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Nash optimality based distributed model predictive control for vehicle platoon ⁎

IFAC-PapersOnLine 2020年第2期53卷 6610-6615页

作者： Shuyou Yu Hao Chen Yangyang Feng Yajing Zhang Yongfu Li Christian Ebenbauer Hong Chen Department of Control Science and Engineering Jilin University Changchun 130012 P.R. China College of Automation Chongqing University of Posts and Telecommunications Chongqing 400065 P.R. China Institute for System Theory and Automatic Control University of Stuttgart Stuttgart 70550 Germany Clean Energy Automotive Engineering Tongji University Shanghai 201804 P. R. China

In this paper, a distributed model predictive control algorithm (DMPC) based on Nash optimality is proposed for automated vehicle platoon control. The optimization decision of vehicle platoon is decomposed into the decentralized optimization of single vehicles, in which the Nash optimality algorithm is adopted to solve the decentralized optimization problem. Thus, each vehicle can reach the local optimal target and the whole team can reach its Nash equilibrium. The methodology employs neighborhood information of the entire platoon through on-board sensors and V2V communication to achieve coordination of the entire platoon. The ability of the methods in terms of robustness to disturbances and cyber-physical interaction is demonstrated with simulation case studies.

关键词： Vehicle platoon Distributed control Model predictive control Nash optimality

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Constrained Gaussian Process Learning for Model Predictive control ⁎

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IFAC-PapersOnLine 2020年第2期53卷 971-976页

作者： Janine Matschek Andreas Himmel Kai Sundmacher Rolf Findeisen Laboratory for Systems Theory and Automatic Control Otto von Guericke University Magdeburg Germany Process Systems engineering Otto von Guericke University Magdeburg Germany Max Planck Institute for Dynamics of Complex Technical Systems Magdeburg Germany

Many control tasks can be formulated as tracking problems of a known or unknown reference signal. examples are motion compensation in collaborative robotics, the synchronisation of oscillations for power systems or the reference tracking of recipes in chemical process operation. Both the tracking performance and the stability of the closed-loop system depend strongly on two factors: Firstly, they depend on whether the future reference signal required for tracking is known, and secondly, whether the system can track the reference at all. This paper shows how to use machine learning, i.e. Gaussian processes, to learn a reference from (noisy) data while guaranteeing trackability of the modified desired reference predictions within the framework of model predictive control. Guarantees are provided by adjusting the hyperparameters via a constrained optimisation. Two specific scenarios, i.e. asymptotically constant and periodic REFERENCES, are discussed.

关键词： Gaussian processes trajectory tracking learning supported model predictive control

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On a solution to a nonlinear heat transfer model by the BEM

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AIP Conference Proceedings 2023年第1期2624卷

作者： A. L. Kazakov L. F. Spevak Matrosov Institute for System Dynamics and Control Theory Siberian Branch of the Russian Academy of Sciences Irkutsk Russia Irkutsk State Transport University Irkutsk Russia Ural State University of Railway Transport Ekaterinburg Russia Institute of Engineering Science Ural Branch of the Russian Academy of Sciences Ekaterinburg Russia

The paper deals with a nonlinear heat transfer model based on the nonlinear second order degenerate parabolic equation with arbitrary form of nonlinearity. Boundary value problems with two non-stationary boundary conditions are discussed: the problem with a specified heat wave front and the problem with a condition specifying the desired function at some manifold. A new existence theorem is formulated. Time-step algorithms based on the boundary element method and the dual reciprocity method are developed. Iteration procedures converging to the continuous solution on each time step are proposed. The algorithms are verified by comparison with known exact solution.

