Using Neural Networks for Identification and Control of Systems

Abstract The present work addresses the utilization of Artificial Neural Networks (NN) for the identification and control of systems, in special to control nonlinear dynamic systems or systems with some degree of uncertainty. Because NNs have an inherent ability to approximate functions and to adapt to changes in input and parameters, they can be used to control systems too complex for linear controllers, such as PID controllers. In the present work a mathematical basis for NN is presented, the mathematical representation of a process unit, or neuron, and how they can be put together in order to form nets that can learn from external data. In sequence, it is presented structures of inputs that can be used along with NN to model nonlinear systems. The most common configurations of input vectors for the training of NN are highlighted. Following, a method of control is presented that take advantage of NN, where a NN is used to build a predictive nonlinear controller using a model predictive control (MPC) structure. Two nonlinear systems were used to test the identification and control of the structures proposed. The results shows the NN used were efficient in modeling and controlling the nonlinear plants