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arXiv 2019年

作者： Varnai, Peter Dimarogonas, Dimos V. Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm114 28 Sweden

Signal temporal logic (STL) provides a userfriendly interface for defining complex tasks for robotic systems. Recent efforts aim at designing control laws or using reinforcement learning methods to find policies which guarantee satisfaction of these tasks. While the former suffer from the trade-off between task specification and computational complexity, the latter encounter difficulties in exploration as the tasks become more complex and challenging to satisfy. This paper proposes to combine the benefits of the two approaches and use an efficient prescribed performance control (PPC) base law to guide exploration within the reinforcement learning algorithm. The potential of the method is demonstrated in a simulated environment through two sample navigational tasks. Copyright © 2019, The Authors. All rights reserved.

关键词： Temporal logic

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Distributed Barrier Certificates for Safe Operation of Inverter-Based Microgrids

Distributed Barrier Certificates for Safe Operation of Inver...

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American control Conference

作者： Soumya Kundu Sijia Geng Sai Pushpak Nandanoori Ian A. Hiskens Karan Kalsi Optimization and Control Group Pacific Northwest National Laboratory USA Electrical Engineering and Computer Science University of Michigan Ann Arbor USA

ISBN: (纸本)9781538679012;9781538679265

Inverter-interfaced microgrids differ from the traditional power systems due to their lack of inertia. Vanishing timescale separation between voltage and frequency dynamics makes it critical that faster-timescale stabilizing control laws also guarantee by-construction the satisfaction of voltage limits during transients. In this article, we apply a barrier functions method to compute distributed active and reactive power setpoint control laws that certify satisfaction of voltage limits during transients. Using sum-of-squares optimization tools, we propose an algorithmic construction of these control laws. Numerical simulations are provided to illustrate the proposed method.

关键词： Safe handling voltage limits control law Licence Satisfaction power systems set point control Distribute

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Colored Petri net model of electrical networks for diagnostic purposes

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IFAC-PapersOnLine 2018年第2期51卷 260-265页

作者： Pózna, A.I. Fodor, A. Gerzson, M. Hangos, K.M. Department of Electrical Engineering and Information Systems University of Pannonia VeszprémH-8201 Hungary Institute for Computer Science and Control Hungarian Academy of Sciences BudapestH-1518 Hungary

A simple and effective colored Petri net (CPN) model is proposed in this paper for a special type of electrical networks that describe local transformer areas, in order to detect and localize illegal loads that may be present. The model is decomposed to characteristic structural elements called feeders with one or two sources the computation of which can be performed independently and in parallel. The CPN model allows to handle the interval-type uncertainties in the model in a transparent and effective way. © 2018

关键词： Electric network parameters

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A Pipelining Loop Optimization Method for Dataflow Architecture

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Journal of computer science & Technology 2018年第1期33卷 116-130页

作者： Xu Tan Xiao-Chun Ye Xiao-Wei Shen Yuan-Chao Xu Da Wang Lunkai Zhang Wen-Ming Li Dong-Rui Fan Zhi-Min Tang State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing 1000J9 China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi 214125 China College of Information Engineering Capital Normal University Beijing 100048 China Department of Computer Science The University of Chicago Chicago IL 60637 U.S.A.

With the coming of exascale supercomputing era, power efficiency has become the most important obstacle to build an exascale system. Dataflow architecture has native advantage in achieving high power efficiency for scientific applications. However, the state-of-the-art dataflow architectures fail to exploit high parallelism for loop processing. To address this issue, we propose a pipelining loop optimization method （PLO）, which makes iterations in loops flow in the processing element （PE） array of dataflow accelerator. This method consists of two techniques, architecture-assisted hardware iteration and instruction-assisted software iteration. In hardware iteration execution model, an on-chip loop controller is designed to generate loop indexes, reducing the complexity of computing kernel and laying a good f（mndation for pipelining execution. In software iteration execution model, additional loop instructions are presented to solve the iteration dependency problem. Via these two techniques, the average number of instructions ready to execute per cycle is increased to keep floating-point unit busy. Simulation results show that our proposed method outperforms static and dynamic loop execution model in floating-point efficiency by 2.45x and 1.1x on average, respectively, while the hardware cost of these two techniques is acceptable.

