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data-driven Energy Management Strategy for Plug-in Hybrid Electric Vehicles with Real-World Trip Information

IFAC-PapersOnLine 2020年第2期53卷 14224-14229页

作者： Yongkeun Choi Jacopo Guanetti Scott Moura Francesco Borrelli Model Predictive Control Lab Department of Mechanical Engineering University of California Berkeley CA 94720 USA Energy Controls and Applications Lab Department of Civil and Environmental Engineering University of California Berkeley CA 94720 USA

This paper presents a data-driven supervisory energy management strategy (EMS) for plug-in hybrid electric vehicles which leverages Vehicle-to-Cloud connectivity to increase energy efficiency by learning control policies from completed trips. The proposed EMS consists of two layers, a cloud layer and an on-board layer. The cloud layer has two main tasks: the first task is to learn EMS policy parameters from historical trip data, and the second task is to provide the policy parameters along a certain route requested from the vehicle. The onboard layer receives the learned policy parameters from the cloud layer and computes a real-time solution to the powertrain energy management problem, using a model predictive control scheme. The proposed EMS is evaluated on more than 3000 miles (48 independent driving cycles) of real-world trip data, collected along three commuting routes in California. For the routes, the proposed algorithm shows 3.3%, 7.3%, and 6.5% improvement in average MPGe when compared to a baseline EMS.

关键词： data-based control Nonlinear predictive control Real-time control Engine modelling control Hybrid alternative drive vehicles Nonlinear optimal automotive control

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data-driven parameterizations of suboptimal LQR and H 2 controllers

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IFAC-PapersOnLine 2020年第2期53卷 4234-4239页

作者： Henk J. van Waarde Mehran Mesbahi Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence and engineering and Technology Institute Groningen University of Groningen the Netherlands William E. Boeing Department of Aeronautics and Astronautics University of Washington Seattle WA 98195 USA

In this paper we design suboptimal control laws for an unknown linear system on the basis of measured data. We focus on the suboptimal linear quadratic regulator problem and the suboptimal H 2 control problem. For both problems, we establish conditions under which a given data set contains sufficient information for controller design. We follow up by providing a data-driven parameterization of all suboptimal controllers. We will illustrate our results by numerical simulations, which will reveal an interesting trade-off between the number of collected data samples and the achieved controller performance.

关键词： data-based control optimal control theory linear systems

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Machine Learning Assisted Solutions of Mixed Integer MPC on Embedded Platforms

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IFAC-PapersOnLine 2020年第2期53卷 5195-5200页

作者： Yannik Löhr Martin Klaučo Miroslav Fikar Martin Mönnigmann Automatic Control and Systems Theory Ruhr-Universität Bochum Bochum Germany Institute of Information Engineering Automation and Mathematics Slovak University of Technology in Bratislava

Many control applications, especially in the field of energy systems, require a simultaneous decision for continuous and binary values of control inputs. In optimal control methods like model predictive control (MPC), this leads to the problem of solving expensive mixed-integer programs online. As this solution in practice has to be calculated with low cost embedded hardware with low energy demand, it is necessary to reduce the computational demand in advance. We present an approach to replacing the mixed-integer program by a simpler quadratic program by means of learning techniques. To be more specific, we design a neural network and a support vector machine to classify the optimal control policies for the binary inputs offline and evaluate this decision in the online step as the basis for the solution of the quadratic program. As a result, we achieve a controller suitable for implementation on embedded hardware. We demonstrate its applicability to a domestic heating system. The results indicate a very high quality of the approximation of the primary optimal controller that solves mixed-integer programs online.

