Staged combustion control design for aero engines

This paper discusses the findings of an analysis of an aero engine staged combustion fuel distribution control system design in the framework of the LECCAGT research project. The paper outlines the motivation for developing a staged combustion system and highlights the control-related issues of such an approach for a civil aerospace application. Findings of an analysis of the emission minimisation are presented and control-related issues of optimal fuel distribution are addressed in the paper. Multi-objective genetic algorithms are implemented for the automation of the engine fuel distribution optimisation. Bumpless transfer techniques for priming and purging of burner stages during transient manoeuvres are analysed and presented.     

An optimal feedforward control design for the set-point following of MIMO processes

In this paper we propose a new methodology for the design of a feedforward action for the improvement of the set-point following performance of feedback controlled square MIMO processes. In particular, by exploiting an analytical decoupling technique, the feedforward signals are determined in order to achieve predefined output transition times, by assuming that the transfer function matrix of the system consists of first order plus dead time transfer functions. An analytical expression of the feedforward signal is derived and this allows to solve easily a multiobjective optimisation problem in order to minimise the transition time of each output subject to constraints of the actuators. Simulation as well as experimental results demonstrate the effectiveness of the method.     

The torque ratio concept for combustion monitoring of internal combustion engines

This work presents a method to analyze combustion events in an internal combustion engine, called the torque ratio concept. The method is based on crankshaft torque measurements, but an extension to angular speed measurements is possible. The torque ratio concept provides a parametrized model for the combustion progress from which, e.g. combustion phasing can be extracted. The torque ratio concept is derived mathematically and related theoretically to other combustion analysis methods, such as pressure ratio and net heat release. Finally, analysis on recorded data from a five cylinder spark ignited engine verifies the relationships between the three methods. For combustion phasing, the 50% torque ratio is an equivalent measure to 50% pressure ratio and can be transformed into the 50% net heat release position by using a derived volume ratio function.     

Self-tuning gross heat release computation for internal combustion engines

The paper describes a novel method for self-tuning gross heat release computation in internal combustion engines suitable for both online usage in combustion phasing control applications and post-processing of cylinder pressure measurements. The method estimates the polytropic exponents and cylinder pressure offsets immediately preceding and succeeding the combustion event, respectively, using a fast nonlinear least-squares method. The polytropic exponent and the pressure offset are subsequently interpolated during the combustion event and a net heat release computation is performed based on the interpolated exponent and pressure. The result is a self-tuning gross heat release algorithm with no need to model heat losses, crevice losses and blow-by explicitly.     

Control of highly diluted combustion in Diesel engines

In this paper, an open-loop control strategy to improve the stability of highly diluted Diesel combustion during sharp transients is proposed. The main advantage of this open-loop method is that no additional in-cylinder sensor is required. The approach coordinates existing airpath and fuelpath controllers, and aims at accurately controlling the end of the cool-flame phenomenon. For this purpose, the injection time is adjusted based on an auto-ignition model and a cool-flame model. Both models are expressed under integral forms, named knock integral models. Experimental results obtained on a test bench and on-board a vehicle are presented. The results highlight the relevance of the approach in terms of combustion stability improvement, pollutant emissions and noise.     

Three-dimensional computer analysis of flow and combustion in automotive internal combustion engines

The paper presents a method for calculating the three-dimensional flow-fields in reciprocating internal combustion engines, as a function of space and time, throughout the complete four-stroke cycle. The method is based on a computational procedure which solves the governing elliptic partial-differential equations on a finite-difference grid which expands and contracts with the motion of the piston, using a fully-implicit, iterative, finite-difference scheme. Results are presented for typical engines, under engine-motoring and spark-ignited conditions. It is concluded that careful physical experiments should now be conducted in parallel with the computer experiments to validate the predictions, before the model can be used directly in assisting engine design; but also that it is now practical, for the first time, to perform fully three-dimensional calculations of the flow within the engine.     

Neuro-controller for reducing cyclic variation in lean combustion spark ignition engines

Literature shows that by controlling engines at extreme lean operating conditions (equivalence ratio <0.75) can reduce emissions by as much as 30% (Inoue, Matsushita, Nakanishi, & Okano (1993). Toyota lean combustion system—the third generation SAE, 930,873) and also it improves fuel efficiency by as much as 5-10%. However, the engine exhibits strong cyclic variation in heat release which may lead to instability and poor performance. A novel neural network (NN) controller is developed to control spark ignition (SI) engines at extreme lean conditions. The purpose of neuro-controller is to reduce the cyclic variation in heat release at lean engine operation even when the engine dynamics are unknown. The stability analysis of the closed-loop control system is given and the boundedness of all the signals is ensured. The adaptive NN does not require an offline learning phase and the weights can be initialized at zero or random. Results demonstrate that the cyclic variation is reduced significantly using the proposed controller developed using an experimentally validated engine model. The proposed approach can also be applied to a class of nonlinear systems that have a similar structure as that of the engine dynamics.     

