An approach to multivariable combustion control design

In this paper, an approach to multivariable combustion control design within the Individual Channel Design (ICD) framework for analysis and control design is presented. ICD is a framework which involves an interplay between customer specification, uncertain plant characteristics, and the multivariable feed-back design itself. Established multivariable methods and process engineering knowledge can be incorporated or evaluated within the ICD framework. The combustion control has been designed and evaluated with a computer simulation of both a linearized model and a nonlinear model of the closed-loop system. The ICD multivariable framework shows in a highly transparent manner, by way of simple graphical frequency response indicators, what the main possibilities and difficulties posed by a combustion process for multivariable control are, and how much trade-off between control specifications is possible. Solutions are also presented for problems such as: integrity of closed-loop control, balance of input-output channels, simple and transparent controller structure, and robustness.     

Nonlinear model predictive control of an internal combustion engine exposed to measured disturbances

This work presents the design procedure of a speed controller for a large, lean burn, natural gas engine in island mode operation. This is a disturbance rejection problem with a measured, large disturbance. The core element is a nonlinear model predictive control (NMPC) algorithm that serves as outer loop controller in a cascaded control structure and generates set-points for low level control loops. The NMPC relies on a control oriented model that includes the physics based equations, assumptions on underlying control loops and constraints given by the control requirements. It is shown how to design the running cost such that the stability of the NMPC without terminal cost and constraints can be guaranteed for the nominal system and for the perturbed system exposed to parametric uncertainties and un-modeled dynamics. The functionality of the control strategy is demonstrated in simulation and by experimental results derived at the engine-testbed.     

Nonlinear internal model controller design for wastegate control of a turbocharged gasoline engine

Internal Model Control (IMC) has a great appeal for automotive powertrain control in reducing the control design and calibration effort. Motivated by its success in several automotive applications, this work investigates the design of nonlinear IMC for wastegate control of a turbocharged gasoline engine. The IMC design for linear time-invariant (LTI) systems is extended to nonlinear systems. To leverage the available tools for LTI IMC design, the quasi-linear parameter-varying (quasi-LPV) models are explored. IMC design through transfer function inverse of the quasi-LPV model is ruled out due to parameter variability. A new approach for nonlinear inversion, referred to as the structured quasi-LPV model inverse, is developed and validated. A fourth-order nonlinear model which sufficiently describes the dynamic behavior of the turbocharged engine is used as the design model in the IMC structure. The controller based on structured quasi-LPV model inverse is designed to achieve boost-pressure tracking. Finally, simulations on a validated high-fidelity model are carried out to show the feasibility of the proposed IMC. Its closed-loop performances are compared with a well-tuned PI controller with extensive feedforward and anti-windup built in. Robustness of the nonlinear IMC design is analyzed using simulations.     

应用椭圆齿轮流量计的内燃机油耗测量系统

介绍一种用于测量内燃机耗油的流量计,以实现对动力机械的燃油控制,达到节能减排的目的.采用椭圆齿轮的测量形式,开发了二次仪表.利用一台喷油泵试验台进行了系统性能验证实验,根据内燃机的实际工作状况,在内燃机三种工况下进行了实验,即最大扭矩转速、最大功率转速以及起动转速.实验结果表明,该测量系统工作稳定,在最大扭矩转速和最大...     

Closed-loop identification with an unstable or nonminimum phase controller

In many practical cases, the identification of a system is done in closed loop with some controller. In this paper, we show that the internal stability of the resulting model, in closed loop with the same controller, is not always guaranteed if this controller is unstable and/or nonminimum phase, and that the classical closed-loop prediction-error identification methods present different properties regarding this stability issue. With some of these methods, closed-loop instability of the identified model is actually guaranteed. This is a serious drawback if this model is to be used for the design of a new controller. We give guidelines to avoid the emergence of this instability problem; these guidelines concern both the experiment design and the choice of the identification method.     

