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Discovering Stop and Parking Behaviors of Last Mile Delivery Vehicles for Urban Areas Based on Not Well Conditioned Gps Traces, Expert Knowledge and Machine Learning

SSRN

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SSRN 2023年

作者： Galende-Hernández, Marta Sainz-Palmero, Gregorio I. Fuente, Maria J. Department of System Engineering and Automatic Control School of Industrial Engineering Universidad de Valladolid Valladolid47011 Spain CARTIF Centro Tecnologico Parque Tecnologico de Boecillo Valladolid Boecillo47151 Spain

Nowadays urban traffic is one of the most serious challenges for local authorities around the world. This challenge implies issues regarding health, energy, safety, environment and quality of life. Achieving a fair traffic outcome is a key priority for city traffic managers while maintaining city’s attractiveness for citizens and travellers and those carrying out commercial activities. As a part of this challenge, delivery and similar companies are relevant stakeholders in optimizing their routes, deliveries, and operational services. It is in this respect that urban traffic and its regulations play a key *** conditions in which urban traffic takes place thus have to be known and one way to deal with this is based on available and accessible data. In the IoT age, there could be huge amounts of available data generated by countless sensors and systems involved in our lives, cities, infrastructures, etc.... Taking advantage of such data, when the accessibility and quality is good enough, can help us to achieve the desired goals concerning the urban traffic and its consequences. However, in practice, the availability and access to such data is currently a very serious *** on with this, data generated from the ever-increasing number of sensors on board vehicles could be very useful, not only for checking the vehiclecondition, but also to gain a better of the "real-time" traffic situation or to discover traffic behaviors/patterns from the said *** this work, a realcase based study has been carried out gathering basic real GPS information regarding delivery vehicles in a city environment and OpenData to "discover" where, when and how long time delivery vehicles use the regulated parking zones for loading/unloading in the city center. Based on Expert Rules, a stop detection criteria is defined and formulated to be applied to real cases in urban areas, focusing on city centers and Machine Learning techniques to discover stop and park behaviors on la

关键词： Parks

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An Algorithm for Discrete State Sequence and Trajectory Optimization for Hybrid systems with Partitioned State Space

An Algorithm for Discrete State Sequence and Trajectory Opti...

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2010 49th IEEE Conference on Decision and control

作者： Benjamin Passenberg Marion Sobotka Olaf Stursberg Martin Buss Peter E. Caines Institute of Automatic Control Department of Electrical Engineering and Information Technology Technische Universitat Munchen Munchen Germany Institute of Control and System Theory Department of Electrical Engineering and Computer Science University of Kassel Kassel Germany Department of Electrical and Computer Engineering and the Center for Intelligent Machines McGill University Montreal Canada

ISBN: (纸本)9781424477456

An algorithm for hybrid optimal control is proposed that varies the discrete state sequence based on gradient information during the search for an optimal trajectory. The algorithm is developed for hybrid systems with partitioned state space. It uses a version of the hybrid minimum principle that allows optimal trajectories to pass through intersections of switching manifolds, which enables the algorithm to vary the sequence. Consequently, the combinatorial complexity of former algorithms can be avoided, since not each possible sequence has to be investigated separately anymore. The convergence of the algorithm is proven and a numerical example demonstrates the efficiency of the algorithm.

关键词： Switches Manifolds Trajectory Complexity theory Partitioning algorithms Convergence Aerospace electronics

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The Minimum Principle for Hybrid systems with Partitioned State Space and Unspecified Discrete State Sequence

The Minimum Principle for Hybrid Systems with Partitioned St...

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2010 49th IEEE Conference on Decision and control

作者： Benjamin Passenberg Peter E. Caines Marion Sobotka Olaf Stursberg Martin Buss Institute of Automatic Control Department of Electrical Engineering and Information Technology Technische Universitat Munchen Munchen Germany Department of Electrical and Computer Engineering and the Center for Intelligent Machines McGill University Montreal Canada Institute of Control and System Theory Department of Electrical Engineering and Computer Science University of Kassel Kassel Germany

ISBN: (纸本)9781424477456

The hybrid minimum principle (HMP) gives necessary conditions to be satisfied for optimal solutions of a hybrid dynamical system. In particular, the HMP accounts for autonomous switching between discrete states that occurs whenever the trajectory hits switching manifolds. In this paper, the existing HMP is extended for hybrid systems with partitioned state space to provide necessary conditions for optimal trajectories that pass through an intersection of switching manifolds. This extension is especially useful for the numerical solution of hybrid optimal control problems as it allows for algorithms with significant reduction of computational complexity. Algorithms based on previous versions of the HMP solve separate optimal control problems for each possible sequence of discrete states. The extension enables us to consider the optimal sequence as subject of optimal control that is varied and finally determined during a single optimization run. A first numerical result illustrates the effectiveness of an algorithm based on the extended HMP.