关键词： dataflow model control-flow model loop optimization exascale computing scientific application

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Gradient-Based STL control with Application to Nonholonomic Systems

arXiv

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arXiv 2019年

作者： Varnai, Peter Dimarogonas, Dimos V. Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm114 28 Sweden

In this paper, we study the control of dynamical systems under temporal logic task specifications using gradient-based methods relying on quantitative measures that express the extent to which the tasks are satisfied. A class of controllers capable of providing satisfaction guarantees for simple systems and specifications is introduced and then extended for the case of unicycle-like dynamics. The possibility of combining such controllers in order to tackle more complex task specifications while retaining their computational efficiency is examined, and the practicalities related to an effective combination are demonstrated through a simulation study. The introduced framework for controller design lays ground for future work in the direction of effectively combining such elementary controllers for the purpose of aiding exploration in learning algorithms. Copyright © 2019, The Authors. All rights reserved.

关键词： controllers

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Image de-blurring Model based on Machine Learning

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Journal of Physics: Conference Series 2021年第2期1952卷

作者： Junsheng Li Silu Xu Cong Wang College of Computer Science and Technology Harbin Engineering University Harbin Heilongjiang 150001 City Institute Dalian University of Technology Dalian Liaoning 1166000 School of Information and Control Engineering Qingdao University of Technology Qingdao Shandong 266520

Due to the influence of haze in winter, outdoor images usually lose contrast and fidelity. In view of the fact that most de-fog algorithms are not effective for images with large sky areas, an improved dark channel a priori de-fog method is proposed. First of all, the sky region is segmented according to the image gradient information, and on the basis of sky segmentation, the atmospheric light value is reasonably estimated by setting the discriminant formula combined with the high brightness and smoothness of atmospheric light reference pixels. Secondly, according to the different dark channel values, the piecewise linear function is used to dynamically modify the adjustable parameters to solve the local shadow caused by excessive defog. Then, the transmittance estimated by the bright channel model and the improved dark channel prior model are fused, and the edge is optimized by guided filtering. Finally, combined with the atmospheric scattering model, the defog image is obtained by brightness compensation and contrast stretching. The experimental results show that the improved method can effectively improve the image distortion, enhance the image contrast and details, especially in maintaining the visual authenticity of the sky region.

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Prescribed Performance control Guided Policy Improvement for Satisfying Signal Temporal Logic Tasks

Prescribed Performance Control Guided Policy Improvement for...

引用

American control Conference

作者： Peter Varnai Dimos V. Dimarogonas Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology 114 28 Stockholm Sweden

ISBN: (纸本)9781538679012;9781538679265

Signal temporal logic (STL) provides a user-friendly interface for defining complex tasks for robotic systems. Recent efforts aim at designing control laws or using reinforcement learning methods to find policies which guarantee satisfaction of these tasks. While the former suffer from the trade-off between task specification and computational complexity, the latter encounter difficulties in exploration as the tasks become more complex and challenging to satisfy. This paper proposes to combine the benefits of the two approaches and use an efficient prescribed performance control (PPC) base law to guide exploration within the reinforcement learning algorithm. The potential of the method is demonstrated in a simulated environment through two sample navigational tasks.

