Robust GRBF Static Neurocontroller With Switch Logic for Control of Robot Manipulators

Mulero-Martínez, Juan Ignacio

doi:10.1109/tnnls.2012.2196053

Cited by 18 publications

(11 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the simulation study, the state observer is designed as (14) and (15). The compounded disturbance is estimated using the disturbance observer which is proposed as (20).…”

Section: Simulation Resultsmentioning

confidence: 99%

“…The state observer (14) with (15) is designed for the robot manipulator. The unknown compounded disturbance is estimated using the disturbance observer (20).…”

Section: Theoremmentioning

confidence: 99%

“…When dealing with system uncertainties, neural networks (NNs), being a universal approximator with learning ability, can be employed to compensate for the uncertain nonlinear dynamics in the robot manipulator [13][14][15]. In [16], an adaptive control was studied for robot manipulators with NN based compensation of frictional uncertainties.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Adaptive Neural Output Feedback Control for Uncertain Robot Manipulators with Input Saturation

Mei

Cheng-jiang

2017

Complexity

View full text Add to dashboard Cite

This paper presents an adaptive neural output feedback control scheme for uncertain robot manipulators with input saturation using the radial basis function neural network (RBFNN) and disturbance observer. First, the RBFNN is used to approximate the system uncertainty, and the unknown approximation error of the RBFNN and the time-varying unknown external disturbance of robot manipulators are integrated as a compounded disturbance. Then, the state observer and the disturbance observer are proposed to estimate the unmeasured system state and the unknown compounded disturbance based on RBFNN. At the same time, the adaptation technique is employed to tackle the control input saturation problem. Utilizing the estimate outputs of the RBFNN, the state observer, and the disturbance observer, the adaptive neural output feedback control scheme is developed for robot manipulators using the backstepping technique. The convergence of all closed-loop signals is rigorously proved via Lyapunov analysis and the asymptotically convergent tracking error is obtained under the integrated effect of the system uncertainty, the unmeasured system state, the unknown external disturbance, and the input saturation. Finally, numerical simulation results are presented to illustrate the effectiveness of the proposed adaptive neural output feedback control scheme for uncertain robot manipulators.

show abstract

“…In the simulation study, the state observer is designed as (14) and (15). The compounded disturbance is estimated using the disturbance observer which is proposed as (20).…”

Section: Simulation Resultsmentioning

confidence: 99%

“…The state observer (14) with (15) is designed for the robot manipulator. The unknown compounded disturbance is estimated using the disturbance observer (20).…”

Section: Theoremmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive Neural Output Feedback Control for Uncertain Robot Manipulators with Input Saturation

Mei

Cheng-jiang

2017

Complexity

View full text Add to dashboard Cite

show abstract

“…The adaptive robust controllers based on hybrid neural networks can exhibit good properties as an attempt to cope with the problem of modeling uncertainty parameters and external disturbances. The neural networks can deal with the unknown dynamics problems of the rubber unstacking robot control system by exploiting their universal approximation ability [8][9][10]. At the same time, the approximate error of the neural network and the interference of the external system are estimated and compensated by adding the adaptive robust term in the controller [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Multiobjective Trajectory Optimization and Adaptive Backstepping Control for Rubber Unstacking Robot Based on RFWNN Method

Liang

Liu

2018

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Multiobjective trajectory optimization and adaptive backstepping control method based on recursive fuzzy wavelet neural network (RFWNN) are proposed to solve the problem of dynamic modeling uncertainties and strong external disturbance of the rubber unstacking robot during recycling process. First, according to the rubber viscoelastic properties, the Hunt-Crossley nonlinear model is used to construct the robot dynamics model. Then, combined with the dynamic model and the recycling process characteristics, the multiobjective trajectory optimization of the rubber unstacking robot is carried out for the operational efficiency, the running trajectory smoothness, and the energy consumption. Based on the trajectory optimization results, the adaptive backstepping control method based on RFWNN is adopted. The RFWNN method is applied in the main controller to cope with time-varying uncertainties of the robot dynamic system. Simultaneously, an adaptive robust control law is developed to eliminate inevitable approximation errors and unknown disturbances and relax the requirement for prior knowledge of the controlled system. Finally, the validity of the proposed control strategy is verified by experiment.

show abstract

“…It has been proved that the fuzzy logic systems (FLSs) and neural networks (NNs) can approximate arbitrary nonlinear continuous functions to a given accuracy on a closed set [30,31]. Mulero-Martinez [32] proposed a new Gaussian radial basis function (GRBF) static neurocontroller, which is a two-stage controller acting in a supervisory fashion by means of a switching logic and allowing arbitration between a neural network (NN) and a robust proportionalderivative controller. This structure is intended to reduce the effects of the curse of dimensionality in multidimensional systems by fully exploiting the mechanical properties of the robot manipulator.…”

Section: Introductionmentioning

confidence: 99%

Design of Adaptive Switching Controller for Robotic Manipulators with Disturbance

Yang

Fei

Wang

et al. 2016

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Two adaptive switching control strategies are proposed for the trajectory tracking problem of robotic manipulator in this paper. The first scheme is designed for the supremum of the bounded disturbance for robot manipulator being known; while the supremum is not known, the second scheme is proposed. Each proposed scheme consists of an adaptive switching law and a PD controller. Based on the Lyapunov stability theorem, it is shown that two new schemes can guarantee tracking performance of the robotic manipulator and be adapted to the alternating unknown loads. Simulations for two-link robotic manipulator are carried out and show that the two schemes can avoid the overlarge input torque, and the feasibility and validity of the proposed control schemes are proved.

show abstract

Robust GRBF Static Neurocontroller With Switch Logic for Control of Robot Manipulators

Cited by 18 publications

References 17 publications

Adaptive Neural Output Feedback Control for Uncertain Robot Manipulators with Input Saturation

Adaptive Neural Output Feedback Control for Uncertain Robot Manipulators with Input Saturation

Multiobjective Trajectory Optimization and Adaptive Backstepping Control for Rubber Unstacking Robot Based on RFWNN Method

Design of Adaptive Switching Controller for Robotic Manipulators with Disturbance

Contact Info

Product

Resources

About