System Identification and Parameter Estimation

The system identification and parameter-estimation process (SIPEP), one of the important approaches to mathematical modeling, provides a powerful tool for systematic analysis and eventual optimization of industrial processes, thereby reducing losses and saving the cost of production. The SIPEP is a fairly mature technology and can be considered as a data-dependent model building process. In aerospace applications it can be used to great advantage to aid the iterative control-law design=flight simulation cycles as well as in certification of atmospheric vehicles. System identification (SID) refers to the determination of an adequate mathematical model structure based on the physics of the problem and analysis of available data using some optimization criterion to minimize the sum of the squares of errors between the responses of the postulated mathematical model and the real system. The computational procedure is generally iterative and requires engineering judgment and use of objective model selection criteria [1]. Parameter estimation is also employed in the SID procedure. Parameter estimation is regarded as a special case of SID procedure and also of Kalman filtering methods. Parameter estimation refers to explicit determination of numerical values of unknown parameters of the postulated state-space mathematical model, or any type of the model. The basic principles are the same as in SID but in many cases the model structure selection procedure may not be needed due to the availability and use of well-defined structure from the physics of the system (Chapters 3 through 5), e.g., aircraft parameter estimation. The SIPEP for a flight vehicle is illustrated in Figure 9.1.