Richard Stobart

Dynamic feedback linearization based control synthesis of the turbocharged Diesel engine

In this paper, a nonlinear control technique based on the property of flatness is proposed to control the diesel engine air system. More precisely, to achieve tracking of suitable references (corresponding to low emissions) for the air-fuel ratio and the fraction of the recirculated exhaust gas. The simulated diesel engine is a six cylinders medium duty Caterpillar 3126 B equipped with a variable geometry turbocharger and an exhaust gas recirculation valve. The proposed controller is designed on the reduced third order mean value model and implemented by endogenous linearizing dynamic feedback on the full order model. The controller is assessed through simulations with a SIL architecture using dSpace simulator. It exhibits good control performance without zero dynamics and ensures global stability and tracking of flat output references.

Brerwulf - the big bad wolf: I'll huff and I'll puff and I'll blow your house down!

Abstract This paper describes the design and performance of a test rig capable of reproducing the fluctuations of surface pressure caused by wind action on areas of buildings and on building components.

Modelling the Compression Ignition Engine for Control: Review and Future Trends.

Constraints change as pollutant standards or embedded diagnosis demands require improvements in model accuracy and their suitability for control algorithm synthesis. From th ermodynamic mathematical modelling to non-parametric models, a wide range of techniques has been investigated for the last thirty years involving both physicists and control engineers. The purpose of this paper is to give an overview of current modelling techniques oriented control analysis and design for compression ignition engines. Short examples illustrate each tech niques and existing applications are considered. Comparison of various engine models exhibit the trend to include more physical knowledge inside model-based control design.

An Explicit Model Predictive Control Framework for Turbocharged Diesel Engines

The turbocharged diesel engine is a typical multi-input multioutput system with strong couplings, actuator constraints, and fast dynamics. This paper addresses the exhaust emission regulation in turbocharged diesel engines using an explicit model predictive control (EMPC) approach, which allows tracking of the time-varying setpoint values generated by the supervisory level controller while satisfying the actuator constraints. The proposed EMPC framework consists of calibration, engine model identification, controller formulation, and state observer design. The proposed EMPC approach has a low computation requirement and is suitable for implementation in the engine control unit on board. The experimental results on a turbocharged Cat C6.6 diesel engine demonstrate that the EMPC controller significantly improves the tracking performance of the exhaust emission variables in comparison with the decoupled single-input single-output control methods.

Input Constraints Handling in an MPC/Feedback Linearization Scheme

Input Constraints Handling in an MPC/Feedback Linearization Scheme The combination of model predictive control based on linear models (MPC) with feedback linearization (FL) has attracted interest for a number of years, giving rise to MPC+FL control schemes. An important advantage of such schemes is that feedback linearizable plants can be controlled with a linear predictive controller with a fixed model. Handling input constraints within such schemes is difficult since simple bound contraints on the input become state dependent because of the nonlinear transformation introduced by feedback linearization. This paper introduces a technique for handling input constraints within a real time MPC/FL scheme, where the plant model employed is a class of dynamic neural networks. The technique is based on a simple affine transformation of the feasible area. A simulated case study is presented to illustrate the use and benefits of the technique.

Diesel engine emissions prediction using parallel neural networks

Emission legislation has forced the pace of development of engine management functions. Legislation that will be applied to diesel engines during the period 2010–2020 continue to put great emphasis on both nitrogen oxides NOx and particulate matter (PM). With the increasing effort to reduce emissions and maintain fuel economy manufacturers are focussing on engine control. Engine control requires data acquisition and acquisition requires sensors, but hardware in the form of sensors adds further cost to the production. As a result, so called virtual sensors are introduced. These are estimators that predict the required data, which is costly to measure or simply incapable of measurement. In this paper a parallel neural network structure is built. It consists of three Non-linear autoregressive exogenous input (NLARX) neural network models used to predict the smoke emissions of a diesel engine operated in a Non-Road-Transient Cycle. Existing resources from Matlab toolboxes are used in order to monitor both the cost and computational expenses of analysis. The data is re-ordered into training and validation sets and processed. To overcome the weakness of the neural network approach in respect of high frequency signals, the data is divided into layers to split up the frequencies and cut high amplitudes. Three horizontal layers of the signal are processed in parallel through independent NLARX-models and their performances are added to give an overall result.

