Adaptive distributed parameter systems identification with enforceable identifiability conditions and reduced-order spatial differentiation

Presents the synthesis of adaptive identifiers for distributed parameter systems (DPS) with spatially varying parameters described by partial differential equations (PDEs) of parabolic, elliptic, and hyperbolic type. The features of the PDE setting are utilized to obtain the not directly intuitive parameter estimation algorithms that use spatial derivatives of the output data with the order reduced from that of the highest spatial plant derivative. The tunable identifier parameters are passed through the integrator block, which forms their orthogonal expansions. The latter are shown to be pointwise plant parameter estimates. In this regard, the approach of the paper is in the spirit of finite-dimensional observer realization in integrating rather than differentiating the output data, only applied to the spatial rather than temporal domain. The constructively enforceable identifiability conditions, formulated in terms of the sufficiently rich input signals referred to as generators of persistent excitation, are shown to guarantee the existence of a unique zero steady state for the parameter errors. Under such inputs, the tunable parameters in the adaptive identifiers proposed are shown to converge to plant parameters in L₂ and the orthogonal expansions of these tunable parameters-pointwise