Cascaded control of combustion and pollutant emissions in diesel engines

Control of the emissions of diesel engines is an upcoming approach for complying with legislation while limiting the calibration effort. In this paper, a controller for engine-out NO_x and PM is combined with a controller for the center of main combustion and the indicated mean effective pressure. The inner cascade combustion controller effectively reduces unwanted influences on the combustion. These influences are partly coupled to the outer-loop manipulated variables and partly result from disturbances which commonly appear in diesel engines. Robust stability is analyzed and holds in a wide operating range. Performance of the control structure is demonstrated with experiments.     

A crankshaft system model for structural dynamic analysis of internal combustion engines

A system model for analyzing the dynamic behavior of an internal combustion engine crankshaft is described. The model couples the crankshaft structural dynamics, the main bearing hydrodynamic lubrication and the engine block stiffness using a system approach. A two-level dynamic substructuring technique is used to predict the crankshaft dynamic response based on the finite-element method. The dynamic substructuring uses a set of load-dependent Ritz vectors. The main bearing lubrication analysis is based on the solution of the Reynold's equation. Comparison with experimental results demonstrates the accuracy of the model. Numerical results also show the capabilities and significance of the model in engine crankshaft design.     

An expert system for fault diagnosis in internal combustion engines using probability neural network

An expert system for fault diagnosis in internal combustion engines using adaptive order tracking technique and artificial neural networks is presented in this paper. The proposed system can be divided into two parts. In the first stage, the engine sound emission signals are recorded and treated as the tracking of frequency-varying bandpass signals. Ordered amplitudes can be calculated with a high-resolution adaptive filter algorithm. The vital features of signals with various fault conditions are obtained and displayed clearly by order figures. Then the sound energy diagram is utilized to normalize the features and reduce computation quantity. In the second stage, the artificial neural network is used to train the signal features and engine fault conditions. In order to verify the effect of the proposed probability neural network (PNN) in fault diagnosis, two conventional neural networks that included the back-propagation (BP) network and radial-basic function (RBF) network are compared with the proposed PNN network. The experimental results indicated that the proposed PNN network achieved the best performance in the present fault diagnosis system.     

Cylinder pressure based combustion phase optimization and control in spark-ignited engines

Efficiency and emissions of spark-ignited engines are significantly affected by combustion phase which can usually be indicated by crank angle of 50\% mass burnt (CA50). Managing combustion phase at the optimal value at which the maximal efficiency can be achieved is a challenging issue due to the cyclic variations of combustion process. This paper addresses this issue in two loops: CA50 set-point optimization (outer loop) and set-point tracking (inner loop) by controlling spark advance (SA). Extremum seeking approach maximizing thermal efficiency is employed in the CA50 set-point optimization. A proportional-integral (PI) controller is adopted to make the moving average value of CA50 tracking the optimal CA50 set-point determined in the outer loop. Moreover, in order to obtain fast responses at steady and transient operations, feed-forward maps are designed for extremum seeking controller and PI controller, respectively. Finally, experimental validations are conducted on a six-cylinder gasoline at steady and transient operations to show the effectiveness of proposed control scheme.     

Synthesis of nonlinear optimal real-time automatic control systems for gas turbine engines

An approach to solution of the central problem of automatic control theory as applied to digital automatic control systems of aeroengines based on formation at each control step of the optimal criterion in the form of an objective function and determination of optimal control actions from the solution of the obtained nonlinear programming problem. The results of the synthesis of a system for controlling the given flight velocity of a heavy transport aircraft optimal with respect to fuel consumption per one kilometer are presented.     