基于内模法的PID控制器自整定算法

为解决传统工业控制中比例-积分-微分(PID)控制器参数整定的问题,提出了一种基于内模法(IMC)以及系统辨识的控制器参数确定算法。该方法首先利用被控过程在开环阶跃信号激励下,输入与暂态输出的对应关系,将被控对象辨识为一阶加滞后(FOPDT)或二阶加时滞(SOPDT)的模型;再利用IMC算法确定控制器的参数。对于在内模法中引入的滤波器参数λ的确定问题,提出通过引入γ和σ两个参数,并与输出误差的平方建立关系来确定λ 的方法。仿真显示,对于输出误差绝对值之和(IAE)这个指标,该种算法与传统基于IMC的PID控制算法相比,在无输入扰动时可提高20%左右,在有输入扰动时可提高10%左右。仿真结果表明:在用单位阶跃信号激励系统时,提出的整定方法在保证了系统鲁棒性的前提下,提高了系统的瞬态响应速度,并有效抑制了系统输出的超调。     

Closed-loop diesel engine combustion phasing control based on crankshaft torque measurements

Methods for closed-loop combustion phasing control in a diesel engine, based on measurements of crankshaft torque, are developed and evaluated. A model-based method for estimation of cylinder individual torque contributions from the crankshaft torque measurements is explained and a novel approach for identification of crankshaft dynamics is proposed. The use of the combustion net torque concept for combustion phasing estimation in the torque domain is also described. Two different control schemes, one for individual cylinder control and one for average cylinder control, are studied. The proposed methods are experimentally evaluated using a light-duty diesel engine equipped with a crankshaft integrated torque sensor. The results indicate that it is possible to estimate and control on a cylinder individual basis using the measurements from the crankshaft torque sensor. Combustion phasing is estimated with bias levels of less than 0.5 crank angle degrees (CAD) and cycle-to-cycle standard deviations of less than 0.7 CAD for all cylinders and the implemented combustion phasing controllers manage to accurately counteract disturbances in both fuel injection timing and EGR fraction.     

Identification for control: Optimal input intended to identify a minimum variance controller

It is well known that the quality of the parameters identified during an identification experiment depends on the applied excitation signal. Prediction error identification using full order parametric models delivers an ellipsoidal region in which the true parameters lie with some prescribed probability level. This ellipsoidal region is determined by the covariance matrix of the parameters. Input design strategies aim at the minimization of some measure of this covariance matrix. We show that it is possible to optimize the input in an identification experiment with respect to a performance cost function of a closed-loop system involving explicitly the dependence of the designed controller on the identified model. In the present contribution we focus on finding the optimal input for the estimation of the parameters of a minimum variance controller, without the intermediate step of first minimizing some measure of the model parameter accuracy. We do this in conjunction with using covariance formulas which are not asymptotic in the model order, which is rather new in the domain of optimal input design. The identification procedure is performed in closed-loop. Besides optimizing the input power spectrum for the identification experiment, we also address the question of optimality of the controller. It is a wide belief that the minimum variance controller should be the optimal choice, since we perform an experiment for designing a minimum variance controller. However, we show that this may not always be the case, but rather depends on the model structure.     

Design of an adaptive terminal sliding‐function controller for nonlinear multivariable systems

This paper presents an adaptive terminal sliding‐function controller approach for controlling a class of nonlinear multivariable systems with uncertainty. An appropriate terminal sliding function (TSF) is designed and then applied to the control law. Based on the Lyapunov stability theory, the adaptive terminal sliding‐function controller for nonlinear multivariable systems guarantees that the TSF is asymptotically convergent. Different from classical terminal sliding mode control, which uses a discontinuous switching control law, the TSF control uses a continuous TSF and thus avoids the chattering problem. The simulation results demonstrate that the proposed method achieves satisfactory stability. Copyright © 2013 John Wiley & Sons, Ltd.     

