Adaptive manipulator trajectory control using neural networks
01 Jul 1994-International Journal of Systems Science (Taylor & Francis Group)-Vol. 25, Iss: 8, pp 1249-1265
TL;DR: Simulation results clearly indicate that the neural network based adaptive controller achieves better tracking in the presence of parametric uncertainties as well as unmodelled effects compared to the simple direct adaptive scheme.
Abstract: A unified study of adaptive control and neural network based control schemes for the trajectory tracking problem of robot manipulators is presented. Efficacy of parametrized adaptive algorithms in compensating the structured uncertainties in robot dynamics is verified through extensive simulation. The ability of neural networks to provide a robust adaptive framework in the presence of both structured and unstructured uncertainties is investigated. A case study is carried out in support of a parametrized adaptive scheme using neural networks. Simulation results clearly indicate that the neural network based adaptive controller achieves better tracking in the presence of parametric uncertainties as well as unmodelled effects compared to the simple direct adaptive scheme.
Citations
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TL;DR: A three-layered recurrent neural network is employed to estimate the forward dynamics model of the robot to minimise the difference between the robot actual response and that predicted by the neural network.
20 citations
Cites methods from "Adaptive manipulator trajectory con..."
...A three-layered recurrent neural network is employed to estimate the forward dynamics model of the robot....
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15 Sep 1996
TL;DR: Simulations illustrate that the proposed neural control approach which is applied to some nonlinear processes can gain satisfactory performance results.
Abstract: This paper investigates the trajectory control of a robot using a new type of recurrent neural network. A three-layered recurrent neural network is used to estimate the forward dynamics model of the robot manipulator. The standard backpropagation (BP) algorithm is used as a learning algorithm for this network to minimise the difference between the robot manipulator actual response and that predicted by the neural network. This algorithm is employed to update the connection weights of a recurrent neural network controller with three layers using a stochastic gradient function. The control architecture consists of a neural feed-forward model which is a recurrent network used for identification of the robot dynamics, a conventional PID controller, a robust controller and a neural controller. Simulations illustrate that the proposed neural control approach which is applied to some nonlinear processes can gain satisfactory performance results. The results of the simulations are presented to show the promising performance of the neural controller.
10 citations
TL;DR: A neural network based control scheme for robot tracking applications using Gaussian networks to model both the forward and the inverse dynamics of a robot arm.
6 citations
TL;DR: It is demonstrated that proposed two-stage approach makes possible to simplify control system synthesis for underwater robot and provide its dynamics close to a reference.
5 citations
TL;DR: In this paper, the authors proposed a decentralised compensation scheme for unstructured uncertainties and modelling errors of robotic manipulators, which employs a central decoupler and independent joint neural network compensators.
2 citations
References
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Book•
01 Jan 1991
TL;DR: Covers in a progressive fashion a number of analysis tools and design techniques directly applicable to nonlinear control problems in high performance systems (in aerospace, robotics and automotive areas).
Abstract: Covers in a progressive fashion a number of analysis tools and design techniques directly applicable to nonlinear control problems in high performance systems (in aerospace, robotics and automotive areas).
15,545 citations
TL;DR: It is demonstrated that neural networks can be used effectively for the identification and control of nonlinear dynamical systems and the models introduced are practically feasible.
Abstract: It is demonstrated that neural networks can be used effectively for the identification and control of nonlinear dynamical systems. The emphasis is on models for both identification and control. Static and dynamic backpropagation methods for the adjustment of parameters are discussed. In the models that are introduced, multilayer and recurrent networks are interconnected in novel configurations, and hence there is a real need to study them in a unified fashion. Simulation results reveal that the identification and adaptive control schemes suggested are practically feasible. Basic concepts and definitions are introduced throughout, and theoretical questions that have to be addressed are also described. >
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01 Jan 1974
4,080 citations
Book•
01 Jan 1989
TL;DR: This self-contained introduction to practical robot kinematics and dynamics includes a comprehensive treatment of robot control, providing background material on terminology and linear transformations and examples illustrating all aspects of the theory and problems.
Abstract: From the Publisher:
This self-contained introduction to practical robot kinematics and dynamics includes a comprehensive treatment of robot control. Provides background material on terminology and linear transformations, followed by coverage of kinematics and inverse kinematics, dynamics, manipulator control, robust control, force control, use of feedback in nonlinear systems, and adaptive control. Each topic is supported by examples of specific applications. Derivations and proofs are included in many cases. Includes many worked examples, examples illustrating all aspects of the theory, and problems.
3,736 citations
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01 Feb 1989TL;DR: Stability theory simple adaptive systems adaptive observers the control problem persistent excitation error models robust adaptive controlThe control problem - relaxation of assumptions multivariable adaptive systems applications of adaptive control.
Abstract: Stability theory simple adaptive systems adaptive observers the control problem persistent excitation error models robust adaptive control the control problem - relaxation of assumptions multivariable adaptive systems applications of adaptive control.
2,955 citations