In this paper, a novel controller is proposed for Networked Control Systems (NCSs) with random delays and non-linearity using BP neural networks and Fisher information. The controller is designed by minimising the Fisher information of tracking error. The Fisher information is calculated based on estimated Probability Density Function (PDF) via the Parzen windowing technique. The convergent condition of the proposed control algorithm is given. A simulation example is given to show the effectiveness of the proposed algorithm. Neuro-PID controller design for Networked Control Systems 447in 1987. Since 1987, he has been researching in control systems engineering, fault diagnosis, non-linear systems control and stochastic distribution systems design.

Based on the recently developed rule based identification algorithm of Wang and Jones (1991), this paper presents a new algorithm for controller tuning in unit feedback control systems. In the paper, the rule based identification algorithm is used directly to identify the model between the reference step input and the integral of the tracking error. Two stages of tuning are proposed. In the first stage, the correct structure of the controller is chosen by monitoring the integral of the tracking error. This enables the realisation of perfect steady-state tracking. In the second stage, the estimated model is used to tune the controller gains in order to provide an improved dynamic performance for the closed-loop response. The results of applying the algorithm to the stimulation of a real process are given.