an adaptive version of growing and pruning rbf neural network has been used to predict the system output and implement linear model-based predictive controller (lmpc) and non-linear model-based predictive controller (nmpc) strategies. a radial-basis neural network with growing and pruning capabilities is introduced to carry out on-line model identification.an unscented kalman filter (ukf) algor...

A self-adaptive scheme is described for automatically tracking the temperature and saturationdependent rotor time constant (Tr ) in indirect vector control. This method is based on a monitoring signal injected into the process. A cross-correlation function between the monitoring signal and the speed error indicates the Tr changes in direction (increase or decrease) and magnitude. The proposed i...

The paper proposed a new fuzzy-neural recurrent multi-model for systems identification and states estimation of complex nonlinear mechanical plants with backlash. The parameters and states of the local recurrent neural network models are used for a local direct and indirect adaptive control systems design. The designed local control laws are coordinated by a fuzzy rule based control system. Sim...

In this paper, stable indirect adaptive control with recurrent neural networks (RNN) is presented for square multivariable non-linear plants with unknown dynamics. The control scheme is made of an adaptive instantaneous neural model, a neural controller based on fully connected “Real-Time Recurrent Learning” (RTRL) networks and an online parameters updating law. Closed-loop performances as well...

We consider a class of MIMO LTI models with uncertain resonant modes and time delays, which are common in control of instabilities arising in jet engines. With uncertain delays preventing the use of model reference adaptive control, we develop an adaptive MIMO pole placement scheme for the system. We use indirect adaptation, estimating a small number of physical parameters from a nonlinearly pa...

During the last two decades, there has been significant progress in the area of adaptive control design of nonlinear systems (Krstic et al., 1995; Sastry & Isidori, 1989; Slotine & Li 1991; Spooner et al., 2002). Most of the developed adaptive control schemes assume that an accurate model of the system is available and the unknown parameters appear linearly with respect to known nonlinear funct...

An indirect adaptive fuzzy power system stabilizer (IDFPSS) is proposed in this paper. It consists of A fuzzy identifier and a feedback linearizing controller. The objective is to damp local and inter-area oscillations that occur following power system disturbance. The effectiveness of the proposed technique is illustrated by applying the IDFPSS to a two-area four-machine system that is typical...

TThe uncertainty estimation and compensation are challenging problems for the robust control of robot manipulators which are complex systems. This paper presents a novel decentralized model-free robust controller for electrically driven robot manipulators. As a novelty, the proposed controller employs a simple Gaussian Radial-Basis-Function Network as an uncertainty estimator. The proposed netw...