André Laurindo Maitelli

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In this paper a new approach of bilinear predictive control is presented. The approach is based in the Bilinear Generalized Predictive Control (BGPC), strategy that uses a time-step quasi-linearised NARIMAX model. In that approach, due to the used model, a prediction error exist, which increases with the prediction horizon, degrading the performance of that(More)
This paper shows a new approach of Model Predictive Control (MPC). A multivariable bilinear multi-model is presented. A set of local bilinear models is identified and the proposed algorithm implements the timestep quasilinearization in trajectory. A metric based in norms is presented to measure the distance from the current operation point to a designed(More)
Arterial pressure control is an important task, mostly in postsurgical patients. This work proposes an adaptive system to patient's arterial blood pressure control using sodium nitroprusside. The proposed system uses Proportional-Integral (PI), Fuzzy-PI, rule-based and predictive controllers. To introduce an adaptive characteristic to PI controller, it was(More)
Multiple model adaptive control procedures have been considered for a computer-based feedback system, which regulates the infusion rate of a drug (nitroprusside) in order to maintain the blood pressure as close as possible to the desirable value. Transfer function parameters can differ significantly between patients, and also time-dependent, so the(More)
In industrial applications involving control systems, PID controllers are present in the great majority of them, mostly because of a very simple architecture and easy tuning. For tuning them, the relay method is also very simple to use and, usually, reach some very satisfactory results, once combined with the appropriate strategy, like Ziegler-Nichols,(More)
Models of real systems are of fundamental importance for its analysis, making it possible to simulate or predict its behavior. Additionally, advanced techniques for controller design, optimization, monitoring, fault detection and diagnosis components are also based on process models. One of the most used techniques to model a system is by identification.(More)