Output Feedback Controller for Operation of Spark Ignition Engines at Lean Conditions Using Neural Networks

Spark ignition (SI) engines operating at very lean conditions demonstrate significant nonlinear behavior by exhibiting cycle-to-cycle bifurcation of heat release. Past literature suggests that operating an engine under such lean conditions can significantly reduce NO emissions by as much as 30% and improve fuel efficiency by as much as 5%-10%. At lean conditions, the heat release per engine cycle is not close to constant, as it is when these engines operate under stoichiometric conditions where the equivalence ratio is 1.0. A neural network controller employing output feedback has shown ability in simulation to reduce the nonlinear cyclic dispersion observed under lean operating conditions. This neural network (NN) output controller consists of three NNs: a) an NN observer to estimate the states of the engine such as total fuel and air; b) a second NN for generating virtual input; and c) a third NN for generating actual control input. The uniform ultimate boundedness of all closed-loop signals is demonstrated by using the Lyapunov analysis without using the separation principle. Persistency of the excitation condition, the certainty equivalence principle, and the linearity in the unknown parameter assumptions are also relaxed. The controller is implemented for a research engine as a program running on an embeddable PC that communicates with the engine through a custom hardware interface, and the results are similar to those observed in simulation. Experimental results at an equivalence ratio of 0.77 show a drop in NO emissions by around 98% from stoichiometric levels with an improvement of fuel efficiency by 5%. A 30% drop in unburned hydrocarbons from uncontrolled case is observed at this equivalence ratio of 0.77. Similar performance was observed with the controller on a different engine

Neural network control of nonstrict feedback and nonaffine nonlinear discrete-time systems with application to engine control

In this dissertation, neural networks (NN) approximate unknown nonlinear functions in the system equations, unknown control inputs, and cost functions for two different classes of nonlinear discrete-time systems. Employing NN in closed-loop feedback systems requires that weight update algorithms be stable...Controllers are developed and applied to a nonlinear, discrete-time system of equations for a spark ignition engine model to reduce the cyclic dispersion of heat release --Abstract, page iv

Neural Network Control of Spark Ignition Engines with High EGR Levels

Research has shown substantial reductions in the oxides of nitrogen (NOx) concentrations by using 10% to 25% exhaust gas recirculation (EGR) in spark ignition (SI) engines [1]. However under high EGR levels the engine exhibits strong cyclic dispersion in heat release which may lead to instability and unsatisfactory performance. A suite of neural network (NN)-based output feedback controllers with and without reinforcement learning is developed to control the SI engine at high levels of EGR even when the engine dynamics are unknown by using fuel as the control input. A separate control loop was designed for controlling EGR levels. The neural network controllers consists of three NN: a) A NN observer to estimate the states of the engine such as total fuel and air; b) a second NN for generating virtual input; and c) a third NN for generating actual control input. For reinforcement learning, an additional NN is used as the critic. The stability analysis of the closed loop system is given and the boundedness of all signals is ensured without separation principle. Online training is used for the adaptive NN and no offline training phase is needed. Experimental results obtained by testing the controller on a research engine indicate an 80% drop of NOx from stoichiometric levels using 10% EGR. Moreover, unburned hydrocarbons drop by 25% due to NN control as compared to the uncontrolled scenario

Neural Network Controller Development and Implementation for Spark Ignition Engines with High EGR Levels

Past research has shown substantial reductions in the oxides of nitrogen (NOx) concentrations by using 10% -25% exhaust gas recirculation (EGR) in spark ignition (SI) engines (see Dudek and Sain, 1989). However, under high EGR levels, the engine exhibits strong cyclic dispersion in heat release which may lead to instability and unsatisfactory performance preventing commercial engines to operate with high EGR levels. A neural network (NN)-based output feedback controller is developed to reduce cyclic variation in the heat release under high levels of EGR even when the engine dynamics are unknown by using fuel as the control input. A separate control loop was designed for controlling EGR levels. The stability analysis of the closed-loop system is given and the boundedness of the control input is demonstrated by relaxing separation principle, persistency of excitation condition, certainty equivalence principle, and linear in the unknown parameter assumptions. Online training is used for the adaptive NN and no offline training phase is needed. This online learning feature and model-free approach is used to demonstrate the applicability of the controller on a different engine with minimal effort. Simulation results demonstrate that the cyclic dispersion is reduced significantly using the proposed controller when implemented on an engine model that has been validated experimentally. For a single cylinder research engine fitted with a modern four-valve head (Ricardo engine), experimental results at 15% EGR indicate that cyclic dispersion was reduced 33% by the controller, an improvement of fuel efficiency by 2%, and a 90% drop in NOx from stoichiometric operation without EGR was observed. Moreover, unburned hydrocarbons (uHC) drop by 6% due to NN control as compared to the uncontrolled scenario due to the drop in cyclic dispersion. Similar performance was observed with the controller on a different engine

