废气再循环–可变几何截面涡轮增压柴油发动机 燃气双环系统建模及协同控制

针对废气再循环(EGR)与可变几何截面涡轮增压(VGT)的柴油发动机,作者联合考虑其燃油动力转速调节回路与气体回路,提出了内外双环稳定动态反馈的控制策略.其中,内环回路是利用Lyapunov函数设计的控制器,控制燃油质量流量来跟踪柴油发动机转速;外环回路则设计EGR–VGT控制器,跟踪气体回路的进排气歧管压力及压气机空气质量流量,并克服了柴油发动机建模中的不稳定零动态问题.同时,研究了气体流量与EGR和VGT阀门开度之间的关系,通过设计流量开度转换模块实现了两者控制的转换.最后,通过专业发动机软件AMESim与仿真软件MATLAB/Simulink的联合仿真试验,验证了该控制策略对柴油发动机燃油动力转速调节与气体回路控制的有效性.     

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.     

Quantitative feedback design of air and boost pressure control system for turbocharged diesel engines

For modern diesel engines, variable geometry turbocharger (VGT) is used to boost engine power output. In addition, exhaust gas recirculation (EGR) is utilized to reduce engine out NO_x emission. To realize these functions, a multivariable control system needs to control both VGT and EGR valve to deliver desired intake manifold (or boost) pressure, and desired EGR flow rate. This two-input and two-output system is nonlinear with cross-couplings between the boost and EGR responses to the input actuators, the system parameters are varying with different engine operating conditions. This paper proposes a closed loop design of a multivariable VGT/EGR control system for a turbocharged diesel engine. The control system is synthesized based on quantitative feedback theory to maintain robust stability and performance via sequential MIMO loop shaping in the frequency domain. Experiment results are included from a turbocharged diesel engine to show the effectiveness of the proposed control design.     

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.     

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.     

Dynamic control of a SI engine with variable intake valve timing

Engines equipped with a means to actuate air flow at the intake valve can achieve superior fuel economy performance in steady state. This paper shows how modern nonlinear design techniques can be used to control such an engine over a wide range of dynamic conditions. The problem is challenging due to the nonlinearities and delays inherent in the engine model, and the constraint on the air flow actuator. The controller is designed on the basis of a mean‐value model, which is derived from a detailed intake stroke model. The control solution has two novel features. Firstly, a recovery scheme for integrator wind‐up due to input constraints is directly integrated into the nonlinear control design. The second novel feature is that the control Lyapunov function methodology is applied to a discrete‐time model. The performance of the controller is evaluated and compared with a conventionally controlled engine through simulation on the detailed engine model. Copyright © 2003 John Wiley & Sons, Ltd.     

Guaranteed Cost Synchronization of Complex Network Systems with Delay

The guaranteed cost synchronization control problem of some general complex dynamical networks with time delay is investigated. A dynamic feedback controller is designed for the system guaranteed cost synchronization. Meanwhile, due to many nodes in the complex networks and the complex of the direct control, use of the the pinning control to make the system achieve guaranteed cost synchronization is also investigated. Based on the Lyapunov stability theory and the matrix inequality, the sufficient conditions are obtained for the existence of the guaranteed cost controller with time delay in complex network. The dynamic feedback controller is designed to ensure the asymptotic stability conditions of the system and make the performance index of the system meet certain requirements. Finally, the feasibility of the proposed method is demonstrated by numerical examples.     

Two-degree-of-freedom H-infinity control of combustion in diesel engine using a discrete dynamics model

This paper proposes an H-infinity combustion control method for diesel engines. The plant model is the discrete dynamics model developed by Yasuda et al., which is implementable on a real engine control unit. We introduce a two-degree-of-freedom control scheme with a feedback controller and a feedforward controller. This scheme achieves both good feedback properties, such as disturbance suppression and robust stability, and a good transient response. The feedforward controller is designed by taking the inverse of the static plant model, and the feedback controller is designed by the H-infinity control method, which reduces the effect of the trubocharger lag. The effectiveness of the proposed method is evaluated in simulations using the nonlinear discrete dynamics model.     

Design of a stable fuzzy controller for an articulated vehicle

This paper presents a backward movement control of an articulated vehicle via a model-based fuzzy control technique. A nonlinear dynamic model of the articulated vehicle is represented by a Takagi-Sugeno fuzzy model. The concept of parallel distributed compensation is employed to design a fuzzy controller from the Takagi-Sugeno fuzzy model of the articulated vehicle. Stability of the designed fuzzy control system is guaranteed via Lyapunov approach. The stability conditions are characterized in terms of linear matrix inequalities since the stability analysis is reduced to a problem of finding a common Lyapunov function for a set of Lyapunov inequalities. Simulation results and experimental results show that the designed fuzzy controller effectively achieves the backward movement control of the articulated vehicle.     

