Abstract: Various methods are described for monitoring cylinder valve deactivation in an engine, the engine including at least a cylinder valve actuator for deactivating at least a cylinder of the engine. One example method comprises indicating degradation of the valve actuator based on crankshaft acceleration.

Abstract: Various methods are described for monitoring cylinder valve deactivation in an engine, the engine including at least a cylinder valve actuator for deactivating at least a cylinder of the engine. One example method comprises indicating degradation of the valve actuator based on crankshaft acceleration.

Abstract: A method is provided of adaptively controlling a paint system for painting vehicle bodies. The method includes the steps of obtaining an estimated Jacobian that identifies characteristics of the paint system and using the estimated Jacobian to determine initial conditions to be implemented for the next vehicle body to be painted in the paint system. The method also includes the steps of storing a database of appropriate initial conditions that resulted in satisfactory transient response in a rule-base and using the rule-base at start-up of the paint system or if the paint system fails to be satisfactorily controlled.

Abstract: A method for air/fuel operation of an engine. The method includes providing a model of the engine. The model represents a relationship between: (1) a signal model LAMBSE, representative of estimated air/fuel ratio of the engine relative to a stoichiometric air/fuel ratio for the engine; and, (2) fuel injected into the cylinder of the engine. Exhaust gas oxygen emission from the engine is measured during operation of such engine. Actual LAMBSE produced by such engine during operation of such engine is produced as a function of such measured oxygen. The actual LAMBSE is compared with the model LAMBSE provided by the model in response to fuel injected into the engine to produce a model error signal. The fuel injected into the engine is adjusted in accordance with the error signal.

Abstract: A method for air/fuel operation of an engine. The engine is supplied fuel from both a fuel purging system to purge fuel in a fuel supply and feed such purged fuel to an intake manifold of the engine and a fuel injection system to inject fuel from such fuel supply into a cylinder of such engine. The method includes producing a first air/fuel ratio control signal in accordance with measured exhaust gas oxygen emission from the engine; producing a second air/fuel ratio control signal in accordance with fuel transport delay through the fuel purging system; combining the first and second air/fuel ratio control signals into a composite control signal; and feeding such composite control signal to the fuel injection system. Producing the first air/fuel ratio control signal comprises determining fuel flow rate through the purge system.

Abstract: A method for air/fuel operation of an engine. The engine is supplied fuel from both a fuel purging system to purge fuel in a fuel supply and feed such purged fuel to an intake manifold of the engine and a fuel injection system to inject fuel from such fuel supply into a cylinder of such engine. The method includes producing a first air/fuel ratio control signal in accordance with measured exhaust gas oxygen emission from the engine; producing a second air/fuel ratio control signal in accordance with fuel transport delay through the fuel purging system; combining the first and second air/fuel ratio control signals into a composite control signal; and feeding such composite control signal to the fuel injection system. Producing the first air/fuel ratio control signal comprises determining fuel flow rate through the purge system.

Abstract: An engine misfire detection system (10) for detecting engine misfire. System (10) includes a conventional controller (12) having a memory unit (14) and a plurality of sensors (16). Controller (12) includes a plurality of neural networks, which are trained by system (10), and which determine whether a firing event is a misfire based upon events occurring before the firing event and events occurring after the firing event. The neural networks are adaptively trained to compensate for the effects of engine variability and aging.

Abstract: A electronic engine control (EEC) module executes a neural network processing program to control the idle speed of an internal combustion engine by controlling the bypass air (throttle duty cycle) and the engine's ignition timing. The neural network is defined by a unitary data structure which defmes the network architecture, including the number of node layers, the number of nodes per layer, and the interconnections between nodes. To achieve idle speed control, the neural network processes input signals indicating the current operating state of the engine, including engine speed, the intake mass air flow rate, a desired engine speed, engine temperature, and other variables which influence engine speed, including loads imposed by power steering and air conditioning systems. The network definition data structure holds weight values which determine the manner in which network signals, including the input signals, are combined.