关键词： state space minimum principle hybrid systems Manifolds Optimal control DYNAMICAL systemS discrete element

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Separable Nonlinear Model Predictive control via Sequential Quadratic Programming for Large-scale systems *

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IFAC-PapersOnLine 2015年第23期48卷 495-500页

作者： Liang Lu Department of Chemical and Biomolecular Engineering (BK21 Plus Program) Korea Advanced Institute of Science & Technology Daejeon Korea. Laboratory for Automatic Control and System Dynamics Technische Universität Chemnitz Chemnitz Germany.

In this paper, a sequential quadratic programming method is presented for largescale nonlinear and possibly non-convex model predictive control (MPC) optimization problem which is often set up with a separable objective function. By introducing the so-call consensus constraints to separate the couplings among the subsystems. The resulting QP subproblem is formulated in a separable form, which makes it possible to use the existing alternating direction methods, like ADMM, to efficiently compute Newton steps for the overall system in a distributed way. In order to enforce the convergence rate of the distributed computation, a distributed line search with local merit functions is also proposed.

关键词： Sequential quadratic programming separable model predictive control alternating direction method of multipliers nonlinear systems non-convex optimization large-scale optimization distributed line search

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Optimization Design and Dynamic Compensation Strategy of Inverter Parallel Virtual Impedance Based on Residual Generator

Optimization Design and Dynamic Compensation Strategy of Inv...

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IEEE Conference on Energy Internet and Energy system Integration (EI2)

作者： Changbin Hu Haiyang Huang Shanna Luo Huiru Wang Xuecheng Li Heng Lu North China University of Technology School of Electrical and Control Engineering Beijing China Beijing Kedong Electric Power Control System Co. Ltd Beijing China Department of Automatic Control and Complex Systems University of Duisburg-Essen Germany

To solve the power imbalance problem caused by the difference between the equivalent impedance of the inverter and the line impedance, and the power quality problem caused by the disturbance of load mutation, unbalanced load and nonlinear load, this paper proposes a closed-loop feedback dynamic compensation strategy based on the equivalent virtual impedance of the residual generator. Firstly, based on the parallel topology of inverters, a quasi-steady-state model is established on the dq axis. Based on the model, the power imbalance caused by the difference of output impedance and line impedance of different inverters and the power quality problems caused by load switching, unbalanced load, nonlinear load and other disturbances in parallel inverters are ***, a residual generator is established based on the inverter model. A virtual impedance equivalent controller Q vir (S) based on the residual generator is designed by the Luenberger observer principle. The disturbance information is fed back in real time through the feedback structure, and then the virtual impedance is adjusted to realize the power sharing of the inverter parallel system. Then, based on Euler parameterization and coprime factorization theory, a dynamic compensation architecture based on residual generator is designed. The transfer function of the dynamic compensation controller Q com (S) is obtained by model matching to realize the steady-state operation of parallel inverters and the improvement of system power quality. Finally, the effectiveness of the proposed architecture is verified on the experimental platform.

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Identification of Aircraft Gas-Turbine Dynamics using B-splines Neural Networks

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IFAC Proceedings Volumes 2000年第6期33卷 123-128页

作者： J.C. Lopes A.E. Ruano P.J. Fleming Unidade de Ciências Exactas e Humanas Universidade do Algarve Campus de Gambelas 8000 Faro Portugal Institute of System & Robotics Portugal Department of Automatic Control and Systems Engineering University of Sheffield Sheffield UK

This paper describes preliminary results regarding the development of a B- splines neural network model of the fuel feed to shaft-speed dynamics of a twin-shaft gas turbine engine. Data recorded from practical testing of the turbine to a multisine input were employed, and models were identified at different points along the turbine operating curve. B-splines neural networks have been found to be good models of the system, delivering good results especially for long-range predictions.

关键词： Gas Turbines systems Identification Neural Networks

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Adaptive Multimodel Estimation for Synthesis of a Robust Stabilizer Under Imperfect Knowledge of the Plant Delay

Adaptive Multimodel Estimation for Synthesis of a Robust Sta...

