Minimizing engine emissions using state-feedback control with LQR and artificial intelligence fuel estimator

This paper presents a novel engine controller targeting the reduction of gas emissions. Toxic emissions, such as Carbon Monoxide (CO) and Nitric Oxide (NOx) affect the environment and the authorities aim to limit their amount by law. Emissions are formed during the high temperature combustion process, and can be optimised by adjusting some engine operating parameters. In this paper, the model describing emissions output of the engine as a function of engine control parameters is represented as a state-space system. A closed-loop controller is developed by using statefeedback control algorithm. The closed-loop gain, K, is obtained from the LQR tuning principles. The fuel estimator developed in previous works is used in order to reduce the model from the 8th order. The results show that the controller is able to control emission to the minimum in all constraints while keeping engine running in the same performance

SI Engine Simulation Using Residual Gas and Neural Network Modeling to Virtually Estimate the Fuel Composition

Research in electronic controlled internal combustion engines mainly focuses on improving performance and lowering the emissions. Combustion performance depends on the geometry of cylinders and on the design of all mechanical parts, which are based on the laboratory experimental research. Due to the limitations of the materials used in the engine and the continuous high operating temperature, engines function in either spark ignition or charge ignition processes. Recent research on computer controlled engines uses sensors and electronic actuators which allows switching the engine operational mode between spark ignition and charge ignition. Thus, this makes possible to mix intake fuel compositions in order to give more choices to consumers. This study employs a neural network which is capable of estimating fuel composition using the parameters of residual gas. The simulation is based on a thermodynamic engine model implemented in Matlab Simulink. The main advantages are the capabilities of the model to 1) calculate the gas exchange as a function of time in transient mode, and 2) to generate data for the design control algorithms without the need of the engine bed test environment to test various fuel compositions

Interactions among mitochondrial proteins altered in glioblastoma

Mitochondrial dysfunction is putatively central to glioblastoma (GBM) pathophysiology but there has been no systematic analysis in GBM of the proteins which are integral to mitochondrial function. Alterations in proteins in mitochondrial enriched fractions from patients with GBM were defined with label-free liquid chromatography mass spectrometry. 256 mitochondrially-associated proteins were identified in mitochondrial enriched fractions and 117 of these mitochondrial proteins were markedly (fold-change ≥2) and significantly altered in GBM (p ≤ 0.05). Proteins associated with oxidative damage (including catalase, superoxide dismutase 2, peroxiredoxin 1 and peroxiredoxin 4) were increased in GBM. Protein–protein interaction analysis highlighted a reduction in multiple proteins coupled to energy metabolism (in particular respiratory chain proteins, including 23 complex-I proteins). Qualitative ultrastructural analysis in GBM with electron microscopy showed a notably higher prevalence of mitochondria with cristolysis in GBM. This study highlights the complex mitochondrial proteomic adjustments which occur in GBM pathophysiology

The optimal non-linear generalised predictive control by the time-varying approximation

This paper looks at the optimal non-linear generalised predictive control by the time-varying approximatio

Non-linear predictive control of 2 DOF helicopter model

This paper presents the application of non-linear predictive control algorithm to a helicopter model. First, the model of the helicopter is discussed. Next, the nonlinear algorithm is introduced which is based on state-space GPC controller. The non-linearity is handled by converting the state-dependent state-space representation into the linear time-varying representation. The predictions of the future controls are used to calculate predictions of the future states and of the future time varying system parameters. Applied to the helicopter model, the algorithm performs well. It is capable of the stabilizing the system for maneuvers for which it's linear counterpart fails