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Distributed Optimization for Quadratic Cost Functions over Large-Scale Networks with Quantized Communication and Finite-Time Convergence

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arXiv 2023年

作者： Rikos, Apostolos I. Grammenos, Andreas Kalyvianaki, Evangelia Hadjicostis, Christoforos N. Charalambous, Themistoklis Johansson, Karl H. The Division of Decision and Control Systems KTH Royal Institute of Technology StockholmSE-100 44 Sweden The Department of Computer Science and Technology University of Cambridge Cambridge United Kingdom The Alan Turing Institute London United Kingdom The Department of Electrical and Computer Engineering School of Engineering University of Cyprus Nicosia1678 Cyprus

We propose two distributed iterative algorithms that can be used to solve, in finite time, the distributed optimization problem over quadratic local cost functions in large-scale networks. The first algorithm exhibits synchronous operation whereas the second one exhibits asynchronous operation. Both algorithms share salient features. Specifically, the algorithms operate exclusively with quantized values, which means that the information stored, processed and exchanged between neighboring nodes is subject to deterministic uniform quantization. The algorithms rely on event-driven updates in order to reduce energy consumption, communication bandwidth, network congestion, and/or processor usage. Finally, once the algorithms converge, nodes distributively terminate their operation. We prove that our algorithms converge in a finite number of iterations to the exact optimal solution depending on the quantization level, and we present applications of our algorithms to (i) optimal task scheduling for data centers, and (ii) global model aggregation for distributed federated learning. We provide simulations of these applications to illustrate the operation, performance, and advantages of the proposed algorithms. Additionally, it is shown that our proposed algorithms compare favorably to algorithms in the current literature. Quantized communication and asynchronous updates increase the required time to completion, but finite-time operation is maintained. © 2023, CC BY.

关键词： Cost functions

Explicit Time-Optimal Speed Profiles for Planar Paths with Monotone Curvature

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arXiv 2022年

作者： Selvaratnam, Daniel Cantoni, Michael Manzie, Chris Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden Department of Electrical and Electronic Engineering The University of Melbourne ParkvilleVIC3010 Australia

Minimum-time speed profiles are constructed for planar paths with smooth strictly-monotonic signed curvature, subject to constraints on velocity, normal acceleration and tangential acceleration. The construction is explicit and exact, and global optimality is rigorously established from first principles under mild regularity conditions on the path. Free, fixed, and inequality-constrained boundary speeds are all accommodated. Numerical implementation is straightforward. © 2022, CC BY-NC-ND.

关键词： Acceleration

Observer-Based control of Second-Order Multi-vehicle systems in Bearing-Persistently Exciting Formations

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Observer-Based Control of Second-Order Multi-vehicle Systems...

IEEE Conference on decision and control

作者： Zhiqi Tang Baris Fidan Karl H. Johansson Jonas Mårtensson Tarek Hamel Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Mechanical and Mechatronics Engineering Department University of Waterloo ON Canada I3S-UCA CNRS Université Côte d’Azur & Institut Universitaire de France (IUF) France

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This paper proposes an observer-based formation tracking control approach for multi-vehicle systems with second-order motion dynamics, assuming that vehicles’ relative or global position and velocity measurements are unavailable. It is assumed that all vehicles are equipped with sensors capable of sensing the bearings relative to neighboring vehicles and only one leader vehicle has access to its global position. Each vehicle estimates its absolute position and velocity using relative bearing measurements and the estimates of neighboring vehicles received over a communication network. A distributed observer-based controller is designed, relying only on bearing and acceleration measurements. This work further explores the concept of the Bearing Persistently Exciting (BPE) formation by proposing new algorithms for bearing-based localization and state estimation of second-order systems in centralized and decentralized manners. It also examines conditions on the desired formation to guarantee the exponential stability of distributed observer-based formation tracking controllers. In support of our theoretical results, some simulation results are presented to illustrate the performance of the proposed observers as well as the observer-based tracking controllers.

