Simple adaptive air-fuel ratio control of a port injection SI engine with a cylinder pressure sensor

The problem of air-fuel ratio(AFR) control of the port injection spark ignition(SI) engine is still of considerable importance because of stringent demands on emission control. In this paper, the static AFR calculation model based on in-cylinder pressure data and on the adaptive AFR control strategy is presented. The model utilises the intake manifold pressure, engine speed, total heat release, and the rapid burn angle, as input variables for the AFR computation. The combustion parameters, total heat release,and rapid burn angle, are calculated from in-cylinder pressure data. This proposed AFR model can be applied to the virtual lambda sensor for the feedback control system. In practical applications, simple adaptive control(SAC) is applied in conjunction with the AFR model for port-injected fuel control. The experimental results show that the proposed model can estimate the AFR, and the accuracy of the estimated value is applicable to the feedback control system. Additionally, the adaptive controller with the AFR model can be applied to regulate the AFR of the port injection SI engine.     

Robust air/fuel ratio control with adaptive DRNN model and AD tuning

Current production engines use look-up table and proportional and integral (PI) feedback control to regulate air/fuel ratio (AFR), which is time-consuming for calibration and is not robust to engine parameter uncertainty and time varying dynamics. This paper investigates engine modelling with the diagonal recurrent neural network (DRNN) and such a model-based predictive control for AFR. The DRNN model is made adaptive on-line to deal with engine time varying dynamics, so that the robustness in control performance is greatly enhanced. The developed strategy is evaluated on a well-known engine benchmark, a simulated mean value engine model (MVEM). The simulation results are also compared with the PI control.     

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.     

Reconstruction of cylinder pressure for SI engine using recurrent neural network

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.     

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.     

A procedure to enhance identification of recurrent neural networks for simulating air–fuel ratio dynamics in SI engines

The paper deals with the identification of recurrent neural networks (RNNs) for simulating the air–fuel ratio (AFR) dynamics into the intake manifold of a spark ignition (SI) engine. RNN are derived from the well-established static multi layer perceptron feedforward neural networks (MLPFF), that have been largely adopted for steady-state mapping of SI engines. The main contribution of this work is the development of a procedure that allows identifying a RNN-based AFR simulator with high generalization and limited training data set. The procedure has been tested by comparing RNN simulations with AFR transients generated using a nonlinear-dynamic engine model. The results show how training the network making use of inputs that are uncorrelated and distributed over the entire engine operating domain allows improving model generalization and reducing the experimental burden.Potential areas of application of the procedure developed can be either the use of RNN as virtual AFR sensors (e.g. engine or individual AFR prediction) or the implementation of RNN in the framework of model-based control architectures.     

Closed-loop individual cylinder air–fuel ratio control via UEGO signal spectral analysis

The paper presents the development and real time application of an original closed-loop individual cylinder AFR control system, based on a spectral analysis of the lambda sensor signal. The observation that any type of AFR disparity between the various cylinders is reflected in a specific harmonic content of the AFR signal spectrum, represents the starting point of the project. The results observed on a 4 cylinder Spark Ignition engine are encouraging, since in the investigated engine operating conditions the controller is able to reduce AFR inequality below 0.01 lambda. The paper also shows how the proposed controller can be applied to other engine configurations.     

多点电喷气体发动机空燃比闭环控制系统设计

空燃比控制是发动机性能实现中最重要的控制之一。基于玉柴某大型六缸单点气体发动机改多点电喷的基础上进行研究,在开放式ECU基础上针对燃气发动机瞬态变化过程中的反馈时间延迟,构建了一种基于前馈PID算法的空燃比闭环控制策略,用来预判和补偿空燃比超调和反馈时间延迟;解决了发动机瞬变工况下空气与燃气的精确匹配问题。通过对台架模型和发动机试验的数据分析,结果表明基于前馈PID控制算法的空燃比闭环控制策略能够进一步提高燃气发动机的排放效率和动力性。     

基于系统辨识的航空发动机稳态燃油自抗扰控制

自抗扰控制技术应用于航空发动机稳态燃油控制存在两个难点：发动机中的高频不确定动态导致扩张状态观测器(Extended State Observer,ESO)增益过高和名义控制系数整定困难。针对此现状,提出一种基于系统辨识的航空发动机稳态燃油自抗扰控制器。首先,使用经典Gram-Schmidt(Classical Gram-Schmidt,CGS)算法对控制系数和发动机未知动态进行辨识,将辨识信息加入ESO中设计改进ESO (Improved ESO,IESO),从而使总扰动中包含较少的高频动态,降低观测器增益。其次,基于IESO设计航空发动机稳态燃油自抗扰控制器,并根据辨识结果快速整定名义控制系数。最后,分析IESO观测误差的收敛性和闭环系统的稳定性。仿真结果表明,所提方法可以快速整定名义控制系数,有效降低观测器增益,进而提高系统的鲁棒性。     