Quantitative non‐diagonal controller design for multivariable systems with uncertainty

The loop coupling reduction of multivariable systems under the presence of plant uncertainty is currently a most discussed topic. Following the ideas suggested by Horowitz, in this paper the role played by the non‐diagonal controller elements is analysed in order to state a design methodology. Thus, the definition of a coupling matrix and a quality function of the non‐diagonal elements come into use to quantify the amount of loop interaction and to design the controllers, respectively. This yields a criterion that makes possible to propose a sequential design methodology of the fully populated matrix controller, in the quantitative feedback theory (QFT) robust control frame. Finally, a real example with the heat exchanger of a pasteurization plant is included to show the practical use of this technique. Copyright © 2002 John Wiley & Sons, Ltd.     

大滞后过程的PI控制器整定

提出一种简单而有效的大滞后过程PI控制器整定方法．基于继电反馈试验,辨识出一阶加纯滞后模型,其Nyquist曲线在一个大的频率范围内接近于大滞后过程．根据有效模型并基于一种新的鲁棒性能指标,设计出PI控制器．仿真结果表明了PI控制器的有效性。     

Filtering adaptive neural network controller for multivariable nonlinear systems with mismatched uncertainties

This paper synthesizes a filtering adaptive neural network controller for multivariable nonlinear systems with mismatched uncertainties. The multivariable nonlinear systems under consideration have both matched and mismatched uncertainties, which satisfy the semiglobal Lipschitz condition. The nonlinear uncertainties are approximated by a Gaussian radial basis function (GRBF)‐based neural network incorporated with a piecewise constant adaptive law, where the adaptive law will generate adaptive parameters by solving the error dynamics between the real system and the state predictor with the neglection of unknowns. The combination of GRBF‐based neural network and piecewise constant adaptive law relaxes hardware limitations (CPU). A filtering control law is designed to handle the nonlinear uncertainties and deliver a good tracking performance with guaranteed robustness. The matched uncertainties are cancelled directly by adopting their opposite in the control signal, whereas a dynamic inversion of the system is required to eliminate the effect of the mismatched uncertainties on the output. Since the virtual reference system defines the best performance that can be achieved by the closed‐loop system, the uniform performance bounds are derived for the states and control signals via comparison. To validate the theoretical findings, comparisons between the model reference adaptive control method and the proposed filtering adaptive neural network control architecture with the implementation of different sampling time are carried out.     

A decentralized multivariable controller for hydrostatic wind turbine drivetrain

A hydrostatic drivetrain transmits wind turbine energy to a generator. One hydrostatic transmission system (HTS) configuration utilizes a fixed displacement pump and a variable displacement motor. The system dynamics are captured in a nonlinear multi‐input multi‐output mathematical model. This paper introduces a decentralized control configuration based on this model to achieve two desired objectives: maximizing the harvested energy without direct measurement of wind and regulating the frequency of the generator without using power electronic converters. To accomplish these objectives, suitable pairing of control actuators and system responses are identified through nonlinear relative gain arrays (RGA) analysis. The pairing also provides a strong decoupling of control loops. So maximum power point tracking (MPPT) is achieved independently while the generator speed is regulated to maintain the frequency of generated power at 60 Hz. Simulation results demonstrate robust performance of MPPT and frequency regulation in the presence of uncertainties in the turbine and HTS model. We also demonstrate that the RGA paired input‐out control configuration offers superior performance over other possible input–output paired control configurations.     

Diagonal dominance for multivariable nyquist array methods using function minimization

A new method of achieving diagonal dominance for Nyquist array design methods is presented. The technique utilizes a conjugate direction function minimization algorithm to obtain dominance over a specified frequency range by minimizing the ratio of the moduli of the off-diagonal terms to the moduli of the diagonal terms of the appropriate open loop transfer matrix. The new dominance algorithm is easily implemented in either a batch or interactive computer mode and will yield dominant compensators when alternative methods fail. Several concepts new to the Nyquist array methods are also presented. The proposed method is applied to the control system design for a sixteenth order state model of the Pratt-Whitney F-100 turbofan engine with three inputs.     