关键词： Tracking Stability Simulation Dynamics Observers Position measurement control theory Sensors Velocity measurement Vehicle dynamics

Distributed Optimal Data Allocation in Finite Time with Efficient Communication and Transmission Stopping over Dynamic Networks

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Distributed Optimal Data Allocation in Finite Time with Effi...

IEEE Conference on decision and control

作者： Apostolos I. Rikos Christoforos N. Hadjicostis Karl H. Johansson Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden Digital Futures Stockholm Sweden Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

In this paper, we focus on the problem of data sharing over a wireless computer network (i.e., a wireless grid). Given a set of available data, we present a distributed algorithm, which operates over a dynamically changing network and allows each node to calculate the optimal allocation of data in a finite number of time steps. We show that our proposed algorithm (i) converges to the optimal solution in finite time with very high probability, and (ii) once the optimal solution is reached, each node is able to cease transmissions without needing knowledge of a global parameter such as the network diameter. Furthermore, our algorithm (i) operates exclusively with quantized values (i.e., each node processes and transmits quantized information), (ii) relies on event-driven updates, and (iii) calculates the optimal solution in the form of a quantized fraction which avoids errors due to quantization. Finally, we demonstrate the operation, performance, and potential advantages of our algorithm over random dynamic networks.

关键词： Wireless communication Knowledge engineering Quantization (signal) Heuristic algorithms Distributed databases Dynamic scheduling computer networks

A State Feedback controller for Mitigation of Continuous-Time Networked SIS Epidemics

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arXiv 2022年

作者： Wang, Yuan Gracy, Sebin Uribe, César A. Ishii, Hideaki Johansson, Karl Henrik Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology and Digital Futures Stockholm Sweden Department of Electrical and Computer Engineering Rice University HoustonTX United States Department of Computer Science Tokyo Institute of Technology Yokohama Japan

The paper considers continuous-time networked susceptible-infected-susceptible (SIS) diseases spreading over a population. Each agent represents a sub-population and has its own healing rate and infection rate;the state of the agent at a time instant denotes what fraction of the said sub-population is infected with the disease at the said time instant. By taking account of the changes in behaviors of the agents in response to the infection rates in real-time, our goal is to devise a feedback strategy such that the infection level for each agent strictly stays below a pre-specified value. Furthermore, we are also interested in ensuring that the closed-loop system converges either to the disease-free equilibrium or, when it exists, to the endemic equilibrium. The upshot of devising such a strategy is that it allows health administration officials to ensure that there is sufficient capacity in the healthcare system to treat the most severe cases. We demonstrate the effectiveness of our controller via numerical examples. © 2022, CC BY.

关键词： Closed loop systems

Distributed Computation of Exact Average Degree and Network Size in Finite Number of Steps under Quantized Communication

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arXiv 2022年

作者： Rikos, Apostolos I. Charalambous, Themistoklis Hadjicostis, Christoforos N. Johansson, Karl H. The Division of Decision and Control Systems KTH Royal Institute of Technology StockholmSE-100 44 Sweden The Department of Electrical and Computer Engineering University of Cyprus Nicosia1678 Cyprus

We consider the problems of computing the average degree and the size of a given network in a distributed fashion under quantized communication. We present two distributed algorithms which rely on quantized operation (i.e., nodes process and transmit quantized messages), and are able to calculate the exact solutions in a finite number of steps. Furthermore, during the operation of our algorithms, each node is able to determine in a distributed manner whether convergence has been achieved and thus terminate its operation. To the best of the authors’ knowledge these algorithms are the first to find the exact solutions under quantized communication (i.e., there is no error in the final calculation). Additionally, note that our network size calculation algorithm is the first in the literature which calculates the exact size of a network in a finite number of steps without introducing a final error. This error in other algorithms can be either due to quantization or asymptotic convergence. In our case, no error is introduced since the desired result is calculated in the form of a fraction involving a quantized numerator and a quantized denominator. Finally, we demonstrate the operation of our algorithms and their potential advantages. © 2022, CC BY.

