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

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Control Theory and Technology 2020年第1期18卷 34-42页

作者： Huanyu DI tielong shen Department of Engineering and Applied Sciences Sophia UniversityTokyo 102-8554Japan

For the study of internal combustion engines,combustion control is an important method to achieve high efficiency and low ***,in-cylinder pressure sensor-based closed-loop control strategies have become the preferred ***,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 *** paper proposes a method of combustion phase estimation for spark ignition(SI)*** this method,the combustion phase is estimated only based on pressure measurements at several crank *** 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 *** 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.

关键词： Information entropy mutual information combustion phase estimation in-cylinder pressure extreme learning machine

Logical control scheme with real-time statistical learning for residual gas fraction in IC engines

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Science China(Information Sciences) 2018年第1期61卷 45-57页

作者： Xun shen Yuhu WU tielong shen Department of Engineering and Applied Sciences Sophia University School of Control Science and Engineering Dalian University of Technology

In this paper, an optimal control scheme for reducing the fluctuation of residual gas fraction(RGF) under variational operating condition is developed by combining stochastic logical system approach with statistical learning method. The method estimating RGF from measured in-cylinder pressure is introduced firstly. Then, the stochastic properties of the RGF are analyzed according to statistical data captured by conducting experiments on a test bench equipped with a L4 internal combustion engine. The influences to the probability distribution of the RGF from both control input and environment parameters are also analyzed. Based on the statistical analysis, a stochastic logical transient model is adopted for describing cyclic behavior of the RGF. Optimal control policy maps for different fixed operating conditions are calculated then. Besides, a statistical learning-based method is applied to learn the probability density function(PDF)of RGF in the real-time which is used to adjust the control MAP based on logical optimization. The whole optimal control policy map is obtained based on Gaussian process regression with consideration of statistical information of RGF. Finally, the performance of the proposed method is experimentally validated.

关键词： combustion engine statistical learning residual gas fraction variable valve timing logical control

A survey on online learning and optimization for spark advance control of SI engines

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Science China(Information Sciences) 2018年第7期61卷 6-26页

作者： Yahui ZHANG Xun shen tielong shen Department of Engineering and Applied Sciences Sophia University

One of the most important factors affecting fuel efficiency and emissions of automotive engines is combustion quality that is usually controlled by managing spark advance(SA) in spark ignition(SI) *** increasing sensing capabilities and enhancements in on-board computation capability, online learning and optimization techniques have been the subject of significant research interest. This article surveys the literature of learning and optimization algorithms with applications to combustion quality optimization and control of SI engines. In particular, this paper reviews extremum seeking control algorithms for iterative solution of online optimization problems, stochastic threshold control algorithms for iterative solution of probability control of stochastic knock event, as well as feedforward learning algorithms for iterative solution of operating-point-dependent feedforward adaptation problems. Finally, two experimental case studies including knock probabilistic constrained optimal combustion control and on-board map learning-based combustion control are carried out on an SI gasoline engine.

关键词： online learning stochastic optimization iterative solution combustion control spark ignition engine

Optimal energy consuming planning for a home-based microgrid with mobility constraint of electric vehicles and tractors

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Control Theory and Technology 2021年第4期19卷 465-483页

作者： Shota Inuzuka tielong shen Faculty of Science and Technology Sophia University7-1 KioichoChiyoda-kuTokyoJapan

This research deals with the energy management problem to minimize the cost of non-renewable energy for a small-scale microgrid with electric vehicles(EV)and electric tractors(ET).The EVs and ETs function as batteries in the power system,while they often have to leave it for their mobility and agricultural *** State of Charge(SoC),which is the charge rate of the battery from 0 to 1,and the operating time of ETs are optimized under the assumption that the required electrical energy,the arrival and departure time of EVs,and the working time of ETs are given by users,but they include *** this paper,we deal with these uncertainties by constraints for robust energy planning and expected optimization based on scenarios,and show that the scheduling of the SoC assuming the worst case and the optimal home-based power consumption planning that considers the cost of each scenario corresponding to each variation can be *** proposed method is formulated as a mixed-integer linear programming(MILP),and numerical simulations show that the optimal cooperative operation among multiple houses can be obtained and its global optimal or sub-optimal solution can be quickly obtained by using CPLEX.

