in this paper, genetic algorithms (gas) are employed to control simultaneous linear systems in both state and output feedback. first, the similarity transformation is applied to obtain parameterized controllers. this requires solution of a system of equations and also some non-linear inequalities. gas are used to solve these equations and inequalities. therefore, the paper presents an analytica...

This paper deals with the short-pulse radiation of three-dimensional (3-D) vector electromagnetic fields from arbitrarily polarized large two-dimensional (2-D) truncated aperture distributions, which are parameterized in terms of narrow-waisted ray-like pulsed Gaussian basis beams centered on a discretized Gabor lattice in a four-dimensional (4-D) configuration-spectrum phase space. The study e...

We consider linear parameterized systems $A(\mu) x(\mu) = b$ for many different $\mu$, where $A$ is large and sparse depends nonlinearly on $\mu$. Solving such individually each $\mu$ would require great computational effort. In this work we propose to compute a partial parameterization $\tilde{x} \approx x(\mu)$, $\tilde{x}(\mu)$ cheap evaluate Our methods are based the observation that compan...

This paper presents a practical direct adaptive fuzzy H tracking control (AFHC) approach for a class of uncertain nonlinear systems with unknown control gain functions and external disturbances. A modified output tracking error is defined to eliminate high gain at the control input and to improve transient performance. An ideal control law is developed to eliminate the restriction that the cont...

An observer-based adaptive iterative learning control (AILC) scheme is developed for a class of nonlinear systems with unknown time-varying parameters and unknown time-varying delays. The linear matrix inequality (LMI) method is employed to design the nonlinear observer. The designed controller contains a proportional-integral-derivative (PID) feedback term in time domain. The learning law of u...

The output regulation problem is a central problem in control theory. The early work on output regulation problem in the state space framework can be traced to the early 1970s, and the problem of nonlinear systems has been an active topic since 1980s. So far there exist various results of this problem under various assumptions. In the case where the high-frequency gain sign is unknown, the glob...

This paper deals with studying the asymptotical properties of multilayer neural networks models used for the adaptive identification of wide class of nonlinearly parameterized systems in stochastic environment. To adjust the neural network’s weights, the standard online gradient type learning algorithms are employed. The learning set is assumed to be infinite but bounded. The Lyapunov-like tool...