Optimization Method of Controller Parameter Tuning Based Immune Principle for Complicated System

In order to solve the puzzle of parameter tuning of the controller in complicated system, the paper proposed a model of parameter tuning based on immune principle. Firstly it defined the antibody, antigen and affinity in tuning parameter needed, secondly explored the process of parameter tuning based on immune principle in detail, thirdly discussed the tuning method by means of optimizing parameter of the controller. And finally it took the control of high-order process as an example, and made the simulation to a highly non-minimum phase process. The simulation result shows that the proposed method is better in control quality and more effective compared with other tuning methods.

Closed-Loop Identification Using Laguerre Series Expansions for Second-Order Plus Time Delay Model

In this paper, a simple yet robust closed-loop identification method based on step response is presented. By approximating the process response firstly using Laguerre series expansions, a high-order process transfer function can be obtained. Then, a linear two-step reduction technique is used to reduce the high-order process to a second-order plus time delay model based on the frequency response data. This method is robust to measurement noise and it also does not need any numerical technique or iterative optimization. Simulation examples show the effectiveness of the proposed method for different process models. Comparison of identification performance between different methods is also illustrated in this work.