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22nd World Congress of the International Federation of Automatic Control (IFAC)

作者： Deshpande, Vedang M. Laughman, Christopher R. Mitsubishi Elect Res Labs Cambridge MA 02139 USA

ISBN: (纸本)9781713872344

state and parameter estimation methodologies have the potential to make a significant impact in the development of broad array of capabilities for widely-used vapor compression cycles, including advanced controls, performance monitoring, data-driven modeling, and deployment of digital twin technologies. However, the nonlinearity and numerical stiffness of large physics-based models of these systems pose challenges for the practical implementation of estimators that must also satisfy the physical state constraints. We present a three-pass fixed-interval smoothing method developed in the extended Kalman estimation formalism that incorporates linear inequality and partially-known nonlinear equality constraints defined in terms of unknown parameters of the system. The smoothing method is demonstrated to have high estimation accuracy during joint state and parameter estimation of the cycle model representing a realistic system that is implemented in Julia language leveraging automatic differentiation capabilities. Copyright (C) 2023 The Authors.

关键词： Constrained estimation Kalman filtering and smoothing nonlinear state and parameter estimation vapor compression cycle HVAC

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Multi-pass Extended Kalman Smoother with Partially-known Constraints for estimation of Vapor Compression Cycles

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IFAC-PapersOnLine 2023年第2期56卷 6751-6758页

作者： Vedang M. Deshpande Christopher R. Laughman Mitsubishi Electric Research Laboratories Cambridge MA 02139 USA

关键词： Constrained estimation Kalman filtering and smoothing nonlinear state and parameter estimation vapor compression cycle HVAC

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Optimization-Based Tuning of a Hybrid UKF state Estimator with Tire Model Adaption for an All Wheel Drive Electric Vehicle

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ENERGIES 2021年第5期14卷 1396-1396页

作者： Heidfeld, Hannes Schunemann, Martin Otto von Guericke Univ Inst Mobile Syst Univ Pl 2 D-39106 Magdeburg Germany

Novel drivetrain concepts such as electric direct drives can improve vehicle dynamic control due to faster, more accurate, and more flexible generation of wheel individual propulsion and braking torques. Exact and robust estimation of vehicle state of motion in the presence of unknown disturbances, such as changes in road conditions, is crucial for realization of such control systems. This article shows the design, tuning, implementation, and test of a state estimator with individual tire model adaption for direct drive electric vehicles. The vehicle dynamics are modeled using a double-track model with an adaptive tire model. state-of-the-art sensors, an inertial measurement unit, steering angle, wheel speed, and motor current sensors are used as measurements. Due to the nonlinearity of the vehicle model, an Unscented Kalman Filter (UKF) is used for simultaneous state and parameter estimation. To simplify the difficult task of UKF tuning, an optimization-based method using real-vehicle data is utilized. The UKF is implemented on an electronic control unit and tested with real-vehicle data in a hardware-in-the-loop simulation. High precision even in severe driving maneuvers under various road conditions is achieved. nonlinear state and parameter estimation for all wheel drive electric vehicles using UKF and optimization-based tuning is shown to provide high precision with minimal manual tuning effort.

关键词： nonlinear state and parameter estimation electric vehicle vehicle dynamics

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A homotopy-based moving horizon estimation

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INTERNATIONAL JOURNAL OF CONTROL 2019年第7期92卷 1672-1681页

作者： Abdollahpouri, Mohammad Quirynen, Rien Haring, Mark Johansen, Tor Arne Takacs, Gergely Diehl, Moritz Rohal-Ilkiv, Boris Slovak Univ Technol Bratislava Inst Automat Measurement & Appl Informat Fac Mech Engn Bratislava Slovakia Norwegian Univ Sci & Technol Ctr Autonomous Marine Operat & Syst AMOS Dept Engn Cybernet Trondheim Norway Univ Freiburg Dept Microsyst Engn IMTEK Freiburg Germany MERL Cambridge MA USA

Moving horizon estimation (MHE) solves a constrained dynamic optimisation problem. Including nonlinear dynamics into an optimal estimation problem generally comes at the cost of tackling a non-convex optimisation problem. Here, a particular model formulation is proposed in order to convexify a class of nonlinear MHE problems. It delivers a linear time-varying (LTV) model that is globally equivalent to the nonlinear dynamics in a noise-free environment, hence the optimisation problem becomes convex. On the other hand, in the presence of unknown disturbances, the accuracy of the LTV model degrades and this results in a less accurate solution. For this purpose, some assumptions are imposed and a homotopy-based approach is proposed in order to transform the problem from convex to non-convex, where the sequential implementation of this technique starts with solving the convexified MHE problem. Two simulation studies validate the efficiency and optimality of the proposed approach with unknown disturbances.