Abstract: Cyclic variation in combustion of a lean burning engine is reduced by detecting an engine combustion event output such as torsional acceleration in a cylinder (i) at a combustion event (k), using the detected acceleration to predict a target acceleration for the cylinder at the next combustion event (k+1), modifying the target output by a correction term that is inversely proportional to the average phase of the combustion event output of cylinder (i) and calculating a control output such as fuel pulse width or spark timing necessary to achieve the target acceleration for cylinder (i) at combustion event (k+1) based on anti-correlation with the detected acceleration and spill-over effects from fueling.

Abstract: An electronic engine control (EEC) module executes both open loop and closed loop neural network processes to control the air/fuel mixture ratio of a vehicle engine to hold the fuel mixture at stoichiometry. The open loop neural network provides transient air/fuel control to provide a base stoichiometric air/fuel mixture ratio signal in response to throttle position under current engine speed and load conditions. The base air/fuel mixture ratio signal from the open loop network is additively combined with a closed loop trimming signal which varies the air/fuel mixture ratio in response to variations in the sensed exhaust gas oxygen level. Each neural network function is defined by a unitary data structure which defines the network architecture, including the number of node layers, the number of nodes per layer, and the interconnections between nodes. In addition, the data structure holds weight values which determine the manner in which network signals are combined.

Abstract: A method of automating the calibration of lookup tables containing correction values to be used in an on-board vehicle system is disclosed. The method includes training a neural network to model engine behavior by outputting cylinder specific crankshaft acceleration correction values in response to any engine speed and load input conditions. The correction values generated are stored in a memory device. The training takes place off-board the vehicle, using a data set previously obtained from operating a representative engine under normal operating conditions.

Abstract: A microcontroller-based fuzzy logic processing module for generating control signal values in response to variable input signal values in accordance with constraints imposed by propositions or "rules" stored in memory in a standardized format. Each rule consists of one or more input conditions and an output directive. The input conditions and the output directive for a given rule are "fuzzified" by determining the degree of membership of each in fuzzy sets defined by membership functions. Fuzzy logic processing is accomplished by determining the extent to which the input conditions are satisfied by the current values of the input signals in order to develop a rule strength value, and then performing a "center-of-gravity" determination based on the rule strength values assigned to the output membership functions.

Abstract: A electronic engine control (EEC) module executes a generic neural network processing program to perform one or more neural network control funtions. Each neural network funtion is defined by a unitary data structure which defines the network architecture, including the number of node layers, the number of nodes per layer, and the interconnections between nodes. In addition, the data structure holds weight values which determine the manner in which network signals are combined. The network definition data structures are created by a network training system which utilizes an external training processor which employs gradient methods to derive network weight values in accordance with a cost function which quantitatively defines system objectives and an identification network which is pretrained to provide gradient signals representative the behavior of the physical plant. The training processor executes training cycles asynchronously with the operation of the EEC module in a representative test vehicle.

Abstract: A method for identifying engine combustion failure of an internal combustion engine having a plurality of cylinders, a crankshaft and a crankshaft position sensor includes the steps of operating the internal combustion engine to rotate the crankshaft, measuring rotational quantities of the crankshaft corresponding to events created by each of the plurality of cylinders during operation of the internal combustion engine, correcting the rotational quantities measured to remove periodic position irregularities to generate a corrected temporal signal, generating an acceleration signal of the crankshaft using the corrected temporal signals, and identifying combustion failures as a function of the acceleration signal. A time-lagged recurrent neural network utilizes the acceleration signal, along with other engine parameters to identify the cylinder-specific misfire events.

Abstract: Irregularities in crankshaft velocity introduced when measuring crankshaft rotation at a section of a crankshaft in an internal combustion engine that is less damped to torsional oscillations than is another more accessible crankshaft section are corrected by performing a nonlinear transformation via a neural network to predict rotation measurements that would have been obtained at the inaccessible section from data actually collected at the accessible crankshaft section. Thus, the effects of torsional oscillations in the crankshaft are substantially filtered away, resulting in crankshaft acceleration values that form the basis of a misfire detector having nearly maximum signal-to-noise performance.