关键词： nonlinear predictive control energy control data-based control neural networks classification heat flows control applications

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Model based Diagnosis of Oxygen Sensors 9th

Model Based Diagnosis of Oxygen Sensors

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9th IFAC International Symposium on Advances in Automotive control (AAC)

作者： Ekinci, Kubra Ertugrul, Seniz AVL Res & Engn Turkey TR-34920 Istanbul Turkey Istanbul Tech Univ Grad Sch Sci Engn & Technol TR-34496 Istanbul Turkey Izmir Econ Univ Mechatron Engn Dept Izmir Turkey

Automotive industry targets such as complying with emission legislations and increasing fuel economy, require the improvement of air-fuel ratio control systems. Oxygen sensors are a crucial part of these control systems and regulations oblige monitoring of their performance and detecting sensor-related faults. The primary purpose of this paper is to develop a methodology for precise and accurate monitoring and diagnosis of oxygen sensors to meet legislations and performance targets while the required calibration effort is reduced. Input parameters with the highest correlation factors were selected to be utilized in different system identification methodologies to statistically determine the most fitting model. In the end, a NARX model with two hidden layers and eight neurons in each hidden layer with standard deviation and mean threshold values was determined to be the optimum design to detect if the oxygen sensor was functioning or faulty. (C) 2019, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Oxygen sensor monitoring OBDII System identification data-based control model based diagnosis artificial neural network NARX residual generation

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data-based Self-Learning Optimal control for Continuous-Time Unknown Nonlinear Systems With Disturbance 28

Data-Based Self-Learning Optimal Control for Continuous-Time...

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28th Chinese control and Decision Conference

作者： Wei, Qinglai Liu, Derong Song, Ruizhuo Yan, Pengfei Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China

ISBN: (纸本)9781467397148

In this paper, a new data-based self-learning control scheme is developed to solve infinite horizon optimal control problems for continuous-time nonlinear systems. The developed optimal control scheme can be implement without knowing the mathematical model of the system. According to the input-output data of the nonlinear systems, a recurrent neural network (RNN) is employed to reconstruct the dynamics of the nonlinear system. According to the RNN model of the system, a new two-person zero-sum adaptive dynamic programming (ADP) algorithm is developed to obtain the optimal control, where the reconstruction error and the system disturbance are considered the control input of the system. Single-layer neural networks are used to construct the critic and action networks, which are presented to approximate the performance index function and the control law, respectively. Finally, simulation results will show the effectiveness of the developed data-based ADP methods.

关键词： Adaptive critic designs Adaptive dynamic programming Approximate dynamic programming Neuro-dynamic programming Recurrent neural network data-based control Optimal control

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A Nonparametric Approach to Design Fixed-order controllers for Systems with Constrained Input

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INTERNATIONAL JOURNAL OF control AUTOMATION AND SYSTEMS 2018年第6期16卷 2870-2877页

作者： Khadraoui, Sofiane Nounou, Hazem Univ Sharjah Dept Elect & Comp Engn Sharjah U Arab Emirates Texas A&M Univ Qatar Elect & Comp Engn Program Doha Qatar

This paper presents an approach for designing fixed-structure controllers for input-constrained linear systems using frequency domain data. In conventional control approaches, a plant model is needed to design a suitable controller that meets some user-specified performance specifications. Mathematical models can be built based on fundamental laws or from a set of measurements. In both cases, it is difficult to find a simple and reliable model that completely describes the system behavior. Hence, errors associated with the plant modeling stage may contribute to the degradation of the desired closed-loop performance. Due to the fact that the modeling stage can be viewed only as an intermediate step introduced for the controller design, the concept of data-based control design has been introduced, where controllers are directly designed from measurements. Most existing data-based control approaches are developed for linear systems, which limit their application to systems with nonlinear phenomena. An important non-smooth nonlinearity observed in practical applications is the input saturation, which usually limits the system performance. Here, we attempt to develop a nonparametric approach to design controllers from frequency-domain data by taking into account input constraints. Two practical applications of the proposed method are presented to demonstrate its efficacy.

