James C. Peyton Jones

Adaptive Analytical Model-Based Control for SI Engine Air–Fuel Ratio

An adaptive, state-space, model-predictive controller for spark ignition (SI) engine air-fuel ratio control is developed and presented in this brief. The linear model-based controller is an analytical controller that does not require online optimization. The model parameters for the predictive controller and the time-varying delay compensation are adapted based on the current measured engine speed and load. These process model parameters and time delays are identified from engine operating data. A Kalman filter is used to estimate the model and disturbance states of the system. The controller is demonstrated on a Ford 2-L I4 engine.

Integrating the Microsoft Kinect With Simulink: Real-Time Object Tracking Example

The introduction of low-cost, relatively high-resolution 3-D sensing systems, like the Microsoft Kinect, has considerable potential in autonomous system applications. One of the impediments to wider Kinect application development is that the available Kinect drivers define C language interfaces. To help fully realize the Kinects potential, the aims of this paper are 1) to develop a “VU-Kinect ” block which provides easy access to the sensors camera and depth image streams, and which enables them to be incorporated seamlessly within a higher level, Simulink-based, image-processing and real-time control strategy; 2) to address implementation issues associated with the Kinect, such as sensor calibration; and 3) to show the utility of both the VU-Kinect block and the Kinect itself through a simple 3-D object tracking example.

Likelihood-Based Control of Engine Knock

Engine knock is an undesirable phenomenon, which requires feedback control in order to maximize engine efficiency and avoid damage to the engine. In this paper, an analysis of experimental data is used to provide further evidence that knock behaves as a cyclically uncorrelated random process. It is argued that all knock controllers are therefore ultimately stochastic in nature and that the knock control problem is best undertaken within a stochastic framework. The properties of knock events are discussed and, based on these properties, a new likelihood-based stochastic knock controller is presented. The new controller achieves a significantly improved regulatory response relative to conventional strategies, while also maintaining a rapid transient response. It is therefore possible to operate closer to the knock limit without increasing the risk of engine damage.

Characterization of knock intensity distributions: Part 1: statistical independence and scalar measures

The characterization of knock intensity distributions can provide useful insights into the process and help to improve knock control system designs. In this paper, an extensive statistical analysis is performed on knock intensity data recorded under a broad range of operating conditions. First, the critical issue of whether the data exhibit any cycle-to-cycle correlations is investigated, and it is shown that knock intensity closely approximates a cyclically independent random process. The study then focuses on the variation of knock intensity distributions with operating condition, and on the quantification of these distributions using simple scalar measures. The relationship between knock event distributions and knock intensity distributions is also investigated, and it is shown that knock event data are binomially distributed regardless of the underlying knock intensity distribution. This supports ongoing efforts to exploit binomial probability theory in knock event simulation and controller design.

Identification and adaptation of linear look-up table parameters using an efficient recursive least-squares technique.

Linear look-up tables are widely used to approximate and characterize complex nonlinear functional relationships between system input and output. However, both the initial calibration and subsequent real-time adaptation of these tables can be time consuming and prone to error as a result of the large number of table parameters typically required to map the system and the uncertainties and noise in the experimental data on which the calibration is based. In this paper, a new method is presented for identifying or adapting the look-up table parameters using a recursive least-squares based approach. The new method differs from standard recursive least squares algorithms because it exploits the structure of the look-up table equations in order to perform the identification process in a way that is highly computationally and memory efficient. The technique can therefore be implemented within the constraints of typical embedded applications. In the present study, the technique is applied to the identification of the volumetric efficiency look-up table commonly used in gasoline engine fueling strategies. The technique is demonstrated on a Ford 2.0L I4 Duratec engine using time-delayed feedback from a sensor in the exhaust manifold in order to adapt the table parameters online.

