scholarly journals Neural Networks-based Adaptive State Feedback Control of Robot Manipulators

2007 ◽  
Vol 2 (4) ◽  
pp. 328 ◽  
Author(s):  
Ghania Debbache ◽  
Abdelhak Bennia ◽  
Noureddine Goléa
Keyword(s):  
Neural Networks ◽  
State Feedback ◽  
Robot Manipulator ◽  
Robot Manipulators ◽  
External Disturbance ◽  
Nonlinear Function ◽  
State Feedback Control ◽  
Robot Dynamics ◽  
Robust Controller ◽  
Control Scheme

This paper proposes an adaptive control suitable for motion control of robot manipulators with structured and unstructured uncertainties. In order to design an adaptive robust controller, with the ability to compensate these uncertainties, we use neural networks (NN) that have the capability to approximate any nonlinear function over a compact space. In the proposed control scheme, we need not derive the linear formulation of robot dynamic equation and tune the parameters. To reduce the NNs complexity, we consider the properties of robot dynamics and the decomposition of the uncertainties terms. The proposed controller is robust against uncertainties and external disturbance. The validity of the control scheme is demonstrated by computer simulations on a two-link robot manipulator.

scholarly journals Nonlinear pd controllers with gravity compensation for robot manipulators

2014 ◽  
Vol 14 (1) ◽  
pp. 141-150 ◽  
Author(s):  
Jianfeng Huang ◽  
Chengying Yang ◽  
Jun Ye
Keyword(s):  
Degrees Of Freedom ◽  
Position Control ◽  
Robot Manipulator ◽  
Robot Manipulators ◽  
Nonlinear Function ◽  
Step Response ◽  
Robot Dynamics ◽  
Gravity Compensation ◽  
Pd Controller ◽  
Globally Asymptotically Stable

Abstract A Nonlinear Proportional-Derivative (NPD) controller with gravity compensation is proposed and applied to robot manipulators in this paper. The proportional and derivative gains are changed by the nonlinear function of errors in the NPD controller. The closed-loop system, composed of nonlinear robot dynamics and NPD controllers, is globally asymptotically stable in position control of robot manipulators. The comparison of the simulation experiments in the position control (the step response) of a robot manipulator with two degrees of freedom is also presented to illustrate that the NPD controller is superior to the conventional PD controller in a position control system. The experimental results show that the NPD controller can obtain a faster response velocity and higher position accuracy than the conventional PD controller in the position control of robot manipulators because the proportional and derivative gains of the NPD controller can be changed by the nonlinear function of errors. The NPD controller provides a novel approach for robot control systems.

scholarly journals Finite-Time Mittag–Leffler Synchronization of Neutral-Type Fractional-Order Neural Networks with Leakage Delay and Time-Varying Delays

Mathematics ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. 1146
Author(s):  
Călin-Adrian Popa ◽  
Eva Kaslik
Keyword(s):  
Neural Networks ◽  
Feedback Control ◽  
Fractional Order ◽  
Finite Time ◽  
State Feedback ◽  
Neutral Type ◽  
Leakage Delay ◽  
State Feedback Control ◽  
Time Varying ◽  
Control Scheme

This paper studies fractional-order neural networks with neutral-type delay, leakage delay, and time-varying delays. A sufficient condition which ensures the finite-time synchronization of these networks based on a state feedback control scheme is deduced using the generalized Gronwall–Bellman inequality. Then, a different state feedback control scheme is employed to realize the finite-time Mittag–Leffler synchronization of these networks by using the fractional-order extension of the Lyapunov direct method for Mittag–Leffler stability. Two numerical examples illustrate the feasibility and the effectiveness of the deduced sufficient criteria.