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Combining Prior Knowledge and Data for Robust controller Design

arXiv

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arXiv 2020年

作者： Berberich, Julian Scherer, Carsten W. Allgöwer, Frank The University of Stuttgart Institute for Systems Theory and Automatic Control Stuttgart70569 Germany The Institute of Mathematical Methods in the Engineering Sciences Numerical Analysis and Geometrical Modeling Department of Mathematics University of Stuttgart Stuttgart70569 Germany

We present a framework for systematically combining data of an unknown linear time-invariant system with prior knowledge on the system matrices or on the uncertainty for robust controller design. Our approach leads to linear matrix inequality (LMI) based feasibility criteria which guarantee stability and performance robustly for all closed-loop systems consistent with the prior knowledge and the available data. The design procedures rely on a combination of multipliers inferred via prior knowledge and learnt from measured data, where for the latter a novel and unifying disturbance description is employed. While large parts of the paper focus on linear systems and input-state measurements, we also provide extensions to robust output-feedback design based on noisy input-output data and against nonlinear uncertainties. We illustrate through numerical examples that our approach provides a flexible framework for simultaneously leveraging prior knowledge and data, thereby reducing conservatism and improving performance significantly if compared to black-box approaches to data-driven control. Copyright © 2020, The Authors. All rights reserved.

关键词： Robust control

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The control of Magnetic Levitation System Based on Improved Q-network

The Control of Magnetic Levitation System Based on Improved ...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Tao Huang Yue Liang Xiaojun Ban Jian Zhang Xianlin Huang Department of Control Theory and Engineering Harbin Institute of Technology Harbin China

ISBN: (数字)9781728124858

ISBN: (纸本)9781728124865

The magnetic system is a typical nonlinear system with the problem of uncertain parameters. Q-network is a model-free reinforcement learning method, which takes reward function as feedback and finds the optimal strategy through iterative learning. In this paper, to solve the problem, we first design the control based on the Q-network which realizes the stable control of the system without depending on the system model, and more importantly, we propose the retraining algorithm to further improve the accuracy of controller. Finally, the effectiveness of the Q-network controller in the control of a magnetic levitation system is verified by numerical simulation. The simulation results show that the network retraining algorithm can effectively reduce the steady-state error of the Q-network controller.

关键词： Magnetic levitation Learning (artificial intelligence) Neural networks Training Steady-state State feedback

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Mandatory Lane Change Strategy in VANET Based on Coordinated Stackelberg Game Model

Mandatory Lane Change Strategy in VANET Based on Coordinated...

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Chinese control and Decision Conference, CCDC

作者： Ding Nan Akira Maria Meng Xianghua Nasibullah Di Wu Department of Computer Science and Technology Dalian University of Technology Dalian P.R. China Electronics and Communication Science Unit Indian Statistical Institute Kolkata India Department of Control Theory and Control Engineering Dalian University of Technology Dalian P.R. China

ISBN: (数字)9781728158556

ISBN: (纸本)9781728158563

Despite the unavailability of intersections and turns, expressway has several lane closures and ramps. Mandatory lane change takes place when the geographical condition requires the vehicles to shift lane, such as, exits, on and off-ramps. Due to a high-speed regulation on the expressway, it is more difficult and riskier to shift lane, therefore better strategies for semi-automation vehicles have to be implemented for the users' safety. This paper studies the efficiency of the Stackelberg based algorithm for safer mandatory lane maneuver, yet still with appropriate speed. The paper also utilizes model predictive controller (MPC) to predict the motions of surrounding vehicles a few time steps in the future to model the probabilities of subject vehicles to shift. Then we consider the costs, MPC and the game to output the best time for subject vehicle to shift. Finally, we simulate the algorithm to determine if it is efficient by comparing it to other existing algorithms and breaking the simulation down to various indicators.