Characterization of knock intensity distributions: Part 2: parametric models

Knock intensity behaves as a random process which may be characterized using simple scalar metrics such as the mean and variance, or (more commonly) by the probability of knock events. However, such measures discard much of the information present in the signal. Several researchers have therefore sought to obtain a more complete characterization of the process by fitting parametric log-normal or gamma distribution models to knock intensity distributions. The present study extends this work both in terms of the range of engine operating conditions considered and in terms of the evaluation of the goodness of fit between two different models and the experimental data. In particular, new and arguably more application-appropriate measures of the goodness of fit provide a clearer assessment of the performance of the models, and a like-for-like comparison of log-normal and gamma distribution model forms demonstrates that the log-normal model better characterizes the experimental data used in this study.

Stochastic simulation and analysis of a classical knock controller

Knock control remains a critical issue in modern engine powertrains, and a renewed emphasis on knock as a stochastic process has proved beneficial in the development of new controller designs. However, the random nature of knock also makes it hard to evaluate the closed-loop performance of a knock controller in a rigorous, repeatable way. This work therefore focuses particularly on the statistical properties of knock intensities and knock events, and a new Markov-based analysis is used to compute the corresponding statistical properties and distribution of the closed-loop response. The method is applied to a conventional knock controller, revealing new aspects of its behavior. In particular, the closed-loop spark advance distribution is found to be periodic initially, only collapsing to an invariant steady-state distribution as a result of limits applied to the spark advance actuation. The stochastic response of the controller to different initial conditions is also investigated, providing a more rigorous insight into its performance. The results of the Markov-based analysis are confirmed using Monte Carlo simulations.

A Fast-Acting Stochastic Approach to Knock Control

Abstract Stochastic methods arguably offer a more theoretically appropriate approach to knock control than conventional deterministic strategies but are typically much slower in their transient response. Recently, however, a new stochastic controller was developed which responds very rapidly to knock rates above the specified target and which maintains tight regulatory control about the knock limit. The response to overly retarded conditions, however, is still relatively slow. In this paper the algorithm is refined in order to speed up the rate of recovery from retarded conditions while maintaining tight control and fast retard action. The performance of the new algorithm is compared to a conventional knock controller. The comparison demonstrates that the stochastic controller presented in this work is able to operate at a mean spark timing that is more advanced with much less cyclic variation about this mean. The transient response to excess knocking events can be as fast, or faster, than the conventional controller though this depends to some extent on the particular realization of the random knock process. The transient response to retarded conditions is still not quite as fast as the conventional controller, but is at least four times faster than that previously achieved. Overall, the results of this work suggest that the stochastic controller will deliver increased torque and engine efficiency under knock limited conditions compared to a conventional deterministic controller under the same conditions.

Optimizing knock thresholds for improved knock control

A new method for optimizing the knock threshold is presented and shown to significantly improve the closed loop performance of a standard knock controller. Traditional approaches assume that in order to control potentially damaging knock events, it is necessary to use thresholds set to detect such events. The proposed new method takes a more stochastic view and sets the threshold such that it maximizes the sensitivity to changes in the knock intensity distribution. The behavior of a standard knock controller in response to different threshold and gain values is investigated and illustrated using experimental and simulation data. In particular, it is shown that optimizing the threshold and controller parameters in the manner proposed results in a controller with fast transient response, improved mean spark advance, and reduced cyclic dispersion. With no modifications other than optimizing the parameters of a standard controller, it is therefore possible to operate closer to the knock limit, thereby improving fuel efficiency, emissions, and output torque.

Experimental Validation of a Likelihood-Based Stochastic Knock Controller

New likelihood-based stochastic knock controllers have the potential to deliver a significantly improved regulatory response relative to conventional strategies, while also maintaining a rapid transient response, but evaluation studies to date have been performed only in simulation. In this paper, an experimental validation of the new strategy is presented. To demonstrate the robustness of the method, the algorithm is implemented on two different engine platforms, using two different knock intensity metrics, and evaluated under different operating conditions. One of these platforms is a five-cylinder variable compression ratio engine, enabling the controller to be tested under different compression ratios, as well as different speed and load conditions. The regulatory and transient performance of the likelihood-based controller is assessed in a back-to-back comparison with a conventional knock controller and it is shown that the new controller is able to operate closer to the knock limit with less variation in control action without increasing the risk of engine damage.