A Digital Algorithm for Near-Minimum-Time Control of Robot Manipulators

1987 ◽  
Vol 109 (4) ◽  
pp. 320-327 ◽  
Author(s):  
C. K. Kao ◽  
A. Sinha ◽  
A. K. Mahalanabis
Keyword(s):  
Control Algorithm ◽  
State Feedback ◽  
Closed Loop ◽  
Robot Manipulator ◽  
Minimum Time ◽  
State Feedback Control ◽  
Final States ◽  
Closed Loop System ◽  
Time Control ◽  
Digital Algorithm

A digital state feedback control algorithm has been developed to obtain the near-minimum-time trajectory for the end-effector of a robot manipulator. In this algorithm, the poles of the linearized closed loop system are judiciously placed in the Z-plane to permit near-minimum-time response without violating the constraints on the actuator torques. The validity of this algorithm has been established using numerical simulations. A three-link manipulator is chosen for this purpose and the results are discussed for three different combinations of initial and final states.

scholarly journals State-feedback control of robot manipulators using polytopic LPV modelling with fuzzy-clustering

2018 ◽  
Vol 9 (4) ◽  
pp. 2841-2848
Author(s):  
Mohammad Hosein Kazemi ◽  
Mohammad Bagher Abolhasani Jabali
Keyword(s):  
Feedback Control ◽  
Fuzzy Clustering ◽  
State Feedback ◽  
Robot Manipulators ◽  
State Feedback Control

Synchronization of time-varying time delayed neutral-type neural networks for finite-time in complex field

2021 ◽  
Vol 8 (3) ◽  
pp. 486-498
Author(s):  
N. Jayanthi ◽  
◽  
R. Santhakumari ◽  
Keyword(s):  
Neural Networks ◽  
Finite Time ◽  
State Feedback ◽  
Neutral Type ◽  
State Feedback Control ◽  
Projective Synchronization ◽  
Time Varying ◽  
Master System ◽  
Complex Valued ◽  
Theoretical Results

This paper deals with the problem of finite-time projective synchronization for a class of neutral-type complex-valued neural networks (CVNNs) with time-varying delays. A simple state feedback control protocol is developed such that slave CVNNs can be projective synchronized with the master system in finite time. By employing inequalities technique and designing new Lyapunov--Krasovskii functionals, various novel and easily verifiable conditions are obtained to ensure the finite-time projective synchronization. It is found that the settling time can be explicitly calculated for the neutral-type CVNNs. Finally, two numerical simulation results are demonstrated to validate the theoretical results of this paper.

MAINTAINING SYNCHRONIZATION BY DECENTRALIZED FEEDBACK CONTROL IN TIME DELAY NEURAL NETWORKS WITH PARAMETER UNCERTAINTIES

2007 ◽  
Vol 17 (02) ◽  
pp. 115-122 ◽  
Author(s):  
MOU CHEN ◽  
CHANG-SHENG JIANG ◽  
QING-XIAN WU ◽  
WEN-HUA CHEN
Keyword(s):  
Neural Networks ◽  
Feedback Control ◽  
External Disturbance ◽  
Synchronization Error ◽  
Linear Matrix ◽  
Parameter Uncertainties ◽  
Time Varying Delay ◽  
Control Scheme ◽  
Error Dynamics ◽  
Varying Delay

A decentralized feedback control scheme is proposed to synchronize linearly coupled identical neural networks with time-varying delay and parameter uncertainties. Sufficient condition for synchronization is developed by carefully investigating the uncertain nonlinear synchronization error dynamics in this article. A procedure for designing a decentralized synchronization controller is proposed using linear matrix inequality (LMI) technique. The designed controller can drive the synchronization error to zero and overcome disruption caused by system uncertainty and external disturbance.

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

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Rong Mei ◽  
ChengJiang Yu
Keyword(s):  
Feedback Control ◽  
Output Feedback ◽  
Disturbance Observer ◽  
Robot Manipulators ◽  
External Disturbance ◽  
Input Saturation ◽  
Output Feedback Control ◽  
System State ◽  
Control Scheme ◽  
Uncertain Robot

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.

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