基于观测器的发动机转矩跟踪模型预测控制

针对如何实现发动机转矩快速精准地跟踪期望转矩的问题,提出一种基于观测器的模型预测控制策略.首先,利用均值模型对汽油发动机的进气歧管压力动态、转矩和转速动态进行建模,考虑到发动机真实转矩不可测的情况,采用Lyapunov稳定性理论和可测转速信号设计观测器对进气歧管压力进行在线估计,进而获得发动机的实时估计转矩;然后,利用基于观测器的模型预测控制算法设计转矩跟踪控制器,通过C/GMRES数值优化算法在线求解滚动时域优化问题,实现转矩的实时跟踪控制;最后,利用汽油发动机实验台进行实验验证以表明所提出算法的有效性.     

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.     

An adaptive estimator of fuel film dynamics in the intake port of a spark ignition engine

In order to meet the limits imposed on automotive emissions, engine control systems are required to constrain air/fuel ratio (AFR) in a narrow band around the stoichiometric value, due to the strong decay of catalyst efficiency in case of rich or lean mixture. An adaptive estimator, based on an extended Kalman filter, is proposed for the fuel film dynamics in the intake port of a spark ignition engine. The observer is based on a two states mean value model which accounts for the impingement of the injected fuel on the manifold walls and the evaporation process. The observer has been tested on a set of experimental transient maneuvers, showing a good accuracy in predicting the AFR.     

Neural-networks-based nonlinear dynamic modeling for automotive engines

This paper presents a procedure for using neural networks to identify the nonlinear dynamic model of the intake manifold and the throttle body processes in an automotive engine. A dynamic neural network called external recurrent neural network, is used for dynamic mapping and model construction. Dynamic Levenberg–Marquardt algorithm is then applied to the weight-estimation problem. Modeling results indicate that the neural-network-based models have a rather simple structure. Early results also confirm that the neural-network-based modeling of the manifold dynamics can result in a model that is comparable if not better than the first-principle-based models. In addition, it was verified that the neural model has good generalization capabilities.     

车用汽油机进气歧管空气流量智能测量模型

为了确保车用汽油机在运转工况下能获得最佳浓度的混合气要求,利用节气门元器件,基于压差式节流流量测量原理,通过对节气门旋转角度函数链神经网络拟合和节气门的流出系数拟合建立了车用汽油机进气歧管空气流量测量模型。实验验证结果表明:随着进气歧管压力升高,车用汽油机空气质量流量智能测量误差呈降低趋势,且其误差均小于5.0%。     

Pressure-based transient intake manifold temperature reconstruction in Diesel engines

Temperature measurements by the typical thermocouples contain some first-order dynamics with varying time-constants and need to be reconstructed in transient conditions for improving the accuracy of the temperature information. Particularly, for Diesel engine advanced combustion mode control, the accurate acquisitions of the rapidly varying transient temperatures, such as the intake manifold gas temperature, are of importance. In this paper, a temperature reconstruction method, without using additional sensors, is proposed by utilizing the counterpart pressure signal. Through investigating the thermocouple dynamics in terms of the intake manifold pressure and temperature, an intake manifold temperature model was derived. According to this proposed temperature model, the transient temperature reconstruction can be formulated as a thermocouple time-constant estimation problem. Within this framework, an extended Kalman filter (EKF) based method was devised for the parameter estimations. The proposed method was validated through high-fidelity GT-Power engine model simulations as well as experimental results obtained on a multi-cylinder medium-duty Diesel engine.     

