Study And Simulation On Single-Variable System Identification

Application of advanced control theory is based on object's accurate mathematical model and the system character plays a critical role in optimal control. In this paper, the principle of system identification methods, signal selection and so on are focused and make identification calculations based on collected data information get by simulation. Based on practical industrial application, this paper make following contributions:1,Introduce the development of system identification as well as modern methods and describe the model types, modeling methods and error criteria. Analysis classical identification algorithms:least squares method, graphical method, direct identification method for continuous by simulation and obtain the advantages and disadvantages of each method.2,Research NLJ, particle swarm optimization (PSO) and genetic algorithm (GA) in system identification applications. In real applications, GA which is easy to fall into local optimum has low convergence speed, less precise. In this paper, GA is improved by adding high limit which ensure that the algorithm can jump out of local optimal search parameters to get consistent and unbiased estimates. Based on the concept of satisfaction, optimize the controller parameters by using the improved genetic algorithm meet the requirements of the control system.3,For the practical application process, control loop is not allowed converted into the open-loop form, but classical identification algorithms can not be applied to the closed-loop identification directly. a novel identification method—PSO-Rosenbrock is proposed by integrating global identification ability of Particle Swarm Optimization (PSO) and local search competence of Rosenbrock. The algorithm does not require prior knowledge of controllers on closed loop conditions and can obtain the all of the parameters to be estimated based on arbitrary test signal. This algorithm can not only improve the convergence rate but also reduce the dependence of identification parameters on initial parameters.4,Describe commonly used polynomial prediction filter, median filter and three alternative filtering algorithms function which all have strong filtering. But if the waveform is too smooth or anamorphic it will reduce useful information provided by data and identification accuracy. Take average valued of median filter can effectively remove the scrap value of the pulse and provide more information. Compared with the simple median filter, this method can improve identification results.