关键词： Actuator saturation constrained optimization data-based control frequency response energy consumption safe process operation

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Synchronization control for reaction-diffusion FitzHugh-Nagumo systems with spatial sampled-data

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AUTOMATICA 2018年 93卷 352-362页

作者： Chen, Sheng Lim, Cheng-Chew Shi, Peng Lu, Zhenyu Nanjing Univ Informat Sci & Technol Sch Informat & Control B DAT CICAAEET Nanjing 210044 Jiangsu Peoples R China Univ Adelaide Sch Elect & Elect Engn Adelaide SA 5005 Australia Nanjing Univ Informat Sci & Technol Sch Elect & Informat Engn Nanjing 210044 Jiangsu Peoples R China

To gain a better understanding the synchronization mechanism of networked neurons, this paper studies the synchronization control fora class of reaction-diffusion FitzHugh-Nagumo systems, associated with a digraph containing at least one directed spanning tree. A novel control method adopting spatial sampling strategies is proposed, in which the control inputs are constructed directly on the spatial means of system state variables on sampling subsets. After discussing the existence and uniqueness of classical solutions, we show analytically that the synchronization of the controlled systems is equivalent to that of the corresponding FitzHugh-Nagumo systems under the corresponding control inputs. based on the study on general algebraic connectivity, a sufficient condition for the system synchronization is given, together with case simulations to illustrate the effectiveness and potential of the new control method. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Networked control systems Synchronization data-based control Spatial means General algebraic connectivity

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Constrained Extremum Seeking: a Modified-Barrier Function Approach ⁎

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IFAC-PapersOnLine 2019年第16期52卷 694-699页

作者： Christophe Labar Emanuele Garone Michel Kinnaert Christian Ebenbauer Department of Control Engineering and System Analysis Université libre de Bruxelles 1050 Brussels Belgium Institute for Systems Theory and Automatic Control University of Stuttgart 70569 Stuttgart Germany

In this paper, we address the problem of steering the input of a convex function to a value that minimizes the function under a convex constraint. We consider the case where the constraint cannot be violated of more than a user-defined value during the whole transient phase. The mathematical expression of both the cost function and the constraint are assumed to be unknown. The only information available are the on-line values of the cost and the constraint. To tackle this problem, an optimization law, based on a modified-barrier function, and involving the gradient of both the cost function and the constraint, is firstly designed. The Lie bracket formalism is then exploited to approximate this law, by combining time-periodic signals with the on-line measurements of both the cost and the constraint. The stability property of the resulting constrained extremum seeking system is proved, and its effectiveness is shown in simulation.

关键词： Constrained Optimization Extremum Seeking Singular Perturbations data-based control Modified Barrier-Function

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Model based Diagnosis of Oxygen Sensors

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IFAC-PapersOnLine 2019年第5期52卷 185-190页

作者： Kübra Ekinci Şeniz Ertuğrul AVL Research and Engineering Turkey Istanbul 34920 Turkey Graduate School of Science Engineering and Technology Istanbul Technical University Istanbul 34496 Turkey Mechatronics Engineering Department Izmir Economy UniversityIzmir Turkey

关键词： Oxygen sensor monitoring OBDII System identification data-based control model based diagnosis artificial neural network NARX residual generation

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data-based Self-Learning Optimal control for Continuous-Time Unknown Nonlinear Systems With Disturbance

Data-Based Self-Learning Optimal Control for Continuous-Time...

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第28届中国控制与决策会议

作者： Qinglai Wei Derong Liu Ruizhuo Song Pengfei Yan The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences School of Automation and Electrical Engineering University of Science and Technology Beijing

ISBN: (纸本)9781467397155

In this paper,a new data-based self-learning control scheme is developed to solve infinite horizon optimal control problems for continuous-time nonlinear *** developed optimal control scheme can be implement without knowing the mathematical model of the *** to the input-output data of the nonlinear systems,a recurrent neural network(RNN) is employed to reconstruct the dynamics of the nonlinear *** to the RNN model of the system,a new two-person zero-sum adaptive dynamic programming(ADP) algorithm is developed to obtain the optimal control,where the reconstruction error and the system disturbance are considered the control input of the ***-layer neural networks are used to construct the critic and action networks,which are presented to approximate the performance index function and the control law,***,simulation results will show the effectiveness of the developed data-based ADP methods.

关键词： Adaptive critic designs Adaptive dynamic programming Approximate dynamic programming Neurodynamic programming Recurrent neural network data-based control Optimal control

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