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IEEE Conference on decision and control

作者： Damianos Tranos Alexandre Proutiere Division of Decision and Control Systems School of Electrical Engineering and Computer Science Royal Institute of Technology (KTH) Stockholm Sweden

ISBN: (纸本)9781665436601

We consider Markov decision Processes (MDPs) with deterministic transitions and study the problem of regret minimization, which is central to the analysis and design of optimal learning algorithms. We present logarithmic problem-specific regret lower bounds that explicitly depend on the system parameter (in contrast to previous minimax approaches) and thus, truly quantify the fundamental limit of the performance achievable by any learning algorithm. Deterministic MDPs can be interpreted as graphs and analyzed in terms of their cycles, a fact which we leverage in order to identify a class of deterministic MDPs whose regret lower bound can be determined numerically. We further exemplify this result on a deterministic line search problem, and a deterministic MDP with state-dependent rewards, whose regret lower bounds we can state explicitly. These bounds share similarities with the known problem-specific bound of the multi-armed bandit problem and suggest that navigation on a deterministic MDP need not have an effect on the performance of a learning algorithm.

关键词： Navigation Conferences Reinforcement learning Markov processes Search problems Minimization

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Distributed Event-Triggered Bandit Convex Optimization With Time-Varying Constraints

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IEEE Transactions on control of Network systems 2025年

作者： Zhang, Kunpeng Yi, Xinlei Wen, Guanghui Cao, Ming Johansson, Karl H. Chai, Tianyou Yang, Tao Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Ministry of Education Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Shanghai201210 China Southeast University School of Automation Nanjing210096 China University of Groningen Engineering and Technology Institute Groningen Faculty of Science and Engineering GroningenAG 9747 Netherlands KTH Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm10044 Sweden KTH Royal Institute of Technology Digital Futures Stockholm10044 Sweden

This paper considers the distributed bandit convex optimization problem with time-varying inequality constraints over a network of agents, where the goal is to minimize network regret and cumulative constraint violation. Existing distributed online algorithms solving this problem require that each agent broadcasts its decision to its neighbors at each iteration. However, communication resources are often limited. To better utilize communication resources, we propose a distributed event-triggered online primal-dual algorithm with two-point bandit feedback. Under several classes of appropriately chosen decreasing parameter sequences and non-increasing event-triggered threshold sequences, we establish dynamic network regret and network cumulative constraint violation bounds. These bounds are comparable to the results achieved by distributed event-triggered online algorithms with full-information feedback. Finally, a numerical example is provided to verify the theoretical results. © 2014 IEEE.

关键词： Optimization algorithms

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Optimal Input Design Through Infinity Norm Minimization Using Proximal Mapping

Optimal Input Design Through Infinity Norm Minimization Usin...

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IEEE Conference on decision and control

作者： Javad Parsa Håkan Hjalmarsson Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9781665436601

To avoid non-convexity of the criterion, various relaxations are typically used in input design. For example, the input may be assumed to be stationary and the design problem may be formulated in terms of the correlation coefficients. In this contribution, we instead propose a method to directly design the input sequence. This allows to maximize the information obtained from short-time (transient) experiments using non-stationary inputs. We do this by fitting the achieved Fisher matrix to a desired target matrix in a matrix sense, using the infinity norm. The target matrix can either be the desired Fisher matrix, obtained from quality considerations of the intended use of the model, or a matrix directly representing the performance of the application. An often used quantity is the Hessian of the so called the application cost. Thus, the method is formulated as a time domain optimization problem that is non-convex. This optimization problem is solved by alternative minimization and proximal mapping, where we split the problem in three steps where in each step, the cost function is minimized in respect to one of the variables and other variables are kept fix. We repeat these three steps until convergence. The procedure of the algorithm is summarized in a pseudo code. Finally, we illustrate our method in two numerical examples.

关键词： Correlation coefficient Costs Frequency-domain analysis Design methodology Fitting Minimization Cost function

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Higher order convergent control barrier functions for leader-follower multi-agent systems under STL tasks

arXiv

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arXiv 2021年

作者： Sharifi, Maryam Dimarogonas, Dimos V. Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

This paper presents control strategies based on time-varying convergent higher order control barrier functions for a class of leader-follower multi-agent systems under signal temporal logic (STL) tasks. Each agent is assigned a local STL task which may be dependent on the behavior of agents involved in other tasks. The leader has knowledge on the associated tasks and controls the performance of the subgroup involved agents. Robust solutions for the task satisfaction, based on the leader’s accessibility to the follower agents’ states are suggested. Our approach finds solutions to guarantee the satisfaction of STL tasks independent of the agents’ initial conditions. Copyright © 2021, The Authors. All rights reserved.

关键词： Multi agent systems

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Barrier function-based collaborative control of multiple robots under signal temporal logic tasks

arXiv

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arXiv 2021年

作者： Lindemann, Lars Dimarogonas, Dimos V. Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

Motivated by the recent interest in cyber-physical and autonomous robotic systems, we study the problem of dynamically coupled multi-agent systems under a set of signal temporal logic tasks. In particular, the satisfaction of each of these signal temporal logic tasks depends on the behavior of a distinct set of agents. Instead of abstracting the agent dynamics and the temporal logic tasks into a discrete domain and solving the problem therein or using optimization-based methods, we derive collaborative feedback control laws. These control laws are based on a decentralized control barrier function condition that results in discontinuous control laws, as opposed to a centralized condition resembling the single-agent case. The benefits of our approach are inherent robustness properties typically present in feedback control as well as satisfaction guarantees for continuous-time multi-agent systems. More specifically, time-varying control barrier functions are used that account for the semantics of the signal temporal logic tasks at hand. For a certain fragment of signal temporal logic tasks, we further propose a systematic way to construct such control barrier functions. Finally, we show the efficacy and robustness of our framework in an experiment including a group of three omnidirectional robots. Copyright © 2021, The Authors. All rights reserved.

