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The increasing number of controllable parameters in modern engine systems has led to increasingly complicated and enlarged engine control software. This in turn has created dramatic increases in software development time and cost. Model-based control design seems to be an effective way to reduce development time and costs and also to enable engineers to understand the complex relationship between the many controllable parameters and engine performance. In the present study, we have developed model-based methodologies for the engine calibration process, employing engine cycle simulation and regression analysis. The reliability of the proposed method was investigated by validating the regression model predictions with measured data.

We have developed a model-based control for the air intake system in a variable valve engine, employing total engine simulation, the response surface method and multi-objective optimization scheme. In our technique, we performed the simulation model tuning and validation, followed by the creation of a dataset for the polynomial regression analysis of the charging efficiency. A D-optimal design, robust least squares method, and likelihood-ratio test were demonstrated to yield a robust and accurate control model. Coupling the total engine simulator with a genetic algorithm, model based calibration for optimal valve timing stored in lookup table was carried out under multiple objectives and restrictions. The reliability of the implementation control model, which considers the effect of gas dynamics in the intake system, was confirmed using a model-in-the-loop simulation.

The purpose of this study is to develop model-based methodologies which employ thermo-fluid dynamic engine simulation and multiple-objective optimization schemes for engine control and calibration, and to validate the reliability of the method using a dynamometer test. In our technique, creating a total engine system model begins by first entirely capturing the characteristics of the components affecting the engine system's behavior, then using experimental data to strictly adjust the tuning parameters in physical models. Engine outputs over the full range of engine operation conditions as determined by design of experiment (DOE) are simulated, followed by fitting the provided dataset using a nonlinear response surface model (RSM) to express the causal relationship among engine operational parameters, environmental factors and engine output. The RSM is applied to an L-jetronic® air-intake system control logic for a turbocharged engine.

The increasing number of controllable parameters in modern engine systems leads to complicated and enlarged engine control software. This in turn has led to dramatic increases in software development time and costs in recent years. Model-based control design seems to be an effective way to reduce development time and costs. In the present study, we have developed model-based methodologies for the engine calibration process using an engine cycle simulation technique combined with a regression analysis of engine responses. From the results it was clear that the engine cycle simulation technique was useful in the engine calibration process, if the empirical parameters included in physical models were adjusted at typical sampling-points in several engine speeds and loads. The cycle simulation produced a multi-dimensional MBT map, and a response surface method was employed in the modeling of the engine map dataset using a polynomial equation.

The concept of a “controller grid”, which makes effective use of computational resources distributed on a network while guaranteeing real-time operation, is proposed and applied to realize highly advanced control. It facilitates the total optimization of a plant control and achieves the high efficiency that is not acquired by individual plant optimization. To realize this concept, migration of a control task customized to be executed on one particular microcontroller to another microcontroller is necessary while strictly observing the required response time. Two techniques to meet this requirement are proposed: “task migration” for a control system and “real-time guaranteed scheduling of task migration and execution”. The effectiveness of the controller grid is assessed by applying it in experiments with electronic-throttle-body (ETB) advanced control.

The flight area of drones and other unmanned aerial vehicles (UAVs) had been highly restricted but has been relaxing, including flights beyond the scope of sight. Deregulation without aircraft-reliability improvement increases the risk of accidents. However, demanding high reliability for all aircraft leads to an increase in the price of the aircraft. Therefore, if airspace restrictions are relaxed for more reliable aircraft, the cost of higher reliability and its benefits can be balanced. This will improve efficiency and optimize cost-effectiveness. The purpose of this proposal is to balance the cost of aircraft-reliability improvement (which allows flight to continue in the event of a failure) and its advantages. Specifically, the author proposes rules that apply more relaxed airspace restrictions to UAVs with higher FCLs (Flight Continuity Possibility Levels) and stricter airspace restrictions to those with lower FCLs.