Leighton Ira Davis

Dynamic neural network methods applied to on-vehicle idle speed control

The application of neural network techniques to the control of nonlinear dynamical systems has been the subject of substantial interest and research in recent years. In our own work, we have concentrated on extending the dynamic gradient formalism as established by Narendra and Parthasarathy (1990, 1991), and on employing it for applications in the control of nonlinear systems, with specific emphasis on automotive subsystems. The results we have reported to date, however, have been based exclusively upon simulation studies. In this paper, we establish that dynamic gradient training methods can be successfully used for synthesizing neural network controllers directly on instances of real systems. In particular we describe the application of dynamic gradient methods for training a time-lagged recurrent neural network feedback controller for the problem of engine idle speed control on an actual vehicle, discuss hardware and software issues, and provide representative experimental results.

Fuzzy logic anti-lock brake system for a limited range coefficient of friction surface

Describes the preliminary research on and implementation of a fuzzy logic controller to control wheel slip for an antilock brake system. The dynamics of braking systems are highly nonlinear and time-variant. Simulation was used to derive an initial rule base, which was then tested on an experimental brake system. The rules were further refined by analysis of the data acquired from vehicle braking maneuvers on a surface with a high coefficient of friction. The robustness of the fuzzy logic slip regulator was further tested by varying operating conditions and external environmental variables.<<ETX>>

Neural network modeling and control of an anti-lock brake system

The authors have previously described neural-network-based methods for modeling automotive systems and training near-optimal controllers. These methods are based on the premise that the physical system can be sufficiently instrumented during network training so that accurate evaluation of the effect of control actions is possible. In certain systems, such a automotive anti-lock braking (ABS), it may be costly to obtain the detailed data that would be required to exploit the full capabilities of neural methods. The present paper reports an initial simulation-based study to determine the performance potential of controllers designed with these methods. Such studies will help determine whether the cost of carrying out neural training methods on actual systems is justified.<<ETX>>

Neural control systems trained by dynamic gradient methods for automotive applications

The use of dynamic gradient-based training of neural controllers for automotive systems is illustrated. The authors use a recurrent structure that embeds an identification network and a neural controller and that properly treats both short- and long-term effects of controller weight changes. This results in an approximately optimal control strategy. Feedforward and hybrid feedforward-feedback neural controllers trained by dynamic backpropagation and a dynamic decoupled extended Kalman filter (DDEKF) are investigated. A quarter-car active suspension model is considered in both linear and nonlinear forms, and representative results are presented. Methods using higher-order information, e.g., DDEKF are very effective in comparison to methods based exclusively upon gradient descent, e.g., dynamic backpropagation (DBP). The use of a recurrent structure for obtaining derivatives for controller training is illustrated.<<ETX>>

Fuzzy logic for vehicle climate control

Typical automobile automatic climate control systems use linear proportional control to maintain a comfortable interior environment. In the process of refining these systems, we have found two significant limitations of linear proportional control when viewed from the standpoint of an occupants subjective comfort: 1) there are certain control situations in any HVAC (Heating, Ventilation, and Air Conditioning) system that are inherently nonlinear; and 2) it is not possible to realize occupant comfort merely by maintaining proximity to a desired temperature. In this paper, we describe a fuzzy logic control system which addresses these limitations by including rules that provide nonlinear compensation, and by allowing the control to be expressed in the same heuristic terms that an occupant would use in describing the level of comfort.<<ETX>>

Controlling cyclic combustion variations in lean-fueled spark-ignition engines

This paper describes the reduction of cyclic combustion variations in spark-ignited engines, especially under idle conditions in which the air-fuel mixture is lean of stoichiometry. Under such conditions, the combination of residual cylinder gas and parametric variations (such as variations in fuel preparation) gives rise to significant combustion instabilities that may lead to customerperceived engine roughness and transient emissions spikes. Such combustion instabilities may preclude operation at air-fuel ratios that would otherwise be advantageous for fuel economy and emissions. This approach exploits the recognition that a component of the observed combustion instability results from a noisedriven, nonlinear deterministic mechanism that can be actively stabilized by small feedback control actions which result in little if any additional use of fuel. Application of this approach on a test vehicle using crankshaft acceleration as a measure of torque and fuel pulse width modification as a control shows as much as 30% reduction in rms variation near the lean limit.

Training a hybrid neural-fuzzy system

It is shown that hybrid neural-fuzzy systems can be described almost as concisely as conventional layered neural networks and can be subjected to the same methods for training. Combining elements of neural and fuzzy systems in this way offers clear benefits whenever the training a neural network can be improved by incorporation of prior knowledge or where a fuzzy system requires careful tuning. The examples suggest that the inclusion of fuzzy elements in a neural network framework may, for certain applications, increase representational power with fewer parameters than would be required by merely increasing the number of conventional nodes and layers.<<ETX>>

Neural network control of a four-wheel ABS model

This paper presents simulation studies of neural network controller training carried out on a four-wheel anti-lock brake system (ABS) model. The indirect controller training method utilized here requires prior training of an identification network, which is then used in a sensitivity circuit that embeds the identification network and carries out a form of real-time recurrent learning. The training of both identification and controller networks makes use of a decoupled extended Kalman filter (DEKF) update scheme. To the extent that the model represents a real system, the resulting controller may be applicable to an actual vehicle. More likely, in our estimation, is that such studies will be useful in determining appropriate identification and controller architectures for on-vehicle training and in determining how to carry out such training.

Strategies and issues in applications of neural networks

The authors survey some of the fundamental aspects of neural networks that have been found crucial to their application to practical problems in diagnostics, modeling, and control. The analysis is a loosely connected collection of remarks on difficulties that have been encountered and the approach to dealing with them. Promising approaches now being explored and suggestions for future work are outlined. The issues raised concern the following: neural nets and engineering; training by higher order methods; sparse data and generalization; local representation networks; prestructured networks; scaling nodes; context switching; recurrent networks; neural controller development; and fusion of neural nets and fuzzy logic.<<ETX>>

Controlling Cyclic Combustion Variations in Lean‐Fueled Spark‐Ignition Engines

Under conditions of lean fueling or high exhaust gas recirculation, interactions between residual cylinder gas and freshly injected fuel and air produce undesirable combustion instabilities in spark‐ignition engines. The resulting dynamics can be described as noisy bifurcations of a nonlinear map and are complicated by cylinder‐to‐cylinder coupling. We discuss the key dynamic features relevant to control and demonstrate simple feedback control of a multi‐cylinder test vehicle.