This paper introduces the method of ‘reference model decomposition’ as a way to improve the robustness of model reference adaptive control systems (MRACs) with respect to unmodelled dynamics with a known structure. Such unmodelled dynamics occur when some of the nominal plant dynamics are purposely neglected in the controller design with the aim of keeping the controller order low. One of the e...

Model predictive control (MPC) based methods are gaining more and more attention in power converters and electrical drives. Nevertheless, high computational burden of MPC is an obstacle for its application, especially when the prediction horizon increases extends. At the same time, increasing the prediction horizon leads to a superior response. In this paper, a long horizon MPC is proposed to c...

This paper investigates a finite-control set model-predictive control (FCS-MPC) algorithm to enhance the performance of synchronous reluctance machine drive. Particular emphasis is placed on definition cost function enabling computationally light implementation while targeting good transient and steady-state performance. In particular, this work proposes inclusion an integral term into ensure z...

Regulation of hemodynamic parameters is a challenging problem, due to interand intra-patient variability, drug interactions, evolving patient condition, and in the case of simultaneous control of mean arterial pressure and cardiac output, tight coupling between the two variables. This requires a controller that can adapt to conditions as they evolve, and without the need to tune the controller ...

the present sought to draw a comparison between model predictive control performance and two other controllers named simple pi and selective pi in controlling large-scale natural gas transport networks. a nonlinear dynamic model of representative gas pipeline was derived from pipeline governing rules and simulated in simulink® environment of matlab®. control schemes were designed to provide a s...