexitymentioning

confidence: 99%

“…Recently, in order to solve the uncertain nonlinear problem in the system, the most authors adopted the fuzzy logical system or adaptive neural network (NN) control [9,10] to approximate the external disturbance [11] and model uncertainty [12,13]. In [14,15], the different stability problems of switched nonlinear systems are solved by using the fuzzy adaptive control.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Neural Network Control for Nonlinear Hydraulic Servo-System with Time-Varying State Constraints

2017

Complexity

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An adaptive neural network control problem is addressed for a class of nonlinear hydraulic servo-systems with time-varying state constraints. In view of the low precision problem of the traditional hydraulic servo-system which is caused by the tracking errors surpassing appropriate bound, the previous works have shown that the constraint for the system is a good way to solve the low precision problem. Meanwhile, compared with constant constraints, the time-varying state constraints are more general in the actual systems. Therefore, when the states of the system are forced to obey bounded time-varying constraint conditions, the high precision tracking performance of the system can be easily realized. In order to achieve this goal, the time-varying barrier Lyapunov function (TVBLF) is used to prevent the states from violating time-varying constraints. By the backstepping design, the adaptive controller will be obtained. A radial basis function neural network (RBFNN) is used to estimate the uncertainties. Based on analyzing the stability of the hydraulic servo-system, we show that the error signals are bounded in the compacts sets; the time-varying state constrains are never violated and all singles of the hydraulic servo-system are bounded. The simulation and experimental results show that the tracking accuracy of system is improved and the controller has fast tracking ability and strong robustness.

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“…Then, there exists the unknown approximation errors ( ) which has supremum ; that is, | ( )| ≤ . Equation (12) will be rewritten in the approximation equations as follows:…”

Section: Complexitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive Neural Network Control for Nonlinear Hydraulic Servo-System with Time-Varying State Constraints

2017

Complexity

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“…Automatic collision avoidance is achieved by the control design at the kinematic level exploiting the joint space redundancy. 2,3 The variable impedance actuators (VIA), series-elastic actuators (SEAs), 4 and variable-stiffness actuators (VSAs) 5 were used in these researches. In Qiu et al, 6 they combine vision compressive sensing, brain-machine reference commands, and adaptive fuzzy control to develop a teleportation control system for exoskeleton robots.…”

Section: Introductionmentioning

confidence: 99%

A universal algorithm for sensorless collision detection of robot actuator faults

Chen

Luo

2018

Advances in Mechanical Engineering

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When a robot is working properly, it is possible to collide with people or objects entering its working space. This research is different than usual control algorithm. It proposes a universal algorithm for sensorless collision detection of robot actuator faults to enhance the security of the robot. On the basis of the dynamic model, a classical friction model to ensure the accuracy of the whole dynamic model is introduced. This collision detection algorithm can conduct without any external sensors or acceleration and realize the real-time detection just needs to measure the motor current and the location information from the encoder of the robot joint. The value of external torque t ext was used to compare with the threshold to detect the collision. After using the proposed collision detection method, the two rotational (2R) planar manipulators can detect the slight collision reliably. The experimental results and performance comparisons show that this sensorless collision detection algorithm is simple and effective. It can be promoted to any other type of robot arm with more degrees of freedom.

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“…These intelligent control methods improve the performance of gyroscopes, so that the applications of MEMS gyroscopes are expanded. With the vigorous development of nonlinear system control methods [1][2][3][4][5][6][7][8], a variety of gyroscopic modal control methods emerged.…”

Section: Introductionmentioning

confidence: 99%

Minimal-Learning-Parameter Technique Based Adaptive Neural Sliding Mode Control of MEMS Gyroscope

Zhang

2017

Complexity

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This paper investigates an adaptive neural sliding mode controller for MEMS gyroscopes with minimal-learning-parameter technique. Considering the system uncertainty in dynamics, neural network is employed for approximation. Minimal-learningparameter technique is constructed to decrease the number of update parameters, and in this way the computation burden is greatly reduced. Sliding mode control is designed to cancel the effect of time-varying disturbance. The closed-loop stability analysis is established via Lyapunov approach. Simulation results are presented to demonstrate the effectiveness of the method.

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Neural-Learning-Based Telerobot Control With Guaranteed Performance

Cited by 282 publications

References 30 publications

Adaptive Neural Network Control for Nonlinear Hydraulic Servo-System with Time-Varying State Constraints

Adaptive Neural Network Control for Nonlinear Hydraulic Servo-System with Time-Varying State Constraints

A universal algorithm for sensorless collision detection of robot actuator faults

Minimal-Learning-Parameter Technique Based Adaptive Neural Sliding Mode Control of MEMS Gyroscope

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