Nonlinear Observer-based Control Design and Experimental Validation for Gasoline Engines with Exhaust Gas Recirculation

This paper presents a nonlinear observer-based control design approach for gasoline engines equipped with exhaust gas recirculation (EGR) system. A mean value engine model is designed for control which includes both the intake manifold and exhaust manifold dynamic focused on gas mass flows. Then, the nonlinear feedback controller based on the developed model is designed for the state tracking control, and the stability of the close loop system is guaranteed by a constructed Lyapunov function. Since the exhaust manifold pressure is usually unmeasurable in the production engines, a nonlinear observer-based feedback controller is proposed by using standard sensors equipped on the engine, and the asymptotic stability of the both observer dynamic system and control dynamic system are guaranteed with Lyapunov design assisted by the detail analysis of the model. The experimental validations show that the observer-based nonlinear feedback controller is able to regulate the intake pressure and exhaust pressure state to the desired values during both the steady-state and transient conditions quickly by only using the standard sensors.     

Modeling And Solving An Engine Intake Manifold With Turbo Charger For Predictive Control

In this paper, we build the intake manifold model of an engine with a turbo charger and develop a high speed calculation algorithm for model‐based predictive control in real time. The model is built according to the analysis of its thermodynamic and hydrodynamic characteristics and the sampled experiment data. The model equations are presented as a set of differential equations with condition selection (bifurcation) on the right hand side. The switching surface is divided into two parts, sliding and crossing. The sliding mode on the switching surface is analyzed in detail, and a calculation algorithm is proposed to remove illegal crossing caused by the numerical errors on this surface. Also, the control formula and the condition guiding the bifurcation between these two parts are demonstrated. Using this method, we can solve this model over the entire region of input throttle angles, the stability is greatly increased, and the calculation time is greatly reduced for real time control systems.     

Model-based cold-start speed control scheme for spark ignition engines

This paper presents a model-based control scheme to the cold-start speed control in spark ignition (SI) engines. The multi-variable control algorithm is developed with the purpose of improving the transient performance of the starting engine speed: the control inputs are the fuel injection, the throttle and the spark advance (SA), while the engine speed and the air mass flow rate are the measured signals. The fuel injection is performed with a dual sampling rate system: the cycle-based fuel injection command is individually adjusted for each cylinder by using a TDC (top dead center)-based air charge estimation. The desired performance for speed regulation is achieved by using a coordinated control of SA and throttle operation. The speed error convergence of the closed loop system is proved for simplified, second-order model with a time-delay, and the robustness with respect to parameter uncertainties is investigated. The performance and the robustness with respect to modeling uncertainties of the proposed control scheme are tested using an industrial engine simulator with six cylinders.     

柴油机油量执行器的干扰估计滑模控制方法

油量执行器是电控分配泵的核心部件之一,其直接控制着柴油发动机的燃油喷射量.模型非线性与外部扰动是油量执行器系统中不可避免的影响因素,前期的许多研究忽略了这些非线性,使得闭环系统性能并不理想.本文考虑了旋转电磁铁和复位弹簧等非线性特性的建模,得到了油量执行器系统的数学模型.进而,在基于模型对系统非线性进行抵消之后引入扩张状态观测器对系统外部干扰和不确定性进行估计,设计了基于扩张状态观测器的滑模控制律.该控制律在保证鲁棒性的同时,可以使得切换增益幅值更小,有利于减小滑模控制的抖振问题.最后,通过MATLAB/Simulink仿真和dSPACE平台实验验证了所提方法的可行性和有效性.     

Estimation and feedback control of air-fuel ratio for gasoline engines

In 4-stroke internal combustion engines, air-fuel ratio control is a challenging task due to the rapid changes of engine throttle,especially during transient operation. To improve the transient performance, managing the cycle-to-cycle transient behavior of the mass of the air, the fuel and the burnt gas is a key issue due to the imbalance of cyclic combustion process. This paper address the model-based estimation and control problem for cyclic air-fuel ratio of spark-ignition engines. A discrete-time model of air-fuel ratio is proposed, which represents the cycle-to-cycle transient behavior of in-cylinder state variables under the assumptions of cyclic measurability of the total in-cylinder charge mass, combustion efficiency and the residual gas fraction. With the model,a Kalman filter-based air-fuel ratio estimation algorithm is proposed that enable us to perform a feedback control of air-fuel ratio without using lambda sensor. Finally, experimental validation result is demonstrated to show the effectiveness of proposed estimation and control scheme that is conducted on a full-scaled gasoline engine test bench.     

