Study Of Process Testing Method And Modeling Technology

The intent of identifying systems which is the prerequisite of getting efficient control method is to establish the accurate process model. In practice the continuous-time systems often have unsteady initial states, so it is very hard to estimate the parameters via traditional methods. In this paper a whole set of solutions which is including multiple integral identification based on step testing, two-stage least-squares algorithms based on pulse testing and state estimate identification has been proposed. Each of the solutions is studied deeply. The main contributions of the paper are as follows:1. The new developments in the identification field in the last few years have been studied and classified. The advantages and disadvantages of each of the identification methods are assessed and compared. Though investigating the needs of the system identification in the industrial field, the difficulties in identifying the continuous-time model in the presence of unsteady initial states, which are the main research subject of this paper, are demonstrated.2. Computer simulation technology is the key to identify the continuous-time systems using the data gathered in the industrial field. The principle of the computer simulation is demonstrated, and the advantages and disadvantages of each of the simulation methods are compared. The arithmetic procedures, calculation accuracy and the application have been discussed as a main point. In addition, two engineering methods which can be used to transform the models conveniently have been proposed.3. Over the last few years new methods have been proposed to simultaneously estimate model parameters and the delay from step responses that require the process to be at steady state when the step is applied. To deal with transient initial conditions, a new method to estimate model parameters and the delay from a single step response in the presence of transient initial conditions is proposed, which is understandable and does not require iteration.4,The so-called two-stage least-squares algorithms is presented to deal with practical identification difficulties often encountered in field testing, such as unsteady initial states, unknown load disturbances, and noise-corrupted measurement. For step and ramp responses, a general linear regression equation is derived from multiple integration of the differential system equation. Four types of pulse inputs are then considered, each of which has its specific advantage in applications and can be represented as a combination of step or ramp inputs. Based on any of these pulse responses, the algorithms are able to overcome those difficulties and yield accurate parameter estimates.5,A novel identification method based on state estimation is proposed in order to overcome the difficulty in identifying the continuous-time systems with non-zero unsteady initial conditions. The initial conditions are described by the state values of the state-space expression, and then the initial state values can be considered as a part of the parameters which can be estimated with the advanced particle swarm optimization. This new method requires nothing to its input and no process data before the test starts. Its effectiveness is demonstrated through simulations.