关键词： Microgrid Electric vehicle(EV) Stochastic scheduling MILP

Feedback stabilization for a class of discontinuous systems driven by integrator

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控制理论与应用（英文版） 2013年第2期11卷 268-274页

作者： Jiangyan ZHANG tielong shen Department of Engineering and Applied Sciences Sophia University College of Electromechanical & Information Engineering Dalian Nationalities University

This paper investigates the feedback stabilization problem for a class of discontinuous systems which is characterized by Filippov differential inclusion. Lyapunov-based backstepping design method is generalized with nons- mooth Lyapunov functions to solve the control problem. A set-valued time derivative is introduced first for nonsmooth function along discontinuous vector fields, which enables us to perform Lyapunov-based design with nondifferentiable Lyapunov function. Conditions for designing a virtual control law which is shown nondifferentiable in general in the re- cursive design problem are proposed. Finally, as a special case, piecewise linear system is discussed to demonstrate the application of the presented design approach.

关键词： Differential inclusion Discontinuous systems Backstepping Nonsmooth Lyapunov function

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COOPERATIVE SHIFT ESTIMATION OF TARGET TRAJECTORY USING CLUSTERED SENSORS

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Journal of Systems Science & Complexity 2014年第3期27卷 413-429页

作者： HU Jiangping HU Xiaoming shen tielong School of Automation Engineering University of Electronic Science and Technology of China Optimization and Systems Theory and ACCESS Linnaeus Center Royal Institute of TechnologyStockholmSweden Department of Engineering and Applied Sciences Sophia UniversityTokyoJapan

In this paper,a mathematical model for target tracking using nonlinear scalar range sensors is formulated first.A time-shift sensor scheduling strategy is addressed on the basis of a k-barrier coverage protocol and all the sensors are divided into two classes of clusters,active cluster,and submissive cluster,for *** two types of time-shift nonlinear filters are proposed for both active and submissive clusters to estimate the trajectory of the moving target with disturbed *** stochastic stability of the two filters is ***,some numerical simulations are given to demonstrate the effectiveness of the new filters with a comparison of EKF.

关键词： Cluster sensor network target tracking time-shift estimation

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Nonlinear observer-based control design and experimental validation for gasoline engines with EGR

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Control Theory and Technology 2019年第3期17卷 216-227页

作者： Weihai JIANG tielong shen Department of Engineering of Science Sophia University Chiyoda-ku Tokyo 102-8554 Japan

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 in take 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 in take pressure and exhaust pressure state to the desired values during both the steady-state and transient conditions quickly by only using the standard sensors.

关键词： Engine exhaust gas recirculation (EGR) Lyapunov design stability observer-based stabilizing control

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

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Control Theory and Technology 2015年第2期13卷 151-159页

作者： Madan KUMAR tielong shen Department of Engineering and Applied Science Sophia University

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.

关键词： Discrete-time model AFR estimation using Kalman filter in-cylinder charge combustion efficiency PI controller

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Special issue on model-based optimal control theory and technology in automotive control

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Control Theory and Technology 2017年第2期15卷 81-82页

作者： tielong shen (1) 1. Sophia University Tokyo Japan

Optimality is one of fundamental pursuits of control engineers in control system design practice. Optimal control is never a new issue in control theory, however, it still attracts much attention in the research field of control theory, especially in control technology development, due to the difficulty in analytical optimal control law synthesis or numerical computing load in implementation. Meanwhile, in the past decades, electronization of automotive pow- ertrain control motivated on-board optimal control algorithm that targets high combustion efficiency and emission performance since complicated real-time control algorithm is enabled by the progress of ECU technology. This spe- cial issue focused mainly on recent researches in model-based optimal control of automotive engine and powertrain systems.

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