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BACKGROUND Oils produced by microalgae are precursors to biodiesel. To achieve a profitable production of biodiesel from microalgae, identification of factors governing oil synthesis and turnover is desirable. The green microalga Chlamydomonas reinhardtii is amenable to genetic analyses and has recently emerged as a model to study oil metabolism. However, a(More)
This paper presents time scale analysis and synthesis (control) methodology for Model Predictive Control (MPC). In this method, a higher-order plant with a two-time (slow and fast) scale character is analyzed (decoupled) into low-order slow and fast subsystems and sub-augmented systems. Then slow and fast subcontrollers based on MPC method are synthesized(More)
As the demand of the wind energy increase, investigations focus on maximizing the energy extraction and efficiency through the design of key components such as blades, gearboxes, generators, etc and implementation of advanced control strategies. This paper focuses on a new application of nonlinear, feedback optimal control strategy to Wind Energy Conversion(More)
Microalgae have emerged as a promising source for biofuel production. Massive oil and starch accumulation in microalgae is possible, but occurs mostly when biomass growth is impaired. The molecular networks underlying the negative correlation between growth and reserve formation are not known. Thus isolation of strains capable of accumulating carbon(More)
An essential control objective of wind energy conversion systems (WECSs) is to maximize the conversion of wind energy into electrical energy. This control objective is difficult to achieve using linear control techniques because the WECSs are time-varying and highly nonlinear. In this paper, we propose a nonlinear fuzzy adaptive output feedback control(More)
—This paper presents a reduced-order H∞ optimal control for wind energy conversion systems. Two different timescale (slow and fast) dynamics of wind energy conversion systems are separated and processed independently using the singular perturbation theory. By using the decomposition technique, low-order, independent H∞ optimal filters and controllers are(More)
This paper presents the design and simulation of adaptive PI controllers for doubly fed induction generators using b-spline neural networks. The control structure is based on a back-to-back arrangement where the interest variables are regulated by PI linear controllers. Also, to deal with the nonlinear and uncertain system conditions, we proposed that the(More)
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