Generalized predictive control—Part I. The basic algorithm
Automatica (Journal of IFAC)
Robotics: control, sensing, vision, and intelligence
Robotics: control, sensing, vision, and intelligence
Distributed Representations, Simple Recurrent Networks, And Grammatical Structure
Machine Learning - Connectionist approaches to language learning
Artificial neural networks in process estimation and control
Automatica (Journal of IFAC)
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Neuro-Control and Its Applications
Neuro-Control and Its Applications
Neural Generalized Predictive Control: A Newton-Raphson Implementation
Neural Generalized Predictive Control: A Newton-Raphson Implementation
Modelling, trajectory calculation and servoing of a computer controlled arm
Modelling, trajectory calculation and servoing of a computer controlled arm
Dynamic recurrent neural networks: a dynamical analysis
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Brief paper: Self-tuning regulators for a class of multivariable systems
Automatica (Journal of IFAC)
Application of the recurrent multilayer perceptron in modeling complex process dynamics
IEEE Transactions on Neural Networks
High-order neural network structures for identification of dynamical systems
IEEE Transactions on Neural Networks
Information Sciences: an International Journal
Locally recurrent neural networks for wind speed prediction using spatial correlation
Information Sciences: an International Journal
Empirical analysis of an on-line adaptive system using a mixture of Bayesian networks
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
Information Sciences: an International Journal
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The aim of this paper was to propose a recurrent neural network-based predictive controller for robotic manipulators. A neural network controller for a six-joint Stanford robotic manipulator was designed using the generalized predictive control (GPC) and the Elman network. The GPC algorithm, which is a class of digital control method, requires long computational time. This is a disadvantage in real-time robot control; therefore, the Elman network controller was designed to reduce processing time by avoiding the highly mathematical and computational complexity of the GPC. The main reason for choosing the Elman network, amongst several neural network algorithms, was that the presence of feedback loops have a profound impact on the learning capability of the network. The designed neural network controller was able to recover quickly because of its significant generalization capability, which allowed it to adapt very rapidly to changes in inputs. The performance of the controller was also shown graphically using simulation software, including the dynamics and kinematics of the robot model.