The Engine Control Research Laboratory at UH focuses on research, education and technology transfer aspects involving the regulations, optimization, control, monitoring and diagnostics of internal combustion engine and power train systems with the overall objective of optimizing their economy and harmful emission reduction.
Developing controller design methodologies that generate production-intent engine controllers for low emissions, improved fuel economy and optimal performance. This includes simplified controller calibration processes (desktop calibration), improved performance and guaranteed robustness.
*Engineering desktop and data driven tools for (a) multivariable controller design, (b) linear/nonlinear robust controller design, (c)gain scheduling controller design, and (d) self-calibrating controllers.
*Multivariable control for loop cooperation in multi-objective engine air handling applications.
*Optimal integration of the engine, exhaust aftertreatment systems, and powertrain.
*Engine/aftertreatment diagnostics & prognostics using information synthesis and simplified models.
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ENGINE CONTROL RESEARCH LABORATORY | |
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—————————————————————————————————————————————— Selected Past Project Descriptions—————————————————————————————————————————————— Learning Controller Based DiagnosticsDemonstrated is the use of robust control for precision tracking applied to system subjected to periodic sequences. The objective is to achieve robust tracking of the periodic inputs/disturbances for engine diagnostics and controller parameter tuning. The control solution is systematic where the controller design process can be executed in a desktop environment using a data driven approach. Lean NOx Trap Gain Scheduled ControlDemonstrated is the linear parameter varying (LPV) control for Lean Operation in SI engines. The objective is to maximize A/F ratio regulating performance despite variations in engine speed and transport delay. The control solution provides the systematic design of a low complexity gain scheduled controller using LPV methods. Model Based DiagnosticsInformation Synthesis is a knowledge basis that integrates online system identification techniques with first principle models to realize accurate prognostics of engines. This approach compresses large amounts of data into a minimal realization, significantly reduces false detections caused by system variability and sensor noise, and addresses transient operation as well as steady state operation. Data Driven Controller IdentificationDemonstrated
is the use of data for the purpose of controller identification
as opposed to controller design. The objective is to identify controllers
using nonparametric and parametric modeling techniques. The control
solution reveals controller structure and calibration in a systematic
design process that can be executed in a desktop environment. Three Way Catalyst Control & Diagnostics Demonstrated is the adaptive control of a Three-Way
Catalyst (TWC). The objective is to maximize the TWC conversion
efficiencies despite TWC health and drive cycle. The control solution
requires the calibration of only 2 parameters. Since on-line TWC
parameter estimation is part of the control solution, TWC health
estimation is also achieved. Multivariable Control-Loop CooperationDemonstrated is the use of loop interaction in multivariable
systems. The objective is to exploit multivariable control to achieve
multi-objectives in engine transient control, air handling, torque/speed
quality and combustion management. The control solution is systematic
where the loop interactions are displayed controller design process
can be executed in a desktop environment. Self-Tuning 2DOF Adaptive ControllersDemonstrated is the online identification of a model from measured inputs/outputs (I/O) and the integration of this adapting model into a robust feedback controller design solution. The objective is to automatically adapt controller parameters based on the adapted I/O model. The control solution directly addresses meaningful performance specifications, saturation and time delays. Automating Governor CalibrationThe objective of this research is to develop a systematic diesel engine PI governor design methodology independent of the application. The solution approach is to integrate online modeling and robust controller design in two steps: first, a system identification is performed in the four-step instrumental variable (IV4) method, and second, an adaptive controller is designed based on Quantitative Feedback Theory (QFT) and is executed on a Nichols Chart. EGR System Real-Time Fault Diagnostics in Diesel Engines Using Least-Squares MethodsA model-based real-time fault detection
and estimation methodology is proposed to detect high flow or low
flow in the EGR system of Diesel engines. An advantage of the proposed
method is its capability for estimating the magnitude of a fault.
The method was successfully validated to diagnose low flow and high
flow faults in Diesel engines using experimental data
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