Evaluation of Spray/Wall Interaction Models under the Conditions Related to Diesel HCCI Engines

Diesel homogeneous charge compression ignition (HCCI) engines with early injection can result in significant spray/wall impingement which seriously affects the fuel efficiency and emissions. In this paper, the spray/wall interaction models which are available in the literatures are reviewed, and the characteristics of modeling including spray impingement regime, splash threshold, mass fraction, size and velocity of the second droplets are summarized. Then three well developed spray/wall interaction models, ORourke and Amsden (OA) model, Bai and Gosman (BG) model and Han, Xu and Trigui (HXT) model, are implemented into KIVA-3V code, and validated by the experimental data from recent literatures under the conditions related to diesel HCCI engines. By comparing the spray pattern, droplet mass, size and velocity after the impingement, the thickness of the wall film and vapor distribution with the experimental data, the performance of these three models are evaluated. The results indicated that the predicted mean droplet diameters by HXT model are in better agreements with measurements due to the consideration of the gas density. However, the film thickness and fuel vapor distribution near the wall region are not significantly affected by the spray/wall interaction models, and all the models present inaccurate predictions relative to the experimental results.

Real-Time Energy Management for Diesel Heavy Duty Hybrid Electric Vehicles

In this paper, a fuzzy-tuned equivalent consumption minimization strategy (F-ECMS) is proposed as an intelligent real-time energy management solution for a conceptual diesel engine-equipped heavy duty hybrid electric vehicle (HEV). In the HEV, two electric motors/generators are mounted on the turbocharger shaft and engine shaft, respectively, which can improve fuel efficiency by capturing and storing energy from both regenerative braking and otherwise wasted engine exhaust gas. The heavy duty HEV frequently involved in duty cycles characterized by start-stop events, especially in off-road applications, whose dynamics is analyzed in this paper. The on-line optimization problem is formulated as minimizing a cost function in terms of weighted fuel power and electric power. In the cost function, a cost factor is defined for both improving energy transmission efficiency and maintaining the battery energy balance. To deal with the nonexplicit relationship between HEV fuel economy, battery state of charge (SOC), and control variables, the cost factor is fuzzy tuned using expert knowledge and experience. In relation to the fuel economy, the air-fuel ratio is an important factor. An online search for capable optimal variable geometry turbocharger (VGT) vane opening and exhaust gas recirculation (EGR) valve opening is also necessary. Considering the exhaust emissions regulation in diesel engine control, the boundary values of VGT and EGR actuators are identified by offline design-of-experiment tests. An online rolling method is used to implement the multivariable optimization. The proposed method is validated via simulation under two transient driving cycles, with the fuel economy benefits of 4.43% and 6.44% over the nonhybrid mode, respectively. Compared with the telemetry equivalent consumption minimization strategy, the proposed F-ECMS shows better performance in the sustainability of battery SOC under driving conditions with the rapid dynamics often associated with off-road applications.

Dynamic feedback linearization of Diesel engines with intake variable valve actuation

In this paper, a nonlinear control technique based on the property of flatness is proposed to control the air system of new generation diesel engines. The simulated engine is equipped with variable valve actuation (VVA), variable geometry turbocharger (VGT) and exhaust gas recirculation (EGR). The control objective is to achieve tracking of suitable references of flat outputs with corresponding intake conditions, which optimize fuel consumption under emission constraints. The proposed controller is designed on a fourth order nonlinear model and implemented by endogenous linearizing dynamic feedback on a full order mean value model. The controller assessed through simulations with a software-in-the-loop (SIL) architecture using dSpace simulator. It exhibits good control performance without zero dynamics and ensures global stability and tracking of flat output references.

Particulate matter prediction in both steady state and transient operation of diesel engines

Diesel engines produce a variety of particles generically classified as diesel particulate matter (PM) owing to incomplete combustion. The increasingly stringent emissions regulations require that engine manufacturers must continue to reduce the PM. The ability to predict the PM emissions is one of the key technologies that could be used in a PM reduction strategy. This paper describes a predictive technique that can be used as a virtual sensor for monitoring PM emissions in both steady and transient states for a medium- or heavy-duty diesel engine. The predictive structure is stable over a broad range of engine operation points. The input parameters are chosen on the basis of the PM formation mechanism, physical knowledge of the process, and an insight into the underlying physics. Principal-component analysis (PCA) is used to reduce the dimensionality of the inputs of a non-linear autoregressive model with exogenous inputs (NLARX) from nine inputs to five inputs. PCA not only reduces the input number but also improves the performance of the prediction model. The results show that the NLARX model could predict the particulate matter successfully with an R2 value above 0.99 with only five inputs.