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Bayesian probabilistic approach for model updating and damage detection for a large truss bridge

INTERNATIONAL JOURNAL OF STEEL STRUCTURES 2015年第2期15卷 473-485页

作者： Mustafa, Samim Debnath, N. Dutta, Anjan Indian Inst Technol Guwahati Dept Civil Engn Gauhati 781039 Assam India

A Bayesian probabilistic methodology is presented for structural model updating using incomplete measured modal data which also takes into account different types of errors such as modelling errors due to the approximation of actual complex structure, uncertainties introduced by variation in material and geometric properties, measurement errors due to the noises in the signal and the data processing. The present work uses linear optimization problems (LOP) to compute the probability that continually updated the model parameters. A real life rail-cum-roadway long steel truss bridge (Saraighat bridge) is considered in the present study, where identified modal data are available from measured acceleration responses due to ambient vibration. The main contributions of this paper are: (1) the introduction of sufficient number of model parameters at the element property level in order to capture any variations in the sectional properties;(2) the development of an accurate baseline model by utilizing limited sensor data;(3) the implementation of a probabilistic damage detection approach that utilizes updated model parameters from the undamaged state and possibly damaged state of the structure.

关键词： model updating system identification modal data bayesian probabilistic approach linear optimization problems damage detection

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Model Predictive Heating Control for Electric Vehicles Using Load Prediction and Switched Actuators

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IFAC-PapersOnLine 2016年第11期49卷 406-411页

作者： Bächle, T. Graichen, K. Buchholz, M. Dietmayer, K. Institute of Measurement Control and Microtechnology University of Ulm Germany

Thermal management of electric vehicles in low-temperature environments poses an interesting control problem. While thermal control of the cabin is vital for passenger comfort, energy consumption and battery aging always remain an essential concern. Since more reliable information about the short-term future electrical power flow is becoming available by more sophisticated cruise control systems and map information, on-line model predictive thermal management systems can utilize this knowledge to improve battery life maintaining passenger comfort. In this contribution, such a model predictive heating control is proposed based on an on-board capable fast gradient method to solve the nonlinear optimization problem. Direct control of simple electronic switches by the proposed method reduces the need for dedicated PWM controllers embedded within the heating actuators and allows for a cost-efficient and easy realization. © 2016

关键词： Electric power system control Actuators Battery management systems Computer control Electric load flow Electric machine control Electric switches Electric vehicles Energy management Energy utilization Gradient methods Information management Nonlinear programming Secondary batteries Temperature Temperature control Thermal management (electronics) Battery modeling Electrochemical systems Electronic switches Fast gradient methods Low temperature environment Non linear optimization problems Passenger comfort Thermal management systems

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Tackling problem nonlinearities & delays via asymmetric, state-dependent objective costs in MPC of an artificial pancreas

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IFAC-PapersOnLine 2015年第23期48卷 154-159页

作者： Gondhalekar, Ravi Dassau, Eyal Doyle III, Francis J. University of California Santa Barbara Santa BarbaraCA United States

The design of a Model Predictive Control (MPC) law for an Artificial Pancreas (AP) that automatically delivers insulin to people with type 1 diabetes mellitus is considered. An MPC law was recently proposed that exploits the simplicity of linear dynamical models, but is in two ways a 'nonlinear' departure of standard linear MPC, while circumnavigating the complexity of cumbersome, fully nonlinear MPC approaches. The first of two issues focused on is the nonlinearity of the control problem, and it is demonstrated how this can be tackled via asymmetric objective functions. The second issue is controller induced hypoglycemia resulting from the large delay in actuation and sensing. The proposed MPC strategy employs an asymmetric, state-dependent objective function that leads to a nonlinear optimization problem. The result is an AP controller with significantly elevated safety and comparable control performance. The contribution of this paper is a detailed in-silico analysis of the proposed control law, and a clinical demonstration of the benefits of asymmetric objective functions. © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Predictive control systems Artificial organs Controllers Costs Insulin Model predictive control Nonlinear programming Safety engineering Artificial pancreas Clinical demonstrations linear dynamical models Non linear optimization Non linear optimization problems State dependent Type 1 diabetes Type 1 diabetes mellitus

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An Iterative Approach to Reduce Systemic Risk among Financial Institutes

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IFAC-PapersOnLine 2017年第1期50卷 9380-9385页

