In this paper, we propose a model reference adaptive control (MRAC) strategy for continuous-time singleinput single-output (SISO) linear time-invariant (LTI) systems with unknown parameters, performing repetitive tasks. This is achieved through the introduction of a discrete-type parametric adaptation law in the ‘iteration domain’, which is directly obtained from the continuous-time parametric ...

The machining quality on computerized numerically controlled (CNC) machine tools is sensitive to the machining parameters. With modern machine tools, an operator still manually adjusts controlling parameters, such as feed rate and cutting speed. The adjusted values mainly depend on operator experience and knowledge. Standard machining catalogues and commercial cutting condition prediction softw...

This paper presents a comparison of two different adaptive control schemes for improving the performance of a hybrid wind-diesel power system in an islanded microgrid configuration against the baseline controller, IEEE type 1 automatic voltage regulator (AVR). The first scheme uses a model reference adaptive controller (MRAC) with a proportional-integral-derivative (PID) controller tuned by a g...

This thesis is intended to be a succinct and rather informal presentation of some of my most recent work, which has been done in collaboration with several other people. In particular this thesis concentrates on our contributions to the study of incomplete information in the context of data interoperability. In this scenario data is heterogenous and decentralized, needs to be integrated from se...

This paper considers the control problem of switched linear systems with unknown parameters and unknown switching signals. For the considered system, robust model reference adaptive control (MRAC) approach can only guarantee error boundedness under an average dwell time (ADT) condition which is implicit and is difficult to estimate. We propose a variable structure (VS) based adaptive control wi...

Abstrac t— This paper propose an Model Reference Adaptive Control (MRAC) for mobile robots for which stability conditions and performance evaluation are given. The proposed control structure combines a feedback linearization model, based on a kinematics nominal model, and a direct neural network-based adaptive dynamics control. The architecture of the dynamic control is based on radial basis fu...

In this article, a robust nonlinear model reference adaptive control (MRAC) is proposed for disturbed linear systems, i.e., systems with parameter uncertainties, and external time-dependent perturbations or unmodeled dynamics matched the input. The law composed of two gains. Such gains allow to counteract effects some ensuring asymptotic convergence tracking error zero, boundedness controller s...

Model Reference Adaptive Control (MRAC) is consists of a reference model, a plant model, and adaptive law. It is made to follow the target system to the reference model by fixed gain. As study example, Yang developed an adaptive control for SISO state space system[1]. Yang’s method[1] was not discussed about the performance of the reference model. MRAC depends on performance of the reference mo...