关键词： Moving horizon estimation non-convex optimisation nonlinear systems nonlinear state and parameter estimation

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A Model-based Technique for Early and Robust Detection of Oscillatory Failure Case in A380 Actuators

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INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2011年第1期9卷 42-49页

作者： Lavigne, Loic Zolghadri, Ali Goupil, Philippe Simon, Pascal Univ Bordeaux 1 IMS Lab Dept Automat Control F-33405 Talence France AIRBUS OPERATIONS SAS Flight Control Syst Dept F-31060 Toulouse 09 France

This paper addresses the problem of Oscillatory Failure Cases (OFC) detection in the Electrical Flight Control System (EFCS) of the Airbus airplanes. OFC can lead to strong interactions with loads and aero-elasticity and consequently are to be detected very early. in time. The work describes the status of on going research activity undertaken within a collaborative project between Bordeaux University (France) and Airbus. An hydraulic actuator model is currently used as the basis for a robust analytical redundancy-based technique implemented in A380 Flight Control Computer (FCC) for detecting unauthorized oscillatory events. For upcoming and future generation aircraft (A/C), it could be required to detect OFC earlier with less important amplitude. The method presented here is based on nonlinear state space modeling, associated with the same decision test as used by in-service Airbus A/C. It is shown that the model quality could be improved significantly by reliable estimating of some physical parameters. The fault indicating signals are compared on data set obtained from A380 computers during flight tests.

关键词： Aircraft actuators airbus electrical flight control system nonlinear state and parameter estimation oscillatory failure case detection

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A software framework and tool for nonlinear state estimation

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IFAC Proceedings Volumes 2009年第10期42卷 510-515页

作者： Ondřej Straka Miroslav Flídr Jindřich Duník Miroslav Ŝimandl Department of Cybernetics & Research Centre Data - Algorithms - Decision Making University of West Bohemia Univerzitní 8 30614 Plzeň Czech Republic

The goal of the article is to describe a software framework designed for nonlinear state estimation of discrete time dynamic systems. The framework was designed with the aim to facilitate implementation, testing and use of various nonlinear state estimation methods in mind. The main strength of the framework is its versatility due to the possibility of either structural or probabilistic description of the problem. Besides the well-known basic nonlinear estimation methods such as the extended Kalman filter, the divided difference filters and the unscented Kalman filter, the framework implements particle filter with advanced features as well. As the framework is designed on the object oriented basis, further extension by user-specified nonlinear estimation algorithms is extremely easy. The paper provides a brief introduction into nonlinear state estimation problem and describes the individual components of the framework, their key features and use. The strengths of the framework are presented in two examples.

关键词： nonlinear state and parameter estimation Bayesian approach Software framework

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A UD factorization-based nonlinear adaptive set-membership filter for ellipsoidal estimation

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INTERNATIONAL JOURNAL OF ROBUST AND nonlinear CONTROL 2008年第16期18卷 1513-1531页

作者： Zhou, Bo Han, Jianda Liu, Guangjun Ryerson Univ Dept Aerosp Engn Toronto ON M5B 2K3 Canada Chinese Acad Sci Shenyang Inst Automat Shenyang 110016 Peoples R China Chinese Acad Sci Grad Sch Beijing 100039 Peoples R China

The extended set-membership filter (ESMF) for nonlinear ellipsoidal estimation suffers from numerical instability. computation complexity as well as the difficulty in filter parameter selection. In this paper, a UD factorization-based adaptive set-membership filter is developed and applied to nonlinear joint estimation of both time-varying stales and parameters. As a result of using the proposed UD factorization, combined with a new sequential and selective measurement update strategy, the numerical stability and real-time applicability of conventional ESMF are substantially improved. Furthermore, an adaptive selection scheme of the filter parameters is derived to reduce the computation complexity and achieve sub-optimal estimation. Simulation results have shown the efficiency and robustness of the proposed method. Copyright (C) 2007 John Wiley & Sons, Ltd.

关键词： set-membership filter UD factorization nonlinear state and parameter estimation adaptive filter parameter determination

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FRAMEWORK FOR IMPLEMENTING AND TESTING nonlinear FILTERS

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IFAC Proceedings Volumes 2006年第6期39卷 117-122页

作者： Miroslav Flídr Jindrich Duník Ondrej Straka Jaroslav Ŝvácha Miroslav Ŝimandl Cybernetics & Research Centre Data - Algorithms - Decision Making University of West Bohemia in Pilsen Univerzitní8 30614 Plzen Czech Republic

The aim of this paper is to present a software framework facilitating implementation, testing and use of various nonlinear estimation methods. This framework is designed to offer an easy to use tool for state estimation of discrete time dynamic stochastic systems. Besides implementation of various local and global state estimation methods it contains procedures for system design and simulation. Its strength is in the fact that it provides means that help students get acquainted with nonlinear state estimation problem and to be able to test features of various estimation methods. Another considerable advantage of proposed framework is its high modularity and extensibility. The paper briefly describes nonlinear estimation problem and its general solution using the Bayesian approach leading to the Bayesian recursive relations. Then it presents key features of the software framework designed in MATLAB environment that supports straightforward implementation of estimation methods based on the Bayesian approach. The strengths of the framework are demonstrated on implementation of the Divided difference filter 1st order.

关键词： stochastic systems nonlinear state and parameter estimation Bayesian recursive relations nonlinear filters software framework

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