Neural Network Based Self-Learning Control Strategy for Electronic Throttle Valve
Recently, the application of the electronic throttle has been very popular in the automotive industry. However, difficulties in the control of electronic throttle valves exist due to multiple nonlinearities and plant parameter variations. A neural-network-based self-learning control (SLC) strategy that consists of a fuzzy neural network (FNN) controller and a recurrent neural network (RNN) identifier is proposed for electronic throttle valves in this paper. The FNN controller, which combines the semantic transparency of rule-based fuzzy systems with the learning capability of a neural network, is utilized as an SLC scheme and will be robust to plant parameter variations. An RNN identifier is employed to model the plant and provides plant information for the learning of the FNN controller. Both the structure and the learning algorithm of the control system are presented. The proposed controller is verified by computer simulations and experiments.