Modeling and online parameter estimation of intake manifold in gasoline engines using sliding mode observer

Model based control of automotive engines for fuel economy and pollution minimization depends on accuracy of models used. A number of mathematical models of automotive engine processes are available for this purpose but critical model parameters are difficult to obtain and generalize. This paper presents a novel method of online estimation of discharge coefficient of throttle body at the intake manifold of gasoline engines. The discharge coefficient is taken to be a varying parameter. Air mass flow across the throttle body is a critical variable in maintaining a closer to stoichiometric air fuel ratio; which is necessary to minimize the pollution contents in exhaust gases. The estimation method is based on sliding mode technique. A classical first Sliding mode observer is designed to estimate intake manifold pressure and the model uncertainty arising from the uncertain and time varying discharge coefficient is compensated by the discontinuity/switching signal of sliding mode observer. This discontinuity is used to compute coefficient of discharge as a time varying signal. The discharge coefficient is used to tune/correct the intake manifold model to engine measurements. The resulting model shows a very good agreement with engine measurements in steady as well s transient state. The stability of the observer is shown by Lyapunov direct method and the validity of the online estimation is successfully demonstrated by experimental results. OBD-II (On Board Diagnostic revision II) based sensor data acquisition from the ECU (Electronic Control Unit) of a production model vehicle is used. The devised algorithm is simple enough to be designed and implemented in a production environment. The online estimation of parameter can also be used for engine fault diagnosis work.     

基于神经网络模型的空燃比非线性模型预测控制

采用基于径向基神经网络（RBFNN）模型的非线性模型预测控制方法,被控对象选择火花塞点火（SI）发动机的空燃比（AFR）高度非线性复杂系统,利用渐消记忆最小二乘法实现基于RBFNN的SI发动机AFR系统建模以及参数在线自适应更新。针对非线性模型预测控制中寻优问题,运用序列二次规划滤子算法对最优控制序列进行求解,并加入滤子技术避免了罚函数的使用。在相同的实验环境下,与PI控制算法和Volterra模型预测控制方法进行仿真对比实验,结果表明,所提算法的控制效果明显优于其他两种方法。     

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.     

Fuzzy Sliding‐mode Strategy for Air–fuel Ratio Control of Lean‐burn Spark Ignition Engines

Minimization of emissions of carbon dioxide and harmful pollutants and maximization of fuel economy for lean‐burn spark ignition (SI) engines relies to a large extent on precise air–fuel ratio (AFR) control. However, the main challenge of AFR control is the large time‐varying delay in lean‐burn engines. Since the system is usually subject to external disturbances and uncertainties, a high level of robustness in AFR control design must be considered. We propose a fuzzy sliding‐mode control (FSMC) to track the desired AFR in the presence of periodic disturbances. The proposed method is model free and does not need any system characteristics. Based on the fuzzy system input–output data, two scaling factors are first employed to normalize the sliding surface and its derivative. According to the concept of the if‐then rule, an appropriate rule table for the logic system is designed. Then, based on Lyapunov stability criteria, the output scaling factor is determined such that the closed‐loop stability of the internal dynamics with uniformly ultimately bounded (UUB) performance is guaranteed. Finally, the feasibility and effectiveness of the proposed control scheme are evaluated under various operating conditions. The baseline controllers, namely, a PI controller with Smith predictor and sliding‐mode controller, are also used to compare with the proposed FSMC. It is shown that the proposed FSMC has superior regulation performance compared to the baseline controllers.     

天然气发动机空燃比智能控制策略的研究

该文详细介绍了基于神经网络的控制器在天然气发动机空燃比中的控制实现，并用Matlab进行了仿真，仿真结果表明，其控制效果优于基于PID的控制器，所研究的基于单神经元控制器的学习算法在工程上易于实现，在发动机空燃比控制的工程应用中有很好的前景。     

基于扩张状态观测器的发动机转速双闭环自适应控制系统设计

为较好控制发动机设备的实时转速水平,使其在不同压力水平下均能呈现理想化工作状态,设计基于扩张状态观测器的发动机转速双闭环自适应控制系统。根据双闭环电路的集成形式,连接发动机控制器与自适应传感器,利用隔离驱动芯片,更改观测器驱动管的作用频率周期,完成自适应控制系统的硬件执行环境搭建。在此基础上,深入分析扩张状态观测器的内部结构,以微分跟踪器作为切入点,选取关键的ESO参数,再借助扰动补偿向量,完成对扩张状态观测器的频域参数配置,联合相关硬件应用设备,实现基于扩张状态观测器的发动机转速双闭环自适应控制系统设计。实验结果表明,在扩张状态观测器作用下,发动机元件在不同压力水平下的实时转速水平均能得到有效控制,可使发动机设备保持较长时间的稳定工作状态。     