关键词： Religious Missions Temporal logic Exploration Simulated Environment Policies Satisfaction Learning Learning methods control law signals

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Push-Pull Finite-Time Convergence Distributed Optimization Algorithm

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American Journal of Computational Mathematics 2020年第1期10卷 118-146页

作者： Xiaobiao Chen Kaixin Yan Yu Gao Xuefeng Xu Kang Yan Jing Wang Department of Science Taiyuan University of Technology Taiyuan China Department of Automation Taiyuan University of Technology Taiyuan China Department of Automation School of Control and Computer Engineering North China Electric Power University Beijing China Department of Economics and Management Taiyuan University of Technology Taiyuan China Department of Mechanical and Electrical Engineering Shanxi Energy Institute Taiyuan China School of Medicine Clinical Medicine Datong University Datong China

With the widespread application of distributed systems, many problems need to be solved urgently. How to design distributed optimization strategies has become a research hotspot. This article focuses on the solution rate of the distributed convex optimization algorithm. Each agent in the network has its own convex cost function. We consider a gradient-based distributed method and use a push-pull gradient algorithm to minimize the total cost function. Inspired by the current multi-agent consensus cooperation protocol for distributed convex optimization algorithm, a distributed convex optimization algorithm with finite time convergence is proposed and studied. In the end, based on a fixed undirected distributed network topology, a fast convergent distributed cooperative learning method based on a linear parameterized neural network is proposed, which is different from the existing distributed convex optimization algorithms that can achieve exponential convergence. The algorithm can achieve finite-time convergence. The convergence of the algorithm can be guaranteed by the Lyapunov method. The corresponding simulation examples also show the effectiveness of the algorithm intuitively. Compared with other algorithms, this algorithm is competitive.

关键词： Distributed Optimization Finite Time Convergence Linear Parameterized Neural Network Push-Pull Algorithm Undirected Graph

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State estimation for a class of linear systems with quadratic output

arXiv

引用

arXiv 2020年

作者： Theodosis, Dionysios Berkane, Soulaimane Dimarogonas, Dimos V. Dynamic Systems and Simulation Laboratory School of Production Engineering and Management Technical University of Crete Greece Département d’informatique et d’ingénierie Université du Québec en Outaouis GatineauQC Canada Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden

This paper deals with the problem of state estimation for a class of linear time-invariant systems with quadratic output measurements. An immersion-type approach is presented that transforms the system into a state-affine system by adding a finite number of states to the original system. Under suitable persistence of excitation conditions on the input and its higher derivatives, global state estimation is exhibited by means of a Kalman-type observer. A numerical example is provided to illustrate the applicability of the proposed observer design for the problem of position and velocity estimation for a vehicle navigating in the n−dimensional Euclidean space using a single position range measurement. Copyright © 2020, The Authors. All rights reserved.

关键词： State estimation

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Nonlinear observer with observability-based parameter adaptation for vehicle motion estimation

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IFAC-PapersOnLine 2018年第15期51卷 60-65页

作者： Marco, Vicent Rodrigo Kalkkuhl, Jens Seel, Thomas Control Systems Group Department of Electrical Engineering and Computer Science Technische Universität Berlin BerlinD-10587 Germany Research and Development Daimler AG Sindelfingen71059 Germany

In the framework of self-driving cars and driver-assistance systems the demand for reliable information about the vehicle ego-motion is increasing. This paper describes an estimation scheme, based on a nonlinear observer design, that provides velocity and attitude angle estimates. The approach relies on a state-affine representation of a kinematic model bolstered by a dynamic model-based measurement equation. By means of a thorough observability analysis, global exponential convergence is theoretically guaranteed. Additionally, in order to minimize the errors introduced by the dynamic model limitations, an observer tuning rule is proposed. The adaptation of the tuning parameters is built upon an online observability assessment of the system without the support of the dynamic model. Experimental results show that the presented approach reliably estimates the motion states. © 2018

关键词： Motion estimation Automobile drivers Automotive industry Dynamic models Kinematics Nonlinear analysis Nonlinear systems Observability Driver assistance system Estimation schemes Global exponential convergence Measurement equations Non linear observer Nonlinear observer design Observability analysis Parameter adaptation

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