We consider the problem of asymptotic reconstruction of the state and parameter values for dynamical systems that cannot be transformed into the canonical adaptive observer form. A solution to this problem is proposed for a class of systems for which the unknowns are allowed to be nonlinearly parameterized functions of state and time. Going beyond asymptotic Lyapunov stability, we provide for t...

We consider the problem of asymptotic reconstruction of the state and parameter values for dynamical systems that cannot be transformed into the canonical adaptive observer form. A solution to this problem is proposed for a class of systems for which the unknowns are allowed to be nonlinearly parameterized functions of state and time. Going beyond asymptotic Lyapunov stability, we provide for t...

We propose a technique for the design and analysis of decentralized adaptation algorithms in interconnected dynamical systems. Our technique does not require Lyapunov stability of the target dynamics and allows nonlinearly parameterized uncertainties. We show that for the considered class of systems, conditions for reaching the control goals can be formulated in terms of the nonlinear L2-gains ...

Using geometric concepts from observability theory for nonlinear systems, we propose an approach for parameter estimation for linearly and nonlinearly parameterized systems. The proposed approach relies on extending a parameter estimation problem to a state estimation problem by introducing the parameters as auxiliary state variables. Applying tools from geometric nonlinear control theory we es...

In this paper, we address the robust filtering problem for a wide class of systems whose state-space data assume a very general nonlinear dependence in the uncertain parameters. Our resolution methods rely on new linear matrix inequality characterizations of 2 and performances, which, in conjunction with suitable linearization transformations of the variables, give rise to practical and computa...

In this paper, an adaptive fuzzy tracking control approach is proposed for a class of single-inputsingle-output (SISO) nonlinear systems in which the unknown continuous functions may be nonlinearlyparameterized. During the controller design procedure, the fuzzy logic systems (FLS) in Mamdani type are applied to approximate the unknown continuous functions, and then, based on the minimal learnin...

We consider the problem of state and parameter reconstruction for uncertain dynamical systems that cannot be transformed into the canonical adaptive observer form. The uncertainties are allowed to be both linearly and nonlinearly parameterized functions of state and time. We provide a technique that allows successful reconstruction of uncertain state and parameters for a broad range of dynamica...