关键词： Errors

Learning For Predictive control: A Dual Gaussian Process Approach

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arXiv 2022年

作者： Liu, Yuhan Wang, Pengyu Tóth, Roland Control Systems Group Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands Flight Dynamics and Control Laboratory Department of Aerospace Engineering Korea Advanced Institute of Science and Technology Daejeon Korea Republic of Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

An important issue in model-based control design is that an accurate dynamic model of the system is generally nonlinear, complex, and costly to obtain. This limits achievable control performance in practice. Gaussian process (GP) based estimation of system models is an effective tool to learn unknown dynamics directly from input/output data. However, conventional GP-based control methods often ignore the computational cost associated with accumulating data during the operation of the system and how to handle forgetting in continuous adaption. In this paper, we present a novel Dual Gaussian Process (DGP) based model predictive control (MPC) strategy that enables efficient use of online learning based predictive control without the danger of catastrophic forgetting. The bio-inspired DGP structure is a combination of a long-term GP and a short-term GP, where the long-term GP is used to keep the learned knowledge in memory and the short-term GP is employed to rapidly compensate unknown dynamics during online operation. Furthermore, a novel recursive online update strategy for the short-term GP is proposed to successively improve the learnt model during online operation. Effectiveness of the proposed strategy is demonstrated via numerical simulations. © 2022, CC BY-NC-SA.

关键词： Model predictive control

Symbiotic control of Uncertain Dynamical systems*

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Symbiotic Control of Uncertain Dynamical Systems*

IEEE Conference on decision and control

作者： Tansel Yucelen Selahattin Burak Sarsılmaz Emre Yildirim Department of Mechanical Engineering Laboratory for Autonomy Control Information and Systems (LACIS http://lacis.eng.usf.edu/) University of South Florida Tampa Florida United States of America Department of Electrical and Computer Engineering Utah State University Logan Utah United States of America

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

In this paper, we consider both the fixed-gain control and adaptive learning architectures to suppress the effects of uncertainties. We note that fixed-gain control provides more predictable closed-loop system behavior, but it comes at the cost of knowing uncertainty bounds. On the other hand, adaptive learning removes the requirement of this knowledge at the expense of less predictable closed-loop system behavior compared to fixed-gain control. To this end, this paper presents a novel symbiotic control framework that integrates the advantages of both fixed-gain control and adaptive learning architectures. In particular, the proposed framework utilizes both control architectures to suppress the negative effects of uncertainties with more predictable closed-loop system behavior and without the knowledge of uncertainty bounds. Both parametric and nonparametric uncertainties are considered, where we use neural networks to approximate the unknown uncertainty basis for the latter case. Several illustrative numerical examples are provided to demonstrate the efficacy of the proposed approach.

关键词： Symbiosis Hands Adaptive learning Uncertainty Upper bound Costs Neurons Closed loop systems Dynamical systems Biological neural networks

Reinforcement Learning-Based Optimal Multiple Waypoint Navigation

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Reinforcement Learning-Based Optimal Multiple Waypoint Navig...

IEEE International Conference on Robotics and Automation (ICRA)

作者： Christos Vlachos Panagiotis Rousseas Charalampos P. Bechlioulis Kostas J. Kyriakopoulos Department of Electrical and Computer Engineering University of Patras Greece School of Mechanical Engineering Control Systems Laboratory National Technical University of Athens Greece Center of AI & Robotics (CAIR) New York University Abu Dhabi

In this paper, a novel method based on Artificial Potential Field (APF) theory is presented, for optimal motion planning in fully-known, static workspaces, for multiple final goal configurations. Optimization is achieved through a Reinforcement Learning (RL) framework. More specifically, the parameters of the underlying potential field are adjusted through a policy gradient algorithm in order to minimize a cost function. The main novelty of the proposed scheme lies in the method that provides optimal policies for multiple final positions, in contrast to most existing methodologies that consider a single final configuration. An assessment of the optimality of our results is conducted by comparing our novel motion planning scheme against a RRT* method.

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