作者： Ye, Xiang-Shen Xue, Ruo-Bing Gao, Jian-Jun Cao, Xi-Ren Department of Automation Shanghai Jiao Tong University Shanghai200240 China School of Information Management and Engineering Shanghai University of Finance and Economics Shanghai200433 China Department of Automation Shanghai Jiao Tong University Institute of Advanced Study Hong Kong University of Science and Technology Shanghai200240 China

Financial institutions are interconnected by holding debt claims against each other. The interconnection is a key contributing factor to the past worldwide financial crisis. A default bank may cause its creditors to default, and the risk may be further propagated to up-stream institutes. We study how the mechanism of default liquidation affects the total wealth of the financial system and curbs the risk contagion. We formulate this problem as a nonlinear optimization problem with equilibrium constraints and propose an optimal liquidation policy to minimize the system's loss without changing the partition of default and non-default banks. We show that the optimization problem resembles a Markov decision problem (MDP) and therefore we can apply the direct-comparison based optimization approach to solve this problem. We derive an iteration algorithm which combines both the policy iteration and the gradient based approach. Our work provides a new direction in curbing the risk contagion in financial networks; and it illustrates the advantages of the direct-comparison based approach, which originated in the field of discrete event dynamic system, in nonlinear optimization problems. © 2017

关键词： Finance Discrete event simulation Iterative methods Nonlinear programming Discrete event dynamic systems Equilibrium constraint Financial networks Markov decision problem Non linear optimization problems optimization approach optimization problems Policy iteration

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Loop Shaping for PID Controller Design Based on Time and Frequency Specifications

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IFAC-PapersOnLine 2018年第4期51卷 592-597页

作者： Bosman Barros, Clarisse Pétua Barros, Péricles Rezende da Rocha Neto, José Sergio Department of Electrical Engineering Federal University of Campina Grande 882 Aprígio Veloso Campina Grande Brazil

In this paper it is described a new method to design PID controllers using a linear programming approach for optimizing performance subject to robustness constraints. It is based on the shaping of a reference loop gain transfer function which forms a convex region on the Nyquist diagram which contains and bounds the designed loop gain Nyquist plot. The convex region is approximated by a set of lines in order to formulate a linear optimization problem. It is also presented an optional performance specification related to the crossover frequency of the designed loop gain. The class of stable linear time-invariant single-input simple-output (SISO) systems is considered and the optimization problem is proposed, solved and analyzed. © 2018

关键词： Three term control systems Design Electric control equipment linear programming Multiobjective optimization Proportional control systems Specifications Cross over frequencies linear optimization problems linear time invariant optimization problems Optimizing performance Parameter optimization Performance specifications PID controller design

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Dynamic Positioning simulations of a Thrust Allocation Algorithm considering Hydrodynamic Interactions

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IFAC-PapersOnLine 2018年第29期51卷 122-127页

作者： Arditti, Felipe Cozijn, Hans Van Daalen, Ed F.G. Tannuri, Eduardo A. Department of Mechatronics Engineering University of São Paulo São PauloSP05508-010 Brazil WageningenPM6708 Netherlands

The Dynamic Positioning (DP) System is responsible for the station keeping of vessels in several offshore operations. The forces required by the DP System are distributed among the available thrusters by a thrust allocation algorithm which should be accurate, efficient and robust. This means that the effective forces match the required forces while power consumption is minimized. To accurately generate the required forces, the physical limitations of the thrusters and the hydrodynamic interactions must be considered. The hydrodynamic interactions are consistently modelled to accommodate the following typical effects: thruster-hull, thruster-current and thruster-thruster interaction. The result of this modeling is a nonlinear optimization problem, which is solved using the Sequential Quadratic Programming (SQP) algorithm with slack variables. The DP simulation carried out show that by considering the hydrodynamic interactions on thrust allocation the overall performance (controllability and power consumption) of the DP system is improved. © 2018

关键词： Dynamic positioning Electric power utilization Hydrodynamics Offshore oil well production Quadratic programming Effective forces Hydrodynamic interaction Non linear optimization problems Offshore operations Physical limitations Sequential quadratic programming Thrust allocations Thruster interaction

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Equilibrium model of a credit market: Statement of the problem and solution methods

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COMPUTATIONAL MATHEMATICS AND MATHEMATICAL PHYSICS 2009年第3期49卷 450-465页

作者： Antipin, A. S. Popova, O. A. Russian Acad Sci Dorodnicyn Comp Ctr Moscow 119333 Russia Siberian State Automobile & Highway Acad Omsk 644050 Russia

An equilibrium model of a credit market is proposed and examined. The credit price or the interest rate in the model is determined by the consistent interaction of two macroscopic factors: supply and demand. Methods for computing an equilibrium interest rate are suggested. The methods are interpreted as market-balancing dynamics. The convergence of the methods is proved.

