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In this paper, first, an adaptive neural network (NN) state-feedback controller for a class of nonlinear systems with mismatched uncertainties is proposed. By using a radial basis function NN (RBFNN), a bound of unknown nonlinear functions is approximated so that no information about the upper bound of mismatched uncertainties is required. Then, an observer-based adaptive controller based on RBFNN...
This paper deals with the robust stochastic stability problem for a class of continuous-time semi-Markov jump linear systems. The system under consideration involves uncertainties both in the transition probabilities and in the system matrices. This new uncertain model generally removes limitations of the certain and time-constant transition rates of jump processes, thus it is appropriate for many...
This work deals with the robust stochastic stabilization problem for continuous-time Markov jump linear systems with time varying transition rates, i.e. for a non-homogeneous Markov jump linear system. Norm bounded time varying uncertainties are affecting the system states. Transition rates are assumed to be piecewise constant and in fact the Markov jump linear system is piecewise homogeneous. Based...
This paper presents an uncertain linear model for an Inertial Stabilization Platform (ISP). The system has a 2 degree of freedom (DOF) gimbal which will be attached to a moving vehicle and the optical sensors mounted on stabilized gimbal. The main purpose of an ISP is to eliminate the various disturbance and body motion to make the line of sight (LOS), hold steady in an inertial space. Due to dynamical...
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