estimation and control for a class of

We propose a new method for the design of adaptation algorithms that guarantees a certain prescribed level of performance and applicable to systems with nonconvex parameterization. The main idea behind the method is two-fold. First, we augment the tuning error function and design the adaptation scheme in the form of ordinary differential equations. The resulting augmentation is allowed to depend on state derivatives. Second, we find a suitable realization of the designed adaptation scheme in an algebraic-integral form. Due to their explicit dependence on the state of the original system, such adaptation schemes are referred to as adaptive algorithms in finite form, in contrast to (conventional) algorithms in differential form. Sufficient conditions for the existence of finite form realizations are proposed. It is shown that our method to design algorithms in finite form is applicable to a broad class of nonlinear systems including systems with nonconvex parameterization and low-triangular systems.

Superposition of sigmoid function over a finite time interval is shown to be equivalent to the linear combination of the solutions of a linearly parameterized system of logistic differential equations. Due to the linearity with respect to the parameters of the system, it is possible to design an effective procedure for parameter adjustment. Stability properties of this procedure are analyzed. Strategies shown in earlier studies to facilitate learning such as randomization of a learning sequence and adding specially designed disturbances during the learning phase are requirements for guaranteeing convergence in the learning scheme proposed.

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 this class of systems a reconstruction technique, based on the concepts of weakly attracting sets, non-uniform convergence, and Poisson stability. Key words: Adaptive observers, nonlinear parametrization, weakly attracting sets, unstable attractors, nonlinear systems, Poisson stability 1