Learning in connectionist networks using the Alopex algorithm
Learning in connectionist networks using the Alopex algorithm
Identification of Systems Using Radial Basis Networks Feedbacked with FIR Filters
Proceedings of the International Conference on Computational Intelligence, Theory and Applications
Learning rules for neuro-controller via simultaneous perturbation
IEEE Transactions on Neural Networks
A direct adaptive neural-network control for unknown nonlinear systems and its application
IEEE Transactions on Neural Networks
Diagonal recurrent neural networks for dynamic systems control
IEEE Transactions on Neural Networks
Using a New Model of Recurrent Neural Network for Control
Neural Processing Letters
Control of a Robotic Wheelchair Using Recurrent Networks
Autonomous Robots
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This paper studies the problem of controlling the movements of a handicapped person‘s motorized wheelchair from a practical point of view. The control system implemented has been divided into two levels: the “low level”, consisting of an electronic system which directly controls the drivers of the chair‘s motors, with a classic PID (proportional-integral-derivative) control loop. The aim of this level is to ensure that the speeds of each one of the wheels is similar to the input speed of these control boards. The second control level (“high level”), implemented by means of neural techniques, ensures that the linear and angular speeds of the wheelchair are those indicated by a trajectory generator. A new recurrent model is used as the neural network, for which the stability conditions of the complete control system are obtained and various practical tests are carried out, which show the correct performance of the actual system implemented.