关键词： problem of credit market equilibrium linear optimization problems variational inequalities saddle points extraproximal method

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Power Control for Efficient Operation of a PEM Fuel Cell System by Nonlinear Model Predictive Control

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IFAC-PapersOnLine 2015年第11 11期48卷 174-179页

作者： Hähnel, C. Aul, V. Horn, J. Institute of Control Engineering Helmut-Schmidt-University Hamburg Germany

Efficiency of polymer electrolyte membrane (PEM) fuel cells depends directly on anode and cathode gas pressures, and stack temperature. For an efficient chemical reaction and a safe operation for both stationary and dynamic loads all values must fall within specific ranges. This paper deals with realtime nonlinear model predictive control (NMPC) of electrical power for a PEM fuel cell system. The NMPC is split into three nonlinear optimization problems to calculate the optimal stack current and the supply of anode and cathode with hydrogen and oxygen. For real-time application the predictive control algorithm is matched to the sampling time of the system. This NMPC handles all kinds of dynamic load changes, while maximizing power efficiency without a steady state error. Modeling of relevant anode and cathode site fuel cell peripherals is shown. Experimental results on a test bench with a 4.4kW PEM fuel cell are presented. © 2015

关键词： Proton exchange membrane fuel cells (PEMFC) Anodes Cathodes Dynamic loads Efficiency Electric power system control Fuel cells Fuel systems Identification (control systems) Model predictive control Nonlinear programming Nonlinear systems Oxygen supply Polyelectrolytes Power control Predictive control systems Non linear optimization problems Nonlinear model predictive control PEM fuel cell system Polymer electrolyte membranes Predictive control algorithm Real time application Stack temperature Steady state errors

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Flatness-based Model Predictive Control for the Fuel optimization of Hybrid Electric Vehicles

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IFAC-PapersOnLine 2015年第23期48卷 464-470页

作者： Joevski, Martina Abel, D. Institute of Automatic Control RWTH Aachen University Aachen52074 Germany Institute of Automatic Control RWTH Aachen University Germany

In this paper a nonlinear model predictive controller for the optimization of the hybrid electric vehicle fuel efficiency is introduced. The idea is to use the concept of differential atness to determine the evolution of system's state across the prediction horizon without numerical integration. The entire nonlinear optimization problem is expressed as a function of a fictious output, called the at output and its first derivative. The optimal distribution of the requested torque between propulsion devices is then determined by a static optimization. The atness-based model predictive controller (FMPC) is compared to a linear time-varying MPC (LTV-MPC) which applies the linearized model of the plant to predict the future state and output trajectories. Both strategies are evaluated on standard driving cycles. To proof the concept it is assumed that the velocity profile and driver torque demand are known and exact over a predefined prediction horizon.

关键词： Model predictive control Electric machine control Energy management Forecasting Fuels Hybrid vehicles Nonlinear programming Predictive control systems B splines flatness Model predictive controllers Non linear optimization problems Nonlinear MPC Numerical integrations Optimal distributions Static optimization

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A polynomial-time weighted path-following interior-point algorithm for linear optimization

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ASIAN-EUROPEAN JOURNAL OF MATHEMATICS 2020年第2期13卷

作者： Achache, Mohamed Univ Ferhat Abbas Lab Math Fondamentales & Numer Setif 1 Setif 19000 Algeria

In this paper, a weighted short-step primal-dual path-following interior-point algorithm for solving linear optimization (LO) is presented. The algorithm uses at each interior-point iteration a full-Newton step, thus no need to use line search, and the strategy of the central-path to obtain an epsilon-approximated solution of LO. We show that the algorithm yields the iteration bound, namely, O(root n log n/epsilon). This bound is currently the best iteration bound for LO. Finally, some numerical results are reported in order to analyze the efficiency of the proposed algorithm.

关键词： linear optimization problems interior-point methods short-step primal-dual algorithms complexity of algorithms

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