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Bearing-Only Formation control with Limited Visual Sensing: Two Agent Case⁎

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IFAC-PapersOnLine 2018年第23期51卷 28-33页

作者： Frank, Daniel Zelazo, Daniel Allgöwer, Frank Institute for Systems Theory and Automatic Control University of Stuttgart Germany Faculty of Aerospace Engineering Technion - Israel Institute of Technology Israel

This work solves the bearing-only formation control problem for two agents with limited field-of-view sensing. We propose a bearing-only control strategy for both the position and heading of each agent that guarantees the desired formation is obtained from almost all initial conditions, and ensures that the agents always remain inside the field-of-view of the sensor. We support our analysis with simulations and explore an extension for the 3- and 4-agent case. © 2018

关键词： Multi agent systems Bearing only control strategies Field of views Formation control Formation control problems Initial conditions Two agent case Visual sensing

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Underactuated pendulum damping by its length adjustment and passive output selection*

Underactuated pendulum damping by its length adjustment and ...

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European control Conference (ECC)

作者： Sergej Čelikovský Milan Anderle Tomáš Vyhlídal Institute of Information Theory and Automation (ÚTIA AV ČR) The Czech Academy of Sciences Prague 8 Czech Republic Department of Instrumentation and Control Engineering Czech Technical University in Prague Czech Republic

ISBN: (数字)9783907144022

ISBN: (纸本)9781728188133

Nonlinear passivity-based feedback controller to damp oscillations of the underactuated pendulum-like system via the active modification of the length of the suspension pendulum string is presented here. The structurally rich family of convenient virtual outputs making that system input-output passive will be determined and various asymptotically stabilizing feedback laws will be computed based on them. Among those controllers two qualitatively different cases were tested both in simulations and laboratory real-time experiments. First of them does not require the angular velocity measurement and guarantees that the string length does not exceed the maximum of its initial and its nominal (i.e., the ideal operational) value. The second one, on the contrary, does not require the angle measurement and guarantees that the string length does not go bellow the minimum of its nominal length and its initial value. Both controllers can clearly nicely complement each other in various practical situations if application to a crane with suspended load is considered. Their performances were compared to some previously published approaches as well.

关键词： Mathematical model Aerospace electronics Solid modeling Damping Oscillators Force Lyapunov methods

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Limitations on control performance in the Czochralski crystal growth process using bright ring measurement as a controlled variable ⁎

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IFAC-PapersOnLine 2019年第14期52卷 129-134页

作者： Halima Zahra Bukhari Morten Hovd Jan Winkler Department of Engineering Cybernetics NTNU Trondheim Norway Institute of Control Theory Faculty of Electrical & Computer Engineering TU Dresden Germany

This paper develops a dynamical model for crystal diameter in the Czochralski process for production of monocrystalline silicon. The model combines simplified crystal growth dynamics with rigorous ray tracing to describe the camera image used for diameter control, and it is demonstrated that the resulting model captures the so-called measurement anomaly that represents a key performance limitation for crystal diameter control.

关键词： Czochralski process bright ring measurement right half plane zero ray tracing interface dynamics

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A Dueling-Double-Deep Q-Network controller for Magnetic Levitation Ball System

A Dueling-Double-Deep Q-Network Controller for Magnetic Levi...

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Chinese control Conference (CCC)

作者： Yefeng Yang Xiaojun Ban Xianlin Huang Chenghao Shan Center for Control Theory and Guidance Technology Harbin Institute of Technology Harbin P. R. China Institute of Marine Navigation Technology Harbin Engineering University Harbin P. R. China

ISBN: (数字)9789881563903

ISBN: (纸本)9781728165233

Deep reinforcement learning technique has attracted extensive attention in the field of control with the rapid development of artifical intelligence. Model-free control methods perform more effcient than traditional model-based control method for complex nonlinear system since they don't require accurate models. In this study, by using the Dueling-Double-DQN (Dueling-DDQN, 3-DQN) algorithm, a self-learning controller is developed for the magnetic levitation ball system. The numerical simulation demonstrates the effectiveness of our controller. Moreover, we compare the above controller with the DQN-based self-learning controller. Numerical simulation shows that the attraction domain of the Dueling-DDQN controller is larger that the latter.

关键词： Mathematical model Magnetic levitation Artificial neural networks control systems Machine learning Voltage control Training

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