Design and Validation of a Model‐Based Starting Speed Control Scheme for Spark Ignition Engines

The main objective of this paper is to present the design and experimental validations of a model‐based control scheme, which aims at smooth and fast response for spark ignition (SI) engines during the starting phase. The control scheme consists of the fuel injection control loop and the speed regulation loop. As far as the fuel control is concerned, an observer‐based estimation scheme is designed which includes the air charge estimation for each cylinder: this estimation is needed to generate the individual injection command by inverting the dynamical model of fuel path. The speed regulation is achieved by an event‐based coordinated feedback control of spark advance (SA) and throttle opening: the convergence analysis is shown by using the Lyapunov–Krasovskiii stability theorem for time‐delay systems. The control scheme proposed formerly by the authors, which was only tested by simulations, is redesigned and recalibrated to meet the robustness specifications which were violated in experiments. The redesigned controller is found to be satisfactory in extensive experimental tests.     

车用汽油发动机电子控制系统研究现状与展望

发动机电子控制系统是高性能、高可靠性发动机开发的核心研究内容,是保证发动机动力性、经济性和排放性的重要因素之一.针对车用汽油发动机,本文首先分析了典型的车用汽油发动机电子控制系统结构,然后围绕电子节气门控制系统、燃油喷射控制系统、点火控制系统、空燃比控制系统、怠速控制系统、涡轮增压控制系统、爆震检测与控制系统以及汽油机先进燃烧模式控制这8项关键问题展开论述,并着重介绍了近年来国内外的研究内容和研究成果.最后对车用汽油发动机电子控制系统的发展前景和发展方向进行了展望.     

Neural control of fast nonlinear systems--application to a turbocharged SI engine with VCT.

Today, (engine) downsizing using turbocharging appears as a major way in reducing fuel consumption and pollutant emissions of spark ignition (SI) engines. In this context, an efficient control of the air actuators [throttle, turbo wastegate, and variable camshaft timing (VCT)] is needed for engine torque control. This paper proposes a nonlinear model-based control scheme which combines separate, but coordinated, control modules. Theses modules are based on different control strategies: internal model control (IMC), model predictive control (MPC), and optimal control. It is shown how neural models can be used at different levels and included in the control modules to replace physical models, which are too complex to be online embedded, or to estimate nonmeasured variables. The results obtained from two different test benches show the real-time applicability and good control performance of the proposed methods.     

Neural Control of Fast Nonlinear Systems— Application to a Turbocharged SI Engine With VCT

Today, (engine) downsizing using turbocharging appears as a major way in reducing fuel consumption and pollutant emissions of spark ignition (SI) engines. In this context, an efficient control of the air actuators [throttle, turbo wastegate, and variable camshaft timing (VCT)] is needed for engine torque control. This paper proposes a nonlinear model-based control scheme which combines separate, but coordinated, control modules. Theses modules are based on different control strategies: internal model control (IMC), model predictive control (MPC), and optimal control. It is shown how neural models can be used at different levels and included in the control modules to replace physical models, which are too complex to be online embedded, or to estimate nonmeasured variables. The results obtained from two different test benches show the real-time applicability and good control performance of the proposed methods.     

Robustness assessment and adaptive FDI for car engine

A new on-line fault detection and isolation （FDI） scheme proposed for engines using an adaptive neural network classifier is evaluated for a wide range of operational modes to check the robustness of the scheme in this paper. The neural classifier is adaptive to cope with the significant parameter uncertainty, disturbances, and environment changes. The developed scheme is capable of diagnosing faults in on-line mode and the FDI for the closed-loop system with can be directly implemented in an on-board crankshaft speed feedback is investigated by diagnosis system （hardware）. The robustness of testing it for a wide range of operational modes including robustness against fixed and sinusoidal throttle angle inputs, change in load, change in an engine parameter, and all these changes occurring at the same time. The evaluations are performed using a mean value engine model （MVEM）, which is a widely used benchmark model for engine control system and FDI system design. The simulation results confirm the robustness of the proposed method for various uncertainties and disturbances.     

