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Abstract. It is difficult to satisfy most of the performance targets by using the PID control law only, if the plants are the processes with uncertain time-delay, varying parameters and non-linearity. For this reason a genetic algorithm based neuro-fuzzy network adaptive PID controller is proposed in this paper. The neuro-fuzzy network is used to amend the parameters of the PID controller online, the global optimal parameters of the network are found with a high speed, and the improved genetic algorithm is introduced to overcome the local optimum defect of the BP algorithm. Finally, the simulation experiment of the control method on the tobacco-drying control process is performed. The simulation results demonstrate that this kind of control method is effective.

operation mode of sintering process is developed and combined with knowledge-based model in order to realize the operation mode recognition of sintering process. Then, the burning through point control subsystem and the subsystem for abnormity diagnosing are presented using artificial neural network and expert system. A real-time intelligent guiding system for controlling sintering process is integrated based on these researches.