Identification and control of a wheelchair using recurrent neural networks
This work involves the control of a wheelchair using a new model of radial base function (RBF) recurrent neural networks. The proposed architecture is made up of two blocks, each with one neural network: one to identify the physical system (plant)the identifier, and another for controlthe controller. The identifier, running in parallel with the plant, is designed to obtain the systems Jacobian, which is used to adjust the weights of the controller. The stability conditions are obtained for the correct functioning of the system, and several tests are described in which the movements of a wheelchair are governed, thus confirming the correct functioning of the control architecture used.
identification wheelchairs neural networks intelligent control radial basis function networks lyapunov stability