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Mediterranean Conference on control and Automation (MED)

作者： S. Alonso-Quesada M. de la Sen A. Ibeas Department of Electricity and Electronics University of Basque Country (UPV-EHU) Leioa Spain System Engineering and Automatic Control Group ETSE Universidad Autónoma de Barcelona Bellaterra Spain

This paper deals with a robustly stable adaptive pole-placement based controller for a class of linear time-invariant time-delay systems with possibly locally unknown point delays under unmodeled dynamics and bounded disturbances. A multi-estimation scheme is used to improve the identification error and also to deal with possibly errors between the true basic delay compared to these used in the regression vectors of the adaptive scheme

关键词： Delay estimation Adaptive control Programmable control Uncertainty Noise robustness Adaptive systems Robust control Robust stability Error correction control systems

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Optimizing the structure of hierarchical mixture of experts using genetic algorithms

Optimizing the structure of hierarchical mixture of experts ...

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2004 2nd International IEEE Conference 'Intelligent systems' - Proceedings

作者： Karras, Dimitrios A. Vlitakis, Constantinos E. Boutalis, Yiannis S. Mertzios, Basil G. Chalkis Institute of Technology Dept. Automation Hellenic Open University Rodou 2 Ano Iliupolis Athens 16342 Greece Democritus University of Thrace Dept. Electrical Engineering 67100 Xanthi Greece Department of Automation Thessaloniki Inst. Technol. Thessaloniki Hellas Democritus University Lab. of Automatic Control System Xanthi Hellas

ISBN: (纸本)0780382781

This study is an effort to give a practical solution in the problem of optimizing the structure of the Hierarchical Mixture of Experts model, which is a natural extension of the Associative Gaussian Mixture of Experts system. We present two novel methods for optimizing such structures using Genetic Algorithms. Special concern is taken for reducing the computational time so as to efficiently allow the structure to "grow" while it evolves with the Genetic Algorithm. The main contribution of the paper lies on the efficient, topologically oriented, representations of such architectures so as to be optimized through involving genetic algorithms.

关键词： Expert systems

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PoCET: a Polynomial Chaos Expansion Toolbox for Matlab

arXiv

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arXiv 2020年

作者： Petzke, Felix Mesbah, Ali Streif, Stefan Automatic Control & System Dynamics Lab Technische Universität Chemnitz Faculty for Electrical Engineering and Information Technology Chemnitz Germany Department of Chemical and Biomolecular Engineering University of California BerkeleyCA United States

We introduce PoCET: a free and open-scource Polynomial Chaos Expansion Toolbox for Matlab, featuring the automatic generation of polynomial chaos expansion (PCE) for linear and nonlinear dynamic systems with time-invariant stochastic parameters or initial conditions, as well as several simulation tools. It offers a built-in handling of Gaussian, uniform, and beta probability density functions, projection and collocation-based calculation of PCE coefficients, and the calculation of stochastic moments from a PCE. Efficient algorithms for the calculation of the involved integrals have been designed in order to increase its applicability. PoCET comes with a variety of introductory and instructive examples. Throughout the paper we show how to perform a polynomial chaos expansion on a simple ordinary differential equation using PoCET, as well as how it can be used to solve the more complex task of optimal experimental design. Copyright © 2020, The Authors. All rights reserved.

关键词： Nonlinear dynamical systems

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A robust multiestimation based stable adaptive control scheme for a tandem of master-slave robotic manipulators with force reflection

A robust multiestimation based stable adaptive control schem...

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American control Conference (ACC)

作者： A. Ibeas M. de la Sen S. Alonso-Quesada System Engineering and Automatic Control Group ETSE Universidad Autónoma de Barcelona Bellaterra Spain Department of Electricity and Electronics University of Basque Country (UPV-EHU) Leioa Spain

The problem of controlling a tandem of robotic manipulators composing a teleoperation system with force reflection is addressed in this paper. The final objective of the manuscript is to design a robust control law capable of improving the tracking of each robot to its corresponding reference model in comparison to existing controllers when the slave is interacting with an obstacle while guaranteeing closed-loop stability. In this way, a multiestimation-based adaptive controller is proposed. Thus, the control scheme for each robot is composed of a set of estimation algorithms running in parallel along with a supervisory index proposed with the aim of evaluating the identification performance of each one. Hence, the master robot is able to follow more accurately the constrained motion defined by the slave when interacting with an obstacle than when a single estimation based controller is used. Finally, some simulation examples showing the working of the multiestimation scheme complete the paper

关键词： Robust control Adaptive control Master-slave Robots Manipulators Reflection Force control control systems Robust stability Programmable control

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