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Dual-fuel combustion engines have shown the potential to extend the operating range of Homogeneous Charge Compression Ignition (HCCI) by using several combustion modes, e.g. Reactivity Controlled Compression Ignition (RCCI) at low/medium load, and Partially Premixed Compression (PPC) at high load. In order to optimize the combustion mode operation, the respective sensitivity to the control inputs must be addressed. To this end, in this work the extremum seeking algorithm has been investigated. By definition, this technique allows to detect the control input authority over the system by perturbing its value by a known periodic signal. By analyzing the system response and calculating its gradient, the control input can be adjusted to reach optimal operation. This method has been applied to a dual-fuel engine under fully, highly and partially premixed conditions where the feedback information was provided by in-cylinder pressure and NOx sensors.

Low pressure exhaust gases recirculation (LP-EGR) is becoming a state-of-the-art technique for Nitrogen oxides (NOx) reduction in compression ignited (CI) engines. However, despite the pollutant reduction benefits, LP-EGR suffers from strong non-linearities and delays which are difficult to handle, resulting in reduced engine performance under certain conditions. Measurement and observation of oxygen concentration at the intake have been a research topic over the past few years, and it may be critical for transition phases (from low pressure to high pressure EGR). Here, an adequate selection of models and sensors is essential to obtain a precise and fast measurement for control purposes. The present paper analyses different sensor configurations, with oxygen concentration measurements at the intake and exhaust manifold and combines observation techniques with sensor models to determine the potential of each configuration.