Improved methodology and set-point design for diagnosis of model-plant mismatch in control loops using plant-model ratio

Performance of any model-based control scheme depends on the quality of model. When these schemes deliver poor loop performance due to model-plant mismatch (MPM), a detection of the same needs to be in place. A recently introduced plant model ratio (PMR) not only detects MPM but also facilitates a unique identification of the source of mismatch, namely, gain, dynamics (time constant) and delay mismatches. The prime objective of this work is to improve the PMR approach in a few key aspects, namely, estimation and experimental effort, and assessment procedure by taking a fresh perspective of PMR and conducting a detailed theoretical study of its signatures. A rigorous assessment procedure based on the theoretical properties of PMR is devised. Three threshold-based hypotheses tests are proposed for significance testing of PMR. A key contribution of this work is the design of set-point with minimal excitation for diagnosis of MPM, based on the features of PMR. The revised methodology is demonstrated and compared with the existing method through simulation examples. The study also demonstrates the potential of the proposed method in serving as a prelude to full/partial model re-identification.     

Terminal sliding mode control of rail pressure for gasoline direct injection engines

The desired fuel rail pressure is a crucial factor for guaranteeing the gasoline direct injection (GDI) engine to work stably. In order to solve the rail pressure control problem, the detailed nonlinear model of GDI is derived and reasonable simplification of this model is carried out for the following controller design. Terminal sliding mode control strategy is proposed to design the rail pressure controller with Lyapunov stability. The designed approach with the fast terminal sliding mode surface makes the system have the capacity of global fast convergence and achieves precise tracking control. To demonstrate the validity of the designed control method, simulations are conducted by tracking the different reference rail pressures. Results show that the designed controller tracks the given reference accurately and has strong robustness.     

Hessian optimal design for image retrieval

Recently there has been a considerable interest in active learning from the perspective of optimal experimental design (OED). OED selects the most informative samples to minimize the covariance matrix of the parameters, so that the expected prediction error of the parameters, as well as the model output, can be minimized. Most of the existing OED methods are based on either linear regression or Laplacian regularized least squares (LapRLS) models. Although LapRLS has shown a better performance than linear regression, it suffers from the fact that the solution is biased towards a constant and the lack of extrapolating power. In this paper, we propose a novel active learning algorithm called Hessian optimal design (HOD). HOD is based on the second-order Hessian energy for semi-supervised regression which overcomes the drawbacks of Laplacian based methods. Specifically, HOD selects those samples which minimize the parameter covariance matrix of the Hessian regularized regression model. The experimental results on content-based image retrieval have demonstrated the effectiveness of our proposed approach.     

Repetitive controller design for optimal performance

In this paper, a repetitive controller design method is proposed for optimal performance. Firstly, to compensate for phase lag introduced by the low‐pass filter in the scheme, the dead time of the repetitive controller is modified. Secondly, a lead compensator is introduced to the repetitive controller, which not only widens the bandwidth of the low‐pass filter, but also improves system gain at high frequencies. The optimal parameters of the repetitive controller are obtained by solving two optimization problems. A numerical example is provided to illustrate the validity of the proposed method. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Objective functions for optimal building design

In this paper, the authors describe 13 investor motivations for initiating a project. Quantitative criteria to measure success in achieving these motivations are listed. One of the these criteria, net present value, is selected as most capable of reflecting key investor motivations. This measure is manipulated to yield a life-cycle costing relationship which can be used as an objective function in the design decision-making process. It is shown that the life-cycle cost equation in its most general form embodies within it a family of commonly used objective functions which describe financial, technical performance and user criteria. Results from a case study in which different objective functions were used are presented.     

低噪声内燃机电站数字励磁调节系统应用研究

低噪声内燃机双频电站电压的稳定性主要取决于双频电站电压同步发电机调压系统的响应特性。通过理论分析,对控制同步机励磁系统的关键技术进行了研究,阐述了电压偏差调节原理,给出了双频电站多参数数字励磁系统的设计方案。试验结果表明,该调节系统结构简单,性能优良,可有效抑制负荷的干扰,保证输出电压精度满足双频电站并联要求。     

Mutual information of cylinder pressure and combustion phase estimation in spark ignition engines

For the study of internal combustion engines, combustion control is an important method to achieve high efficiency and low emissions. Currently, in-cylinder pressure sensor-based closed-loop control strategies have become the preferred solution. However, their productional application in automotive industries is limited due to the cost of intensive pressure acquisition for a whole cycle and the calculation load of combustion phase indicators. This paper proposes a method of combustion phase estimation for spark ignition (SI) engines. In this method, the combustion phase is estimated only based on pressure measurements at several crank angles. Information entropy and mutual information are introduced to analyze the feasibility and accuracy of the combustion phase estimation, which shows that the pressure measurements at selected points contain most of the information for the estimation. As a result, only pressure measurements at 3 points and ELM estimation models are required to obtain the combustion phase, instead of intensive data acquisition and calculation.