A model-based gain scheduling approach for controlling the common-rail system for GDI engines

The progressive reduction in vehicle emission requirements have forced the automotive industry to invest in research for developing alternative and more efficient control strategies. All control features and resources are permanently active in an electronic control unit (ECU), ensuring the best performance with respect to emissions, fuel economy, driveability and diagnostics, independently from engine working point. In this article, a considerable step forward has been achieved by the common-rail technology which has made possible to vary the injection pressure over the entire engine speed range. As a consequence, the injection of a fixed amount of fuel is more precise and multiple injections in a combustion cycle can be made. In this article, a novel gain scheduling pressure controller for gasoline direct injection (GDI) engine is designed to stabilise the mean fuel pressure into the rail and to track demanded pressure trajectories. By exploiting a simple control-oriented model describing the mean pressure dynamics in the rail, the control structure turns to be simple enough to be effectively implemented in commercial ECUs. Experimental results in a wide range of operating points confirm the effectiveness of the proposed control method to tame efficiently the mean value pressure dynamics of the plant showing a good accuracy and robustness with respect to unavoidable parameters uncertainties, unmodelled dynamics, and hidden coupling terms.     

Trends and future perspectives of electronic throttle control system in a spark ignition engine

Electronic throttle control (ETC) system has turned into an extremely prominent system with a specific end goal to vary the intake airflow rate to provide a better fuel economy, emissions, drivability and also for integration with other systems in spark ignition engines. ETC system consists of mechatronic device called as electronic throttle body (ETB) which is located in the intake manifold of an engine after the air filter and also has a separate control system in the engine management system (EMS). The throttle angle has to be precisely maintained based on the driver and other system requirements to provide an enhanced throttle response and drivability. However, existence of nonlinearities in the system, such as limp-home position, friction, airflow and aging, affects the position accuracy of the throttle valve. A control system strategy is employed in EMS to handle the other system requirements in throttle opening angle estimation and the nonlinearities in position control. This work features developments within the electronic throttle control system and reviews about the various research work carried in this area. This work will not enforce any new results rather than it will discuss the trends followed in past and also proposes some of the future perspectives in the electronic throttle control process.     

Neural network-based sliding mode control of electronic throttle

A neural network-based sliding mode controller for an electronic throttle of an internal combustion engine is proposed. Electronic throttle is modeled as a linear system with uncertainties and affected by disturbances depending on the states of the system. The disturbances, consisting of an unknown friction and a torque caused by the dual spring mechanism inside the mechanical part of the throttle, are estimated by a neural network whose parameters are adapted on-line. The sliding mode controller and the parameters adaptation scheme are derived in order to achieve a tracking of a smooth reference signal, while preserving boundedness of all signals in the closed-loop system. Experimental results are presented which demonstrate the efficiency and robustness of the proposed control scheme.     

A review on control system architecture of a SI engine management system

Engine management systems (EMS) has become an essential component of a spark ignition (SI) engine in order to achieve high performance; low fuel consumption and low exhaust emissions. An engine management system (EMS) is a mixed-signal embedded system interacting with the engine through number of sensors and actuators. In addition, it includes an engine control algorithm in the control unit. The control strategies in EMS are intended for air-to-fuel ratio control, ignition control, electronic throttle control, idle speed control, etc. Hence, the control system architecture of an EMS consists of many sub-control modules in its structural design to provide an effective output from the engine. Superior output from the engine is attained by the effective design and implementation of the control system in EMS. The design of an engine control system is a very challenging task because of the complexity of the functions involved. This paper consolidates an overview of the vital developments within the SI engine control system strategies and reviews about some of the basic control modules in the engine management system.     

Online linearization-based neural predictive control of air–fuel ratio in SI engines with PID feedback correction scheme

Model predictive control (MPC) frequently uses online identification to overcome model mismatch. However, repeated online identification does not suit the real-time controller, due to its heavy computational burden. This work presents a computationally efficient constrained MPC scheme using nonlinear prediction and online linearization based on neural models for controlling air–fuel ratio of spark ignition engine to its stoichiometric value. The neural model for AFR identification has been trained offline. The model mismatch is taken care of by incorporating a PID feedback correction scheme. Quadratic programming using active set method has been applied for nonlinear optimization. The control scheme has been tested on mean value engine model simulations. It has been shown that neural predictive control with online linearization using PID feedback correction gives satisfactory performance and also adapts to the change in engine systems very quickly.     

