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.     

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.     

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.     

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.     

Data-driven diagnosis of sensor precision degradation in the presence of control

This paper considers the precision degradation type of sensor faults within control loops. In a closed loop, sensor faults propagate through controller to manipulated variables and disturb the other process variables, which obscures the source of sensor faults but receives less attention in existing methods of data-driven sensor fault diagnosis. With the assumption that only closed-loop data in normal condition are available, difficulty arises due to the facts that little a priori knowledge is known about closed-loop sensor fault propagation and the open-loop process model may not be identifiable. The proposed method in this paper constructs residual that is regarded as including two parts: the first part is the current sensor faults whose fault direction is known to be the identity matrix; and for the purpose of diagnosing the first part, the second part is considered as the disturbance which is affected by noises and past sensor faults due to unknown fault propagation. The disturbance variance is minimized in residual generator design to improve fault sensitivity. And the corresponding disturbance covariance is estimated and then utilized in residual evaluation. The proposed method in this paper is motivated by a pioneer work on closed-loop sensor fault diagnosis which performs principal component analysis in the feedback-invariant subspace of the closed-loop process outputs. But it is revealed by the proposed method that the feedback-invariant signal is affected by past sensor faults, leading to performance degradation of the pioneer work. The improvement of the proposed approach is due to analysis of residual dynamics and explicit handling of the disturbance in residual evaluation, which is not considered in the pioneer work. A simulated 4 × 4 dynamic process and a simulated two-product distillation column are studied to verify the effectiveness of the proposed approach compared to the existing principal component analysis method in feedback-invariant subspace.     

基于后驱动装置控制系统设计

本文介绍了涡轴发动机试车台零扭矩试验的工作原理及试验验证方法,详细介绍了其关键设备即后驱动装置的设备组成、选型设计、控制原理、控制逻辑、人机界面、安全措施。经试验表明后驱动装置的设备选型合理,控制系统工作稳定可靠、控制精度高、响应速度快、监控和安全保护措施完善,满足了涡轴发动机对零扭矩试验的功能要求,实现了涡轴发动机试车台零扭矩试验零的突破,填补了国内空白。     

Computer aided control system design applied to milk drying plants

This paper reviews a computer aided control system design facility that has been used to develop a set of control systems that have been implemented on a pilot scale industrial process. The facility caters for a broad range of control situations including those with interactions, time delays and disturbances. Online interactive graphics is used as a design aid for the identification of plant dynamics and for the assessment of control system performance. Control systems may be developed systematically to be structured in a hierarchical configuration on the plant. Particular applications to the plant, an evaporator and a spray drier, are discussed in detail.     

Persistent excitation condition within the dual control framework

Model Predictive Control framework is currently used in many different fields of expertise. The inherent part and very often also the main bottleneck is the model of a process used for the computation of predictions.Due to many reasons e.g. ageing, from time to time there exists a need to adjust/re-identify (if there was already some kind of a model-based controller) or to construct a brand new model (in other cases). Frequently, the process generating the data is under some kind of control, imposing thus problems when classical open loop identification methods are considered. The need for models identified from the data gathered in a closed-loop fashion and a request for possible re-identification of the model parameters lead to the emerge of dual control where the problems of control and system identification are addressed simultaneously.In this paper, we present a new algorithm based on the persistent excitation condition when the minimal eigenvalue of the information matrix is maximized in order to have sufficiently exciting optimal control signal satisfying the control requirements.     

On the necessity of identifying the true parameter in adaptive LQ control

In adaptive control problems one may drop the requirement of identifying the true system in order to simplify the problem of control. It will be shown that in the adaptive LQ control problem this does not at all lead to an easier problem.     

面向控制器设计的多变量系统辨识实验信号

给出一种当多交量系统辨识模型用于控制器设计时的开环实验输入信号．假定系统未楚模动态可以用加性不确定性表示,取实际输出和理想输出误差平方均值最小,将其与系统辨识最小二乘法相结合,得到一种最优输入信号的设计方法．因输入信号与控制性能的相关性,可获得比普通随机信号更好的辨识结果．仿真结果证明了该方法的有效性．     

λ传感器的输出电动势与理论分析

λ传感器广泛地用于汽车尾气检测和控制.在发动机台架上对自制λ传感器进行了性能测试,并用气体化学平衡原理对浓、稀燃烧状态的输出电动势进行了解释,同时探讨了温度对输出电动势的影响.所得到的理论模型与测试结果相一致.     

Closed-loop identification with routine operating data: Effect of time delay and sampling time

In industry, in order to store the reams of data that are collected from all the different flow, level, and temperature sensors, the fast-sampled data is very often downsampled before being stored in a data historian. This downsampled or even compressed data is, then, often used by process engineers to recover the appropriate process parameters. However, little has been written about the effects of the sampling on the quality of the model obtained. Therefore, in this paper, the effects of sampling time are investigated from both a theoretical and practical perspective using results that come out of the theory of closed-loop system identification with routine operating data. It is shown that if the discrete time delay in a process is sufficiently large or the sampling time is sufficiently small, then it is possible to recover the true system parameters. The most common industrial processes that fulfill this constraint are temperature control loops. These results suggest that the sampling time has an important bearing on the quality of the model estimated from routine operating data. Using both Monte Carlo simulations and an experimental set-up with a heated tank, the effect of discrete time delay on the identification of the true continuous time parameters was considered for different sampling times. It was shown that increasing the sampling time above a given threshold resulted in identifying an incorrect model. As well, the models obtained using a PID controller were less sensitive to sampling time than those obtained using a PI controller. However, the PID controller values were more sensitive to the effects of aliasing at larger sampling times.     

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.     

The polytropic volume method to detect engine events based on the measured cylinder pressure

Internal combustion engines deliver work using an intermittent thermodynamic process. For control and diagnosis purposes, it is useful to detect key events relative to the crank angle position. A new method to detect the intake valve closing (IVC), start of combustion (SOC), end of combustion (EOC) or exhaust valve opening (EVO), using the measured cylinder pressure as input, is described. The method is based on the observation that the compression and expansion processes are of polytropic nature. It is shown that the events can be detected by detecting the points where the real and the polytropic volume diverge.     

内燃机曲柄连杆机构的建模与仿真研究

对内燃机曲柄连杆机构的工作性能进行仿真分析是内燃机产品设计过程中的重要组成部分.文章根据机械系统动态仿真技术的相关理论,介绍了一种基于Pro/E和Matlab工程软件的内燃机曲柄连杆机构动力学仿真分析方法.文章首先结合某内燃机曲柄连杆机构模型实例,利用Pro/E软件的三维实体建模功能建立了该机构的实体模型,然后利用Pro/E提供的Mechanism模块进行了曲柄连杆机构的动力学仿真分析,得到了活塞、连杆大头轴承等主要运动部件的运动规律及受力情况.最后,结合Mechanism模块得到的分析结果,利用Matlab软件对各主轴承负荷状况进行了数值模拟分析.对仿真结果的分析表明,仿真结果与内燃机的实际工作状况基本一致.文章介绍的仿真方法为曲柄连杆机构的选型、优化设计提供了一种简便有效的新思路.