Inverted decoupling internal model control for square stable multivariable time delay systems

This paper presents a new tuning methodology of the main controller of an internal model control structure for n × n stable multivariable processes with multiple time delays based on the centralized inverted decoupling structure. Independently of the system size, very simple general expressions for the controller elements are obtained. The realizability conditions are provided and the specification of the closed-loop requirements is explained. A diagonal filter is added to the proposed control structure in order to improve the disturbance rejection without modifying the nominal set-point response. The effectiveness of the method is illustrated through different simulation examples in comparison with other works.     

柴油机可调涡流比燃烧过程三维仿真

在柴油机进气歧管前安装蝶形涡流调节阀,通过调整直气道侧的有效流通面积改变缸内涡流强度。在稳流吹风试验平台,研究涡流调节阀角度对进气道流量因数和涡流比的影响,并结合粒子图像测速（particle image velocimetry,PIV）分析缸内涡流的形成过程。采用计算流体力学（computational fluid dynamics,CFD）评估涡流调节阀角度对缸内混合气体形成过程的影响,计算结果可复现三维 PIV测量的缸内流场结构和相似的涡心位置。随着进气门关闭,涡流比从0.57提高到2.05,油气在周向的相互作用增强,从而加速预混燃烧阶段的放热速度,促使燃烧重心提前、燃烧持续期缩短。在相同进气流量条件下,强涡流运动也促使累积放热量增加。     

Output regulation by postprocessing internal models for a class of multivariable nonlinear systems

In this paper we propose a new design paradigm, which employs a postprocessing internal model unit, to approach the problem of output regulation for a class of multivariable minimum‐phase nonlinear systems possessing a partial normal form. Contrary to previous approaches, the proposed regulator handles control inputs of dimension larger than the number of regulated variables, provided that a controllability assumption holds, and can employ additional measurements that need not to vanish at the ideal error‐zeroing steady state, but that can be useful for stabilization purposes or to fulfill the minimum‐phase requirement. Conditions for practical and asymptotic output regulation are given, underlying how in postprocessing schemes the design of internal models is necessarily intertwined with that of the stabilizer.     

A self-tuning regulator for multivariable systems

The controller described in this paper is designed for multivariable plants with constant, unknown parameters. The algorithm operates on-line with the a priori information about the time delay. The order of the system may be given a priori. In the case where the order of the system is unknown it can be determined by a generalized likelihood-ratio statistical test which is described in this paper. The multivariable self tuning regulator consists of the two tasks of estimation and regulation. Estimation of the input-output system model parameters is based on the least-squares principle. The control is computed to minimize the combined cost of output deviation and control energy. Asymptotic properties of the estimation are discussed. Usefulness and simplicity of this approach are illustrated by examples.     

Using combustion net torque for estimation of combustion properties from measurements of crankshaft torque

Two methods, both based on the concept of combustion net torque, for estimation of combustion properties using measurements of crankshaft torque data are investigated in this work. The first of the proposed methods estimates entire burned mass fraction traces from corresponding combustion net torque traces. This is done by solving a convex optimization problem that is based on a derived analytical relation between the two quantities. The other proposed estimation method estimates the well established combustion phasing measure referred to as 50% burned mass fraction directly from combustion net torque using a nonlinear black-box mapping. The methods are assessed using both simulations and experimental data gathered from a 5-cylinder light-duty diesel engine equipped with a crankshaft torque sensor and cylinder pressure sensors that are used for reference measurements. The results indicate that both methods work well but the method that estimates entire burned mass fraction traces is more sensitive to torque data quality. Based on the experimental crankshaft torque data, the direct combustion phasing estimation method delivers estimates with a bias of less than 1 CAD and a cycle-to-cycle standard deviation of less than 2.7 CAD for all cylinders.     

Comments on: ‘Diagonal dominance for multivariable Nyquist array methods using function minimization’

The above paper (Leininger, 1979) claims to give a ‘new’ method for achieving diagonal dominance in multivariable systems. This is, however, not the first time that a function minimization approach has been used to obtain dominance. An optimisation oriented method was first proposed in 1975 (Ahson, 1975). This method is based on a new formulation of diagonal dominance criterion which takes into account model parameter uncertainties.