RCMAC-based adaptive control for uncertain nonlinear systems.

An adaptive control system, using a recurrent cerebellar model articulation controller (RCMAC) and based on a sliding mode technique, is developed for uncertain nonlinear systems. The proposed dynamic structure of RCMAC has superior capability to the conventional static cerebellar model articulation controller in an efficient learning mechanism and dynamic response. Temporal relations are embedded in RCMAC by adding feedback connections in the association memory space so that the RCMAC provides a dynamical structure. The proposed control system consists of an adaptive RCMAC and a compensated controller. The adaptive RCMAC is used to mimic an ideal sliding mode controller, and the compensated controller is designed to compensate for the approximation error between the ideal sliding mode controller and the adaptive RCMAC. The online adaptive laws of the control system are derived based on the Lyapunov stability theorem, so that the stability of the system can be guaranteed. In addition, in order to relax the requirement of the approximation error bound, an estimation law is derived to estimate the error bound. Finally, the simulation and experimental studies demonstrate the effectiveness of the proposed control scheme for the nonlinear systems with unknown dynamic functions.     

变时滞复杂网络同步牵制保性能控制器设计

针对具有可变时滞的复杂网络系统,研究此类系统的同步保性能控制问题。为了使系统同步保性能,设计了一种动态反馈控制器;同时,由于复杂网络的节点较多,考虑到直接控制比较复杂,所以又研究了利用牵制控制使系统达到同步保性能控制的方法。主要运用Lyapunov稳定性理论并结合矩阵不等式处理方法,得出了具有可变时滞性复杂网络系统同步保性能控制器存在的充分条件。所设计的动态反馈控制器在保证系统的渐近稳定条件下使系统的性能指标满足一定的要求。最后给出两个数值仿真说明其有效性。     

基于观测器的广义时滞系统弹性保成本控制

在同时考虑系统矩阵参数不确定性和控制器不确定性对系统性能影响的前提下,研究了一类基于观测器的不确定广义时滞系统的弹性保成本控制问题.基于Lvapunov稳定性理论,通过构造广义Lyapunov函数和广义二次性能指标函数,以线性矩阵不等式的形式给出了基于观测器状态反馈的弹性保成本控制器的设计方法.该控制器不仅保证了广义时滞系统是鲁棒稳定而且使其具有相应的性能指标上界.采用一种新方法将系统弹性保成本控制器设计和状态观测器增益矩阵求取转化为两组严格线性矩阵不等式的可行解问题.最后通过算例验证了该方法的可行性和有效性.     

Neural networks for real‐time nonlinear control of a variable geometry turbocharged diesel engine

New engines are submitted to emission standards that are becoming more and more restrictive. Diesel engines are typically equipped with variable geometry turbo‐compressor, exhaust gas recirculation system, high‐pressure common rail system and post‐treatment devices in order to meet these legislative requirements. Consequently, the control of diesel engines becomes a very difficult task involving five to 10 control variables that interact with each other and that are highly nonlinear. Until the present day, the control schemes integrated in the engine's controller are all based on static maps identified by steady‐state engine mapping. Afterward, these schemes are adjusted and calibrated in the vehicle using various control techniques in order to assure a better dynamic response of the engine under dynamic load. In this paper, we are interested in developing a mathematical optimization process that searches for the optimal control scheme under static and dynamic operating conditions. Firstly, we suggest modeling the engine and its emissions using mean value models which require limited experiments and are in good agreement with the experimental data. These models are then used in a dynamic optimization process based on the Broyden–Fletcher–Goldfarb–Shanno algorithm in order to find the optimal control scheme of the engine. The results show a reduction of the engine emissions without deteriorating its performance. Finally, we propose a practical control technique based on neural networks in order to apply these control schemes online to the engine. The results are promising. Copyright © 2007 John Wiley & Sons, Ltd.     

Fixed-time sliding mode observer-based controller for a class of uncertain nonlinear double integrator systems

This paper investigates a fixed-time convergence issue using the sliding mode observer-based controller for a class of uncertain nonlinear double integrator systems. This observer-based controller is designed assuming that only the first state measurement is available and there is no information about external disturbances and modeling uncertainties. A new form of sliding mode observer in combination with a sliding mode controller is designed to estimate unmeasured state and unknown disturbances and uncertainties as well as provide the estimated data in the control law. A novel form of sliding surfaces for the robust observer-based controller is proposed for which fixed-time convergence is guaranteed to achieve trajectory tracking. In the proposed fixed-time scheme, the bound on the settling time is user-defined using design parameters regardless of the system's initial conditions. The control law and observer law are designed such that the chattering issue is alleviated in the control signal. The stability analysis of the closed-loop system using the observer-based controller is established via the Lyapunov theory. The validity of the controller design is tested by applying and simulating an example of a robot manipulator in Simulink/MATLAB. The superiority of the proposed method is demonstrated by comparing it with two other methods from the relevant literature.     

