Research On Fuzzy-Neuro Model-Free Control Methods

The fuzzy-neuro model-free control is one of the forward positions of intelligent control. The design theory and applications of fuzzy-neuro model-free control systems are discussed in this thesis. Several fuzzy-neuro model-free control methods are proposed and the simulation experiments of controlling industrial plants with uncertainties such as turning processes, pH processes and so on are made. The results show the efficiency of the algorithms proposed in this thesis. The main contents of this thesis are as follows:1. The model-free PID control method with neuron tuning gain and The neuro-fuzzy control method for a constant cutting force metal turning process system are proposed. The former method keeps the cutting force to be constant by using the neuron to change the PID controller gain on-line. The latter method construct the fuzzy neuron controller by combing the fuzzy controller and the neuron controller. The simulation results of using the proposed controllers for a cutting process show that very strong robustness and satisfying performance are reached.2. The neuro-fuzzy PD double-layer control method is presented for a experimental nuclear reactor. In this control system, the neuro-fuzzy controller integrated by neuron controller and fuzzy controller is used in the outside layer control loop, the PD controller is used in the inside control loop. The simulation results show the proposed control method has strong robustness and satisfying performance.3. The fuzzy control method with neuron tuning fuzzification factors is proposed for the ship changeable pitch propeller. The simulation results demonstrate the applicability of the proposed method to achieve desired performance.4. The neuron control method with self-tuning gain is proposed for a pH neutralization process. In this control system, the fuzzy T-S model is used to predict the control signal. The neuron controller gain is calculated according to the parameter estimation and experience formulas. The simulation results show that the proposed control method can control the pH process with nonlinearity.