ABSTRACT
The system identification and parameter estimation process (SIPEP), as one of the important approaches to mathematical modeling, provides a powerful tool for systematic analysis and optimization of industrial processes, thereby reducing losses and saving the cost of production. The SIPEP is a highly matured technology and can be looked upon as a data-dependent model-building process. For aerospace applications, it can be advantageously utilized to aid the iterative control law design/flight simulation (FS) cycles as well as in certification of atmospheric vehicles. System identification (SID) refers to determination of adequate mathematical model structure based on the physics of the problem and analysis of available data using an optimization criterion so as 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. Parameter estimation is also utilized in the SID procedure, and is regarded as a special case of SI procedure and also of Kalman filtering methods. Parameter estimation refers to explicit determination of numerical values of the unknown parameters of the postulated state-space mathematical model, or any type of the model. Basic principles are same as in SID, but in many cases, the model structure selection procedure may not be needed due to well-defined structure available and used from the physics of the system, for example, aircraft parameter estimation.