基于单片机的双燃料汽化器微机控制系统设计

汽油发动机双燃料汽化器采用乙醇和汽油分开放置的燃料储存方式,避免了乙醇汽油存在的诸多弊端,在此,设计了以AT89S52单片机为核心的双燃料汽化器的微机控制系统,该控制系统实现了双燃料汽化器的按键显示、缺液检测及报警、发动机转速测量及计算、乙醇泵堵转检测及保护、乙醇量喷射控制、液压缸液压杆限位检测及保护等功能,同时,能实时采集汽车发动机的转速信号,通过预置的软件对发动机转速进行分析,精确计算出乙醇单次喷射时间和喷射频率,从而准确控制乙醇与空气、汽油的按配比混合掺烧,提高了汽油的燃烧效率,降低了油耗,减少了有害物质的排放,达到了节能减排的目的;实际应用中,该控制系统运行稳定,抗干扰性能良好,控制准确,具有一定的实际应用价值。     

Engine performance and optimum injection timing for 4-cylinder direct injection hydrogen fueled engine

This paper presents the engine performance and optimum injection timing for 4-cylinder direct injection hydrogen fueled engine. The 4-cylinder direct injection hydrogen engine model was developed utilizing the GT-Power commercial software. This model employed one dimensional gas dynamics to represent the flow and heat transfer in the components of engine model. Sequential pulse injectors are adopted to inject hydrogen gas fuel within the compression stroke. Injection timing was varied from 110° before top dead center (BTDC) until top dead center (TDC) timing. Engine speed was varied from 2000 rpm to 6000 rpm, while the equivalence ratio was varied from 0.2 to 1.0. The validation was performed with the existing previous experimental results. The negative effects of the interaction between ignition timing and injection duration was highlighted and clarified. The results showed that optimum injection timing and engine performance are related strongly to the air fuel ratio and engine speed. The acquired results show that the air fuel ratio and engine speed are strongly influence on the optimum injection timing and engine performance. It can be seen that the indicated efficiency increases with increases of AFR while decreases of engine speed. The power and torque increases with the decreases of AFR and engine speed. The indicated specific fuel consumption (ISFC) decreases with increases of AFR from rich conditions to lean while decreases of engine speed. The injection timing of 60° BTDC was the overall optimum injection timing with a compromise.     

Idle speed control of a F1 racing engine

This paper presents the design and implementation of an idle speed regulator for the Toyota Formula 1 racing car. The control variables are the throttle opening and spark advance, while the controlled variable is the crank shaft speed. First, a set of linear models has been identified from experimental data. Then, a nominal estimated model has been used to synthesize an idle speed regulator with the H₂ approach, and its robustness properties have been tested both in the frequency domain and in simulation. The regulator, already implemented on the engine with fully satisfactory results, has been mounted on the 2003 F1 racing car.     

基于模型的PI鲁棒控制的电子节气门方法研究

精确控制电子节气门对提高汽车动力特性,减少危害气体排放,提高燃油利用效率具有重要意义。针对电子节气门系统的非线性和干扰等不确定特性,设计PI鲁棒控制器,克服非线性等对电子节气门输出响应的影响。该控制器在传统的PID算法中增加非线性控制量,能抑制由于系统非线性环节等干扰引起的性能恶化。根据电子节气门动作信号抽象出系统输入信号,对控制系统进行仿真。仿真结果表明鲁棒PI控制的电子节气门控制系统具有较小的超调量,能快速跟踪输入并具有较小的稳态误差和一定的鲁棒特性。     

增程器用天然气发动机转速双闭环自适应控制

针对增程器用天然气发动机参数不确定,输出扭矩难以精确计算,存在未知干扰,且需要大范围调速等问题,设计发动机转速的双闭环自适应控制策略,并分析系统的稳定性.所提策略的外环为发动机转速环,控制器输出为目标进气压力,内环为进气歧管压力环,控制器输出为节气门开度.该策略结构简单,不需要知道发动机各个参数的具体值,抗干扰性能强,能够满足增程器发动机大范围调速的特点.分别在Matlab/Simulink平台和增程器台架上验证了所提策略的有效性和实用性.     