Two air path observers for turbocharged SI engines with VCT

Nowadays, downsizing is a major way to reduce fuel consumption and pollutant emissions of spark ignition (SI) engines. In downsized engines, new air path management systems such as turbocharging or variable camshaft timing (VCT) are included, and an efficient control of the air actuators is required for engine torque control. Two non-linear estimators are proposed to estimate non-measured variables of the air path. The first one is an in-cylinder air mass observer that combines feedforward neural static models and a linear parameter varying (LPV) polytopic observer. The second one is a neural estimator of the burned gas and scavenged air masses. Test bench results on a turbocharged SI engine with VCT show the real time applicability and good performance of the proposed estimators. Finally, a strategy for developing the engine supervisor is presented.     

A control design and calibration reduction methodology for AFR control in gasoline engines

A control architecture for air to fuel ratio (AFR) control of gasoline engines designed to work with switching and/or wide range oxygen sensors, with the goal of minimizing calibration effort while meeting performance requirements, is described. A high bandwidth, dithered inner-loop reference tracking controller with pre-catalyst oxygen sensor feedback coupled with a low bandwidth setpoint tracking outer-loop with post catalyst oxygen sensor feedback, is used to control engine exhaust and O₂ storage in the three-way catalyst (TWC), respectively. A total synthesis inspired design ensures that significant non-linearity in the system is handled through a coordinated and corrective action and expected response blocks in the open-loop, without burdening the closed loop controller. Calibration is achieved offline, through closed loop optimization using genetic algorithms, while simultaneously meeting performance and stability criteria with significantly reduced need for in-vehicle tuning. Experimental results show comparable emissions performance with the stock OEM AFR controller under warmed up conditions over a standard drive cycle.     

基于CVT的混合动力汽车建模与仿真

建立了基于无级变速器 (Continously Variable Transmission,CVT) 的前向并联式混合动力电动汽车动力系统模型,为了研究整车动力性、经济性,根据行驶动力学方程,采用极值原理和曲面拟合法对发动机台架试验得到的数据进行了多项式拟合,建立了发动机万有特性与最佳操作曲线(Optimal Operating Line,OOL) 模型,并建立了牵引用三相感应电机动力模型以及牵引蓄电池(State of Charge,SOC)模型.同时,提出了燃油消耗最低、蓄电池充放电平衡的能量分配控制策略,进行整车动力性仿真计算,仿真结果表明在保证循环结束电池充放电基本平衡的同时发动机燃油消耗最低,仿真试验对比结果验证了建立的模型的精确性.     

A neural network model-based observer for idle speed control of ignition in SI engine

The paper presents an algorithm of idle speed stabilization in the spark ignition automotive engine by means of spark advance control. The algorithm is based on a well-known approach of a model-based adaptive control and uses artificial neural networks. The control algorithm is based on a neural network model observer of the additional effective torque. The additional load is estimated as difference between effective torque, estimated by the neural network observer, and brake torque, estimated on the basis of a linear quadratic model. In that case the additional load is understood as the sum of the alternator brake torque (additional load form electric car equipments) and the momentary and/or permanent changes of the engine’s characteristics.On the basis of estimated values of the additional load, the required value of angular acceleration is determined to make the engine return to the specified speed. This acceleration is achieved by adjusting the spark advance. The required value of spark advance is estimated by means of a neural network model converse to that of the effective torque.The algorithm was experimentally compared with PID and adaptive algorithms in the same test bed. The tests were conducted under sudden change of external load. The proposed algorithm proved to be more effective in terms of control error.     

A parameter-varying filtered PID strategy for air–fuel ratio control of spark ignition engines

In this paper, a new synthesis method is presented to control air–fuel ratio (AFR) in spark ignition engines to maximize the fuel economy while minimizing exhaust emissions. The major challenge in the control of AFR is the time-varying delay in the control loop which restricts the application of conventional control techniques. In this paper, the time-varying delay in the system dynamics is first approximated by Padé approximation to render the system dynamics into non-minimum phase characteristics with time-varying parameters. Application of parameter-varying dynamic compensators is invoked to retrieve unstable internal dynamics. The associated error dynamics is then utilized to construct a filtered PID controller combined with a parameter-varying dynamic compensator to track the desired AFR command using the feedback from the universal exhaust gas oxygen sensor. The proposed method achieves desired dynamic properties independent of the matched disturbances. It also accommodates the unmatched perturbations due to the dynamic compensator features. The results of applying the proposed method to experimental numerical data demonstrate the closed-loop system stability and performance against time-varying delay, canister purge disturbances and measurement noise for both port fuel injection engines and lean-burn engines.