A neural network-based robust adaptive control design scheme is developed for a class of nonlinear systems represented by input–output models with an unknown nonlinear function and unmodeled dynamics. By on-line approximating the unknown nonlinear functions and unmodeled dynamics by radial...

Using the Lyapunov stability theory an adaptive control is proposed for chaos synchronization between two Chua systems which have stochastically time varying unknown coefficients. The stochastic variations of the coefficients around their unknown mean values are modeled through Gaussian white...

The objective of this paper is to present a survey on extremum seeking control methods and their applications to process and reaction systems. Two important classes of extremum seeking control approaches are considered: perturbation-based and model-based methods.

This paper deals with the problem of controlling energy generation systems including fuel cells (FCs) and interleaved boost power converters. The proposed nonlinear adaptive controller is designed using sliding mode control (SMC) technique based on the system nonlinear model. The latter accounts...

At this article are examined conceptual attitudes to the forming modeling basis of evaluation, analysis and forecasting state financial security (SFS). Realization of the conceptual attitudes in the system SFS gives the opportunity to compose leading progress indicators, which allows analyzing...

This paper illustrates the use of dual/adaptive control methods to compare passive and active adaptive management decisions in the context of an ecosystem with a threshold effect. Using discrete-time dynamic programming techniques, we model optimal phosphorus loadings under both uncertainty...

The paradigm of adaptive natural resource management (AM), in which experiments are used to learn about uncertain aspects of natural systems, is gaining prominence as the preferred technique for administration of large-scale environmental projects. To date, however, tools consistent with...

Comparisons of various methods for solving stochastic control economic models can be done with Monte Carlo methods. These methods have been applied to simple one-state, one-control quadraticlinear tracking models; however, large outliers may occur in a substantial number of the Monte Carlo runs...