On nonparametric identification and dual control of quasilinear dynamic processes

The problems of identification and control of stochastic objects with a discrete - continuous technological process nature under conditions of nonparametric indeterminacy are considered. This term means a situation when the process structure with the accuracy to within parameters remains unknown. A more general class of dynamical nonlinear proc- esses, later of quasilinear processes, is investigated. The processes in this category are characterized by a low degree of nonlinearity, that is the superposition principle for this type of object is insolvent. Such processes often occur in vari- ous control loops for aerospace objects and systems. Nonparametric models where the dynamic process memory depth is specified on the basis of the selection of essential variables rule are given. According to this rule, the only variables included in the nonparametric model, at which the optimum blurring factor of the kernel is minimal. Nonparametric algorithms of quasilinear objects dual control are given. Control devices built on the basis of these algorithms not only perform the object control function directly, but also its study. The case when the control device corresponding to its inverse model “turns on” at the object input is considered. The process of dual control system training with active information accumulation is analyzed. The results of a numerical study of nonparametric models for quasilinear proc- esses with memory are presented in detail, as well as the results of a computational experiment using the algorithm of nonparametric adaptive dual control. In the simulation, objects characteristics were described by equations with different degrees of nonlinearity, the form of which was unknown, and which, during active information accumulation, were automatically restituted on the basis of the input-output variables process measurement. Also, the influence of various noise levels affecting an object and in measurement channels was investigated. The given computational ex- periments confirmed the possibility of using nonparametric algorithms for identifying and controlling of quasilinear systems.