This paper presents the design and implementation of an adaptive fuzzy logic controller for the speed control of brushless dc motors. The proposed system uses an adaptation of the slope of the membership functions of the variables used in the conventional fuzzy controller based on the magnitude of the error. A simulation analysis of the fuzzy controller and the adaptive fuzzy controller are don...

The performance of a massively parallel computing system is often limited by the speed of its interconnection network. One strategy that has been proposed for improving network e ciency is the use of adaptive routing, in which network state information can be used in determining message paths. The design of an adaptive routing system involves several parameters, and in order to build high speed...

In adaptive control the goal is to design a controller to control an uncertain system whose parameters may be changing with time. Typically the controller consists of an identi-er (or tuner) which is used to adjust the parameters of a linear time-invariant (LTI) compensator, and under suitable assumptions on the plant model uncertainty it is proven that good asymptotic behaviour is achieved, su...

The design of two multiple-input multiple-output (MIMO) controllers for induction motors, based on adaptive passivity, is presented in this paper. The controller design method is based on concepts of equivalence passivity via adaptive feedback, previously developed by the authors. Robustness under variations of the motor-load parameters is guaranteed and the knowledge of such a parameters is no...

We present an adaptive observer design for a first-order hyperbolic system of Partial Differential Equations with uncertain boundary parameters. The design relies on boundary measurements only, and is based on a backstepping approach. Using a Gradient Descent technique, we prove exponential convergence of the distributed system and estimation of the parameter. This method is applied to the esti...

This work proposes a sequential procedure which is useful to select the best model among several nested non-linear models and to estimate efficiently the parameters of the chosen model. At the first step of this procedure, a generalized DKL-optimum design is computed, which is optimal for the double goal of model selection and parameter estimation. Subsequently, at each following step, an adapt...

This paper presents the design of an Iterative Learning Observer (ILO) for the purpose of estimating system states while simultaneously identifying timevarying parameters. The proposed ILO uses a novel updating mechanism to identify time-varying parameters instead of using integrators which are commonly used in classical adaptive observers to identify constant parameters while estimating system...

Controller adaptation is always a major concern. A controller that meets certain performance design objectives can not be satisfactory unless it can preserve such quality in the presence of system parameters fluctuation. For the nuclear power reactors (NPR) a controller that preserves stability and performance for wide range of operation conditions and fluctuations is especially desirable. This...

We compare several adaptive design strategies using a data set of 223 M2AX family of compounds for which the elastic properties [bulk (B), shear (G), and Young's (E) modulus] have been computed using density functional theory. The design strategies are decomposed into an iterative loop with two main steps: machine learning is used to train a regressor that predicts elastic properties in terms o...