Reciprocal convex approach to output‐feedback control of uncertain LPV systems with fast‐varying input delay

Robust control of parameter‐dependent input delay linear parameter‐varying (LPV) systems via gain‐scheduled dynamic output‐feedback control is considered in this paper. The controller is designed to provide disturbance rejection in the context of the induced ‐norm or the norm of the closed‐loop system in the presence of uncertainty and disturbances. A reciprocally convex approach is employed to bound the Lyapunov‐Krasovskii functional derivative and extract sufficient conditions for the controller characterization in terms of linear matrix inequalities (LMIs). The approach does not require the rate of the delay to be bounded, hence encompasses a broader family of input‐delay LPV systems with fast‐varying delays. The method is then applied to the air‐fuel ratio (AFR) control in spark ignition (SI) engines where the delay and the plant parameters are functions of the engine speed and mass air flow. The objectives are to track the commanded AFR signal and to optimize the performance of the three‐way catalytic converter (TWC) through the precise AFR control and oxygen level regulation, resulting in improved fuel efficiency and reduced emissions. The designed AFR controller seeks to provide canister purge disturbance rejection over the full operating envelope of the SI engine in the presence of uncertainties. Closed‐loop simulation results are presented to validate the controller performance and robustness while meeting AFR tracking and disturbance rejection requirements.     

Revisiting the benchmark problem of starting control of combustion engines

Starting of combustion engines is a typical transient operating mode that has significant influence to the engine performance. Due to the distinct variations in the pathes of air intake and fuel injection, the model of the engine system contains considerable uncertain parameters. To search effective control schemes that guarantee desired performance, engine starting control is proposed as a benchmark challenge problem. As a challenging result, a model-based control scheme is developed perviously. In this work, the benchmark problem is revisited and a modification for the fuel injection path control of the previous work is proposed by integrating a time sequence regressive based parameter tuning strategy. Validation by the benchmark problem simulator shows that although the new strategy has simple structure, similar control performance is obtained. Especially, the new strategy has potential extensibility with learning based methods to further improve the performance of the benchmark problem on engine starting control.     

Robustness of a discrete-time predictor-based controller for time-varying measurement delay

A predictor-based controller for time-varying delay systems is presented in this paper and its robustness properties for different uncertainties are analyzed. First, a time-varying delay dependent stability condition is expressed in terms of LMIs. Then, uncertainties in the knowledge of all plant-model parameters are considered and the resulting closed-loop system is shown to be robust with respect to these uncertainties. A significant improvement with respect to the same control strategy without predictor is achieved. The scheme is applicable to open-loop unstable plants and it has been tested in a real-time application to control the roll angle of a quad-rotor helicopter prototype. The experimental results show good performance and robustness of the proposed scheme even in the presence of long delay uncertainties.     

Adaptive internal model control with application to fueling control

This paper presents an adaptive internal model controller for stable but not necessarily minimum-phase SISO plants and its application to the air/fuel ratio control system of a spark-ignited engine. The internal model of the controller is formulated in an output-error structure that can be adapted by using standard adaptive laws. The method is applied to an air/fuel ratio control system with a reduced-order internal model and unknown sensor dynamics. Experiments on an engine test bench demonstrate the capability of the adaptive controller to recover the performance and robustness properties of the control system in the case of an aged oxygen sensor.     

Adaptive neural network model based predictive control for air–fuel ratio of SI engines

The dynamics of air manifold and fuel injection of the spark ignition engines are severely nonlinear. This is reflected in nonlinearities of the model parameters in different regions of the operating space. Control of the engines has been investigated using observer-based methods or sliding-mode methods. In this paper, the model predictive control (MPC) based on a neural network model is attempted for air–fuel ratio, in which the model is adapted on-line to cope with nonlinear dynamics and parameter uncertainties. A radial basis function (RBF) network is employed and the recursive least-squares (RLS) algorithm is used for weight updating. Based on the adaptive model, a MPC strategy for controlling air–fuel ratio is realised to a nonlinear simulation of the engines, and its control performance is compared with that of a conventional PI controller. A reduced Hessian method, a new developed sequential quadratic programming (SQP) method for solving nonlinear programming (NLP) problems, is implemented to speed up the nonlinear optimisation in MPC.