冷带轧机厚控系统自适应鲁棒输出反馈动态控制器设计

冷带轧机厚控系统可被认为是一个受外界干扰的线性不确定时滞系统.本文首先设计了标称系统下的鲁棒输出反馈动态控制器,以改善闭环系统的动静态性能;其次,在系统不需要满足不确定性匹配条件的情况下,将参数和外部扰动不确定性综合考虑.应用Lyapunov稳定性理论设计了系统不确定性上界参数的自适应估计器和系统的自适应控制器,保证了闭环系统的渐近稳定性,减小了设计的保守性;两者结合实现了板带出口厚度的有效控制.最后通过一个仿真实例说明本文所提出的自适应鲁棒控制器的有效性.     

Robust fuzzy neural network control for linear ceramic motor drivevia backstepping design technique

This study presents a robust fuzzy-neural-network (RFNN) control system for a linear ceramic motor (LCM) that is driven by an unipolar switching full-bridge voltage source inverter using LC resonant technique. The structure and operating principle of the LCM are introduced. Since the dynamic characteristics and motor parameters of the LCM are nonlinear and time varying, a RFNN control system is designed based on the hypothetical dynamic model to achieve high-precision position control via the backstepping design technique. In the RFNN control system a fuzzy neural network (FNN) controller is used to learn an ideal feedback linearization control law, and a robust controller is designed to compensate the shortcoming of the FNN controller. All adaptive learning algorithms in the RFNN control system are derived from the sense of Lyapunov stability analysis, so that system-tracking stability can be guaranteed in the closed-loop system. The effectiveness of the proposed RFNN control system is verified by experimental results in the presence of uncertainties. In addition, the advantages of the proposed control system are indicated in comparison with the traditional integral-proportional (IP) position control system     

基于观测器的不确定广义时滞系统保成本控制

讨论了基于观测器的一类不确定广义时滞系统的保成本控制问题,给出了系统保成本控制器的设计方法.控制器的构造使得闭环系统是鲁棒稳定的.利用线性矩阵不等式以及Lyapunov函数方法,给出了鲁棒保成本控制器存在的充分条件以及相应的可保成本指标,并证明了这个条件等价于一组线性矩阵不等式(LM Is)的可解性问题,同时推广了已知的相关结果.最后以算例验证了设计方法的有效性.     

涡轮增压汽油机气路预测模型的建立与预测控制

针对涡轮增压汽油机气路系统中节气门与废气旁通阀动力学耦合、机理建模复杂的问题, 本文提出基于神经网 络模型的气路系统预测控制方法,实现了节气门与废气旁通阀的协调控制. 首先, 针对涡轮增压汽油机气路系统map与 机理混合描述的特性, 利用系统的输入输出数据,采用反向传播神经网络(back propagation neural network, BPNN)训 练得到一个非线性气路模型; 其次, 基于泰勒展开式对预测模型进行线性化, 并对模型的精度进行了验证,进而利用该 模型预测系统的未来动态; 然后, 在考虑系统存在输入约束的条件下, 设计了一个线性模型预测控制器对节气门与废气 旁通阀进行协调控制, 实现了进气歧管压力和升压的跟踪控制进而满足发动机的扭矩需求; 最后, 通过离线仿真和基 于dSPACE的快速原型实验(rapid control prototyping, RCP)验证了控制系统的有效性和实时性.     

Adaptive output control of a class of uncertain chaotic systems

In this paper, a new observer-based backstepping output control scheme is proposed for stabilizing and controlling a class of uncertain chaotic systems. The controller is designed through the use of a robust observer and backstepping technique. We firstly show that many chaotic systems as paradigms in the research of chaos can be transformed into a class of nonlinear systems in the feedback form. Secondly, the synchronization problem is converted to the tracking problem from control theory, thereby leading to the use of state observer design techniques. A new observer is utilized to estimate the unmeasured states. Unlike some existing methods for chaos control, no priori knowledge on the system parameters is required and only the output signal is available for control purpose. The Lyapunov functions are quadratic in the state estimates, the observer errors and the parameter estimation error based on the backstepping technique. It is shown that not only global stability is guaranteed by the proposed controller, but also both transient and asymptotic tracking performances are quantified as explicit functions of the design parameters so that designers can tune the design parameters in an explicit way to obtain the desired closed-loop behavior.