Sliding mode observers for a class of linear parameter varying systems

In this paper, a new framework for the synthesis of a class of sliding mode observers for affine linear parameter varying (LPV) systems is proposed. The sliding mode observer is synthesized by selecting the design freedom via linear matrix inequalities ( LMIs ). Posing the problem from a small gain perspective allows existing numerical techniques from the literature to be used for the purpose of synthesizing the observer gains. In particular, the framework allows affine parameter‐dependent Lyapunov functions to be considered for analyzing the stability of the state estimation error dynamics, to help reduce design conservatism. Initially a variable structure observer formulation is proposed, but by imposing further constraints on the LMIs, a stable sliding mode is introduced, which can force and maintain the output estimation error to be zero in finite time. The efficacy of the scheme is demonstrated using an LPV model of the short period dynamics of an aircraft and demonstrates simultaneous asymptotic estimation of the states and disturbances.     

基于全可变气门汽油机进气量测量和控制问题研究

基于全可变气门运动机构的气门参数控制燃烧技术是改善传统SI汽油机燃油经济性,提高动力性和降低排放的最为有效的途径之。但是这也对发动机进气量的测量和控制提出了更高的要求。本文通过仿真和试验相结合的方法,对基于全可变气门机构汽油机进气量测量和控制问题进行了探讨和研究。     

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.     

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.     

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.     

Combustion timing control of HCCI engine based on NNPC and Elman-NN model under complex conditions

Homogeneous Charge Compression Ignition (HCCI) combines the characteristics of gasoline engine and diesel engine with high thermal efficiency and low emissions. However, since there is no direct initiator of combustion, it is difficult to control the combustion timing in HCCI engines under complex working conditions. In this paper, Neural Network Predictive Control (NNPC) for combustion timing of the HCCI engine is designed and implemented. First, the black box model based on Elman neural network is designed and developed to estimate the combustion timing. The fuel equivalence ratio, intake valve closing timing, intake manifold temperature, intake manifold gas pressure, and engine speed are chosen as the system inputs. Then, a NNPC controller is designed to control combustion timing by controlling the intake valve closing timing. Simulation results show that the Elman neural network black box model is capable of estimating the HCCI engine combustion timing. In addition, regardless of whether the HCCI engine is in constant or complex condition, the designed NNPC controller is capable of keeping the combustion timing within the ideal range. In particular, under New European Driving Cycle (NEDC) working conditions, the maximum overshoot of the controller is 28.95% and the average error is 1.03 crank angle degree. It is concluded that the controller has good adaptability and robustness.     

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.     

一种基于滑模—神经网络观测器的故障检测和诊断方法

本文针对一类非线性系统，提出了一种用于故障检测和诊断的滑模观测器方法．其 中，观测器中的滑模项保证了该系统在无故障情况时的鲁棒性，并且系统运行的滑动区域提供了故障检测的条件．当检测出故障之后，观测器中的故障估计部分被启动，利用RBF神经网络估计故障，从而能在线辨识故障的形态．仿真结果验证了该方法的有效性．     

An adaptive sliding mode observer for lithium-ion battery state of charge and state of health estimation in electric vehicles

As the demand for electric vehicle (EV)'s remaining operation range and power supply life, Lithium-ion (Li-ion) battery state of charge (SOC) and state of health (SOH) estimation are important in battery management system (BMS). In this paper, a proposed adaptive observer based on sliding mode method is used to estimate SOC and SOH of the Li-ion battery. An equivalent circuit model with two resistor and capacitor (RC) networks is established, and the model equations in specific structure with uncertainties are given and analyzed. The proposed adaptive sliding mode observer is applied to estimate SOC and SOH based on the established battery model with uncertainties, and it can avoid the chattering effects and improve the estimation performance. The experiment and simulation estimation results show that the proposed adaptive sliding mode observer has good performance and robustness on battery SOC and SOH estimation.