Obtaining all feasible parameters of the proportional-integral-differential (PID) controller is the key goal in uncertain systems. This paper proposes a graphical tuning method based on an internal model control (IMC) strategy for uncertain systems with time delay. Specifically, the Kharitonov theorem is introduced first to simplify the uncertain system into 32 polynomials. Then, for each polynomial, the IMC structure is applied to reduce the tuning parameters of the PID controller in order to rapidly determine the controller parameters. Finally, the maximum sensitivity (Ms) is used to further guarantee the controlled system with a certain robustness and dynamic performance, which can portray constant gain margin and phase margin boundaries, and can even determine the range of parameters of the proposed IMC filter. Three example results from simulations are presented to demonstrate the effectiveness and applicability of the proposed method.

A radial basis function(RBF) neural network model has been proposed for predicting production yields and reducing simulation times of a modified special pseudo-components(SPCs) fluid catalytic cracking riser. Subsequently, a modified special pseudo-components riser, served as the source of reliable data for artificial neural network(ANN) model training and testing. The ANN model experimental results show a substantial reduction of computation time and good match compared to special pseudo-components fluid catalytic cracking riser mechanism model. Its potential use for modeling optimize and control of the riser is enormous.