关键词： Multi agent systems

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Experimental evaluation of sensor attacks and defense mechanisms in feedback systems

arXiv

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arXiv 2021年

作者： Umsonst, David Sandberg, Henrik Division of Decision and Control Systems School of Computer Science and Electrical Engineering KTH Royal Institute of Technology Stockholm Sweden

In this work, we evaluate theoretical results on the feasibility of, the worst-case impact of, and defense mechanisms against a stealthy sensor attack in an experimental setup. We demonstrate that for a controller with stable dynamics the stealthy sensor attack is possible to conduct and the theoretical worst-case impact is close to the achieved practical one. However, although the attack should theoretically be possible when the controller has integral action, we show that the integral action slows the attacker down and the attacker is not able to remain stealthy if it has not perfect knowledge of the controller state. In addition to that, we investigate the effect of different anomaly detectors on the attack impact and conclude that the impact under detectors with internal dynamics is smaller. Finally, we use noise injection into the controller dynamics to unveil the otherwise stealthy attacks. Copyright © 2021, The Authors. All rights reserved.

关键词： controllers

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Asymptotic Security by Model-based Incident Handlers for Markov decision Processes

Asymptotic Security by Model-based Incident Handlers for Mar...

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IEEE Conference on decision and control

作者： Hampei Sasahara Henrik Sandberg Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9781665436601

This study investigates general model-based incident handler’s asymptotic behaviors against cyber attacks to control systems. The attacker’s and the defender’s dynamic decision making is modeled as an equilibrium of a dynamic signaling game. It is shown that the defender’s belief on existence of an attacker converges over time for any attacker’s strategy provided that the stochastic dynamics of the control system is known to the defender. This fact implies that the rational behavior of the attacker converges to a harmless action as long as the defender possesses an effective counteraction. The obtained result supports the powerful protection capability achieved by model-based defense mechanisms.

关键词： Conferences decision making Games Markov processes control systems Security Cyberattack

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Adaptive Estimation for Environmental Monitoring Using an Autonomous Underwater Vehicle

Adaptive Estimation for Environmental Monitoring Using an Au...

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IEEE Conference on decision and control

作者： Ziwen Yang Joana Fonseca Shanying Zhu Cailian Chen Xinping Guan Karl H. Johansson Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Digital Futures

This paper considers the problem of monitoring and adaptively estimating an environmental field, such as temperature or salinity, using an autonomous underwater vehicle (AUV). The AUV moves in the field and persistently measures environmental scalars and its position in its local coordinate frame. The environmental scalars are approximately linearly distributed over the region of interest, and an adaptive estimator is designed to estimate the gradient. By orthogonal decomposition of the velocity of the AUV, a linear time-varying system is equivalently constructed, and the sufficient conditions on the motion of the AUV are established, under which the global exponential stability of the estimation error system is rigorously proved. Furthermore, an estimate of the exponential convergence rate is given, and a reference trajectory that maximizes the estimate of the convergence rate is obtained for the AUV to track. Numerical examples verify the stability and efficiency of the system.

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On the confidentiality of the reference signal under sensor attacks

On the confidentiality of the reference signal under sensor ...

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IEEE Conference on decision and control

作者： David Umsonst Henrik Sandberg Division of Decision and Control Systems School of Electrical Engineering and Computer Science at the KTH Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9781665436601

In this paper, we investigate the confidentiality of the reference signal of a feedback system under sensor attacks. In particular, we analyze the conditions for when an attacker with perfect model knowledge and access to the sensor measurements can obtain an unbiased estimate of the reference signal in a feedback control system such that the estimate’s error covariance converges to zero. We call such an estimate a perfect estimate. Under the assumption of linear dynamics for the plant, the controller, and the reference signal, we show that the attacker can perfectly estimate the reference signal if and only if the reference dynamics do not have eigenvalues outside the unit circle. This implies that an attacker is able to perfectly estimate common reference signals such as step functions and sinusoidal signals from noisy measurements. The convergence rate of the estimate is, however, not exponentially fast for common reference signals and depends on the reference signal to be estimated as well as the controller used. We verify our results numerically with a simulation of a three-tank system.

关键词： Sufficient conditions Estimation error Measurement uncertainty Particle measurements Eigenvalues and eigenfunctions Robustness Numerical models

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A Sample-Based Algorithm for Approximately Testing r-Robustness of a Digraph

A Sample-Based Algorithm for Approximately Testing r-Robustn...

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IEEE Conference on decision and control

作者： Yuhao Yi Yuan Wang Xingkang He Stacy Patterson Karl H. Johansson Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Digital Futures Stockholm Sweden Department of Electrical Engineering University of Notre Dame Notre Dame IN USA Department of Computer Science Rensselaer Polytechnic Institute Troy NY USA

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

One of the intensely studied concepts of network robustness is r-robustness, which is a network topology property quantified by an integer r. It is required by mean subsequence reduced (MSR) algorithms and their variants to achieve resilient consensus. However, determining r-robustness is intractable for large networks. In this paper, we propose a sample-based algorithm to approximately test r-robustness of a digraph with n vertices and m edges. For a digraph with a moderate assumption on the minimum in-degree, and an error parameter 0

关键词： Network topology Approximation algorithms Robustness Testing

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