Neural Network-Based Output Feedback Controller for Lean Operation of Spark Ignition Engines

Spark ignition (SI) engines running at very lean conditions demonstrate significant nonlinear behavior by exhibiting cycle-to-cycle dispersion of heat release even though such operation can significantly reduce NOx emissions and improve fuel efficiency by as much as 5-10%. A suite of neural network (NN) controller without and with reinforcement learning employing output feedback has shown ability to reduce the nonlinear cyclic dispersion observed under lean operating conditions. The neural network controllers consists of three NN: a) A NN observer to estimate the states of the engine such as total fuel and air; b) a second NN for generating virtual input; and c) a third NN for generating actual control input. For reinforcement learning, an additional NN is used as the critic. The uniform ultimate boundedness of all closed-loop signals is demonstrated by using Lyapunov analysis without using the separation principle. Experimental results on a research engine at an equivalence ratio of 0.77 show a drop in NOx emissions by around 98% from stoichiometric levels. A 30% drop in unburned hydrocarbons from uncontrolled case is observed at this equivalence ratio

Addressing nonlinear combustion instabilities in highly dilute spark ignition engine operation

Dilute operation is a promising approach for increasing spark-ignition engine efficiency, in the form of either lean burn (air dilution) or EGR (inert dilution). High levels of charge dilution, however, lead to cyclic variability that is largely deterministic in nature. The determinism and nonlinear nature of the system indicate that it should be possible to reduce the cycle-to-cycle variations by implementing an electronic controller. Several needs arise when considering the development of such a controller. Three topics of interest are covered herein. First, a method of analysis for nonlinear dynamical systems is applied to engine data in order to estimate the effect that a controller could have by removing the cycles that contribute to repeated, deterministic sequences...Second, the sensitivity of the engine to variations in control input is evaluated by examining a FFT of heat release data when the injected fuel mass is perturbed in a periodic manner...Finally, a combined thermodynamic and turbulent mass entrainment model was developed to predict energy release for many consecutive engine cycles --Abstract, page iii

Reinforcement Learning Based Dual-Control Methodology for Complex Nonlinear Discrete-Time Systems with Application to Spark Engine EGR Operation

A novel reinforcement-learning-based dual-control methodology adaptive neural network (NN) controller is developed to deliver a desired tracking performance for a class of complex feedback nonlinear discrete-time systems, which consists of a second-order nonlinear discrete-time system in nonstrict feedback form and an affine nonlinear discrete-time system, in the presence of bounded and unknown disturbances. For example, the exhaust gas recirculation (EGR) operation of a spark ignition (SI) engine is modeled by using such a complex nonlinear discrete-time system. A dual-controller approach is undertaken where primary adaptive critic NN controller is designed for the nonstrict feedback nonlinear discrete-time system whereas the secondary one for the affine nonlinear discrete-time system but the controllers together offer the desired performance. The primary adaptive critic NN controller includes an NN observer for estimating the states and output, an NN critic, and two action NNs for generating virtual control and actual control inputs for the nonstrict feedback nonlinear discrete-time system, whereas an additional critic NN and an action NN are included for the affine nonlinear discrete-time system by assuming the state availability. All NN weights adapt online towards minimization of a certain performance index, utilizing gradient-descent-based rule. Using Lyapunov theory, the uniformly ultimate boundedness (UUB) of the closed-loop tracking error, weight estimates, and observer estimates are shown. The adaptive critic NN controller performance is evaluated on an SI engine operating with high EGR levels where the controller objective is to reduce cyclic dispersion in heat release while minimizing fuel intake. Simulation and experimental results indicate that engine out emissions drop significantly at 20% EGR due to reduction in dispersion in heat release thus verifying the dual-control approach

Reinforcement-learning based output-feedback controller for nonlinear discrete-time system with application to spark ignition engines operating lean and EGR

