Microbioreactors are a promising technology to accelerate biologic drug development. In aerobic cellular respiration, potential limit the productivity of such systems is transport oxygen from an external gas most oxygen-deficient cells, and for excessive spatially localized dissolved which can result in damage. This article analytically solves mechanistic model spatiotemporal through gas-permea...

This research investigates the Adaptive Model Predictive Controller (AMPC) and Linear Parameter-Varying (LPV) control system for a direct current (dc-dc) buck-boost converter, considering parameters’ uncertainty. The LPV model AMPC are explicitly constructed to perform robust design proposed dc-dc converter. was created out of set linearized systems at different operating conditions Time-Invari...

This article presents a novel methodology to predict the optimal adaptive cruise control set speed profile (ACCSSP) by optimizing engine operating conditions (EOC) considering vehicle level vectors (VLV) (body parameter, environment, driver behaviour) as affecting parameters. paper investigates criteria develop predictive model of ACCSSP in real-time. We developed deep learning (DL) using NARX ...

The three-phase inductor and capacitor filter (LC)-filtered voltage source inverter (VSI) is subjected to uncertain time-variant parameters disturbances, e.g., due aging, thermal effects, load changes. These uncertainties disturbances have a considerable impact on the performance of VSI’s control system. It can degrade system or even cause instability. Therefore, considering effects all in desi...

Due to varying characteristics of the wind condition, performance turbines can be optimized by adapting parameters control system. In this letter, an adaptive technique is proposed for novel model predictive (MPC) yaw system turbines. The horizon adapted one with best among multiple horizons. MPC demonstrated simulations using real data, and its compared baseline at fixed horizon. Results show ...

Model Predictive Control and Identification is an adaptive control technique which solves an online optimization problem to find process inputs for dual control problem. Its main goal is to bring robustness, to Model Predictive Control, increase the capability to handle uncertainties and time varying parameters in the processes. Theoretical properties, such as feasibility of the optimization pr...