Human-in-the-Loop Model Predictive Control of an Irrigation Canal

Until now, advanced model-based control techniques have been predominantly employed to control problems that are relatively straightforward to model. Many systems with complex dynamics or containing sophisticated sensing and actuation elements can be controlled if the corresponding mathematical models are available, even if there is uncertainty in this information. Consequently, the application of model-based control strategies has flourished in numerous areas, including industrial applications [1]-[3].Junta de Andalucía P11-TEP-812

Lipid Classes and Fatty Acid Patterns are Altered in the Brain of γ-Synuclein Null Mutant Mice

The well-documented link between α-synuclein and the pathology of common human neurodegenerative diseases has increased attention to the synuclein protein family. The involvement of α-synuclein in lipid metabolism in both normal and diseased nervous system has been shown by many research groups. However, the possible involvement of γ-synuclein, a closely-related member of the synuclein family, in these processes has hardly been addressed. In this study, the effect of γ-synuclein deficiency on the lipid composition and fatty acid patterns of individual lipids from two brain regions has been studied using a mouse model. The level of phosphatidylserine (PtdSer) was increased in the midbrain whereas no changes in the relative proportions of membrane polar lipids were observed in the cortex of γ-synuclein-deficient compared to wild-type (WT) mice. In addition, higher levels of docosahexaenoic acid were found in PtdSer and phosphatidylethanolamine (PtdEtn) from the cerebral cortex of γ-synuclein null mutant mice. These findings show that γ-synuclein deficiency leads to alterations in the lipid profile in brain tissues and suggest that this protein, like α-synuclein, might affect neuronal function via modulation of lipid metabolism

Human in the Loop Model Predictive Control: an Irrigation Canal Case Study

53rd IEEE Conference on Decision and Control. 15-17 Dec. 2014 Los Angeles, CA, USAIn this work, we propose to expand the application of model predictive control (MPC) to problems in which there are human agents involved in the sensing and actuation processes. To this end, a new configuration of a control system structure that combines centralized predictive control and local operations is presented. Additional constraints are included in the optimization problem to take into account the mobility and the role of the operators over the prediction horizon. This new type of control system structure, referred to as Mobile Model Predictive Control (MoMPC), is tested on a linear model of a large scale irrigation canal and its performance is compared to centralized MPC and manual operation