A spark ignition (SI) engine can be described by non-strict feedback nonlinear discrete-time system with the output dependent upon on the states in a nonlinear manner. The controller developed in this thesis utilizes the inherent universal approximation property of neural networks (NN) to simplify the design process and solve the non-causality problem inherent with traditional designs. It also exploits a long-term performance index called the strategic utility function to minimize and assist in updating of the NN weights; therefore, an optimal controller can be realized. Finally, through Lyapunov equations, the controller guarantees stability --Abstract, page iv

Investigation Into Advanced Architecture and Strategies For Turbocharged Compressed Natural Gas Heavy Duty SI-engine

CNG is at present retaining a growing interest as a factual alternative to traditional fuel for SI engine thanks to its high potentials in reducing the engine-out emissions. Increasing thrust into the exploitation of NG in the transport field is in fact produced by the even more stringent emission regulations which are being introduced into the worldwide scenario. Specific attention is also to be devoted to heavy duty engines given the high impact they retain due to the diesel oil exploitation and to the PM emissions, the latter issue assessing for the need to shift towards alternative fuels such as natural gas. A thorough control of the air-to-fuel ratio appears to be mandatory in spark ignition CNG engines in order to meet the even more stringent thresholds set by the current regulations. The accuracy of the air/fuel mixture highly depends on the injection system dynamic behavior and to its coupling to the engine fluid-dynamic. The amount of injected fuel should in fact be properly targeted by the ECU basing on the estimation of the induced air and accounting for the embedded closed-loop strategies. Still, these latter are normally derived from engine-base routines and totally ignore the injection system dynamics. Thus, a sound investigation into the mixing process can only be achieved provided that a proper analysis of the injection rail and of the injectors is carried out. The first part of the present work carries out a numerical investigation into the fluid dynamic behavior of a commercial CNG injection system by means of a 0D-1D code. The research has been focused on defining the set of parameters to be precisely reproduced in the 0D-1D simulation so as to match the injection system experimental behavior. Specific attention has been paid to the one component which significantly contributes to fully defining its dynamic response, i.e. the pressure reducing valve. The pressure reducer is made up of various elements that retain diverse weights on the valve behavior and should consequently be differently addressed to. A refined model of the pressure reducer has hence been proposed and the model has been calibrated, tested and run under various operating conditions so as to assess for the set-up validity. Comparisons have been carried out on steady state points as well as through out a vehicle driving cycle and the model capability to properly reproduce the real system characteristic has been investigated into. The proposed valve model has proved to consistently replicate the injection system response for different speed and load conditions. A few methodological indications concerning modeling aspects of a pressure regulator can be drawn from the present study. The model has been run in a predictive mode so as to inquiry into the response of the system to fast transient operations, both in terms of speed and load. The model outputs have highlighted mismatches between the ECU target mass and the actually injected one and have hinted at the need for dedicated and refined control strategies capable of preventing anomalies in the mixture formation and hence in the engine functioning. The second part of the present work aims at deeply investigating into the potentials of a heavy duty engine running on CNG and equipped with two different injection systems, an advanced SP one and a prototype MP one. The considered 7.8 liter engine was designed and produced to implement a Sigle-Point (SP) strategy and has hence been modified to run with a dedicated Multi-Point (MP) system so as to take advantage of its flexibility in terms of control strategies. More specifically, a thorough comparison between the experimental performances of the engine equipped with the two injection systems has been carried out at steady state as well as at transient operations. Better performances in terms of cycle-to-cycle variability were proved for the MP system despite poorer mixture homogeneity. Attention has also been paid to the different engine control strategies to be eventually adopted in compliance with the constraints set by the two different layouts. A 0D-1D model has also been built and validated on the experimental data set to be hence exploited for investigating into different strategies both for the SP and for the MP layout. An extensive simulation has been carried out on the effects of the injection phasing on the SP system performance referring to the engine power output and to the air-to-fuel ratio homogeneity amongst the cylinders. Finally, as far as the MP injection system is concerned, the innovative fire-skipping (DSF) or cylinder deactivation has been considered and deployed by means of the numerical model, assessing for an overall decrease in the fuel consumption of 12% at part load operations

Cylinder Pressure-Based Adaptive Air-Fuel Ratio Control and Compression HeatTransfer Analysis

芝浦工業大学2016年