1 code implementation • 15 Apr 2022 • Mahmoud Selim, Amr Alanwar, Shreyas Kousik, Grace Gao, Marco Pavone, Karl H. Johansson
Reinforcement learning (RL) is capable of sophisticated motion planning and control for robots in uncertain environments.
1 code implementation • 6 Jun 2020 • Dominik Baumann, Friedrich Solowjow, Karl H. Johansson, Sebastian Trimpe
In this paper, we propose a method that identifies the causal structure of control systems.
1 code implementation • 16 Oct 2022 • Amr Alanwar, Frank J. Jiang, Samy Amin, Karl H. Johansson
A logical zonotope, which is a new set representation for binary vectors, is introduced in this paper.
1 code implementation • 27 Mar 2024 • Ahmad Hafez, Frank J. Jiang, Karl H. Johansson, Amr Alanwar
To address this, we formulate constrained polynomial logical zonotopes, which maintain the computational efficiency and exactness of polynomial logical zonotopes for reachability analysis while supporting exact intersections.
1 code implementation • 1 May 2023 • Joana Fonseca, Sriharsha Bhat, Matthew Lock, Ivan Stenius, Karl H. Johansson
The performance of this method is evaluated through realistic simulations for an algal bloom front in the Baltic sea, using the models of the AUV and the chlorophyll a sensor.
1 code implementation • 10 Sep 2023 • Muhammad Umar B. Niazi, Michelle S. Chong, Amr Alanwar, Karl H. Johansson
When a strategic adversary can attack multiple sensors of a system and freely choose a different set of sensors at different times, how can we ensure that the state estimate remains uncorrupted by the attacker?
1 code implementation • 4 Apr 2024 • Loizos Hadjiloizou, Frank J. Jiang, Amr Alanwar, Karl H. Johansson
In this paper, we introduce a hybrid zonotope-based approach for formally verifying the behavior of autonomous systems operating under Linear Temporal Logic (LTL) specifications.
no code implementations • 6 Mar 2019 • Xinlei Yi, Xiuxian Li, Lihua Xie, Karl H. Johansson
Assuming Slater's condition, we show that the algorithm achieves smaller bounds on the constraint violation.
no code implementations • 4 Jun 2020 • Xinlei Yi, Shengjun Zhang, Tao Yang, Tianyou Chai, Karl H. Johansson
The distributed nonconvex optimization problem of minimizing a global cost function formed by a sum of $n$ local cost functions by using local information exchange is considered.
Optimization and Control
no code implementations • 11 Apr 2020 • Xingkang He, Xiaoqiang Ren, Henrik Sandberg, Karl H. Johansson
The resilience of the secured filter with detection is verified by an explicit relationship between the upper bound of the estimation error and the number of detected attacked sensors.
no code implementations • 19 Feb 2021 • Yu Xing, Xingkang He, Haitao Fang, Karl H. Johansson
The considered problem is to jointly recover the community labels of the agents and estimate interaction probabilities between the agents, based on a single trajectory of the model.
no code implementations • 9 Apr 2021 • Wanchun Liu, Daniel E. Quevedo, Karl H. Johansson, Branka Vucetic, Yonghui Li
We investigate the stability conditions for remote state estimation of multiple linear time-invariant (LTI) systems over multiple wireless time-varying communication channels.
no code implementations • 12 Apr 2021 • Matin Jafarian, Mohammad H. Mamduhi, Karl H. Johansson
We assume constant exogenous frequencies and derive sufficient conditions for achieving both stochastic phase-cohesive and phase-locked solutions, i. e., stochastic phase-cohesiveness with respect to the origin.
no code implementations • 1 May 2021 • Xinlei Yi, Xiuxian Li, Tao Yang, Lihua Xie, Tianyou Chai, Karl H. Johansson
This is a sequential decision making problem with two sequences of arbitrarily varying convex loss and constraint functions.
no code implementations • 10 May 2021 • Jianqi Chen, Jieqiang Wei, Wei Chen, Henrik Sandberg, Karl H. Johansson, Jie Chen
Undetectable attacks are an important class of malicious attacks threatening the security of cyber-physical systems, which can modify a system's state but leave the system output measurements unaffected, and hence cannot be detected from the output.
no code implementations • 22 May 2021 • Ehsan Nekouei, Henrik Sandberg, Mikael Skoglund, Karl H. Johansson
To ensure parameter privacy, we propose a filter design framework which consists of two components: a randomizer and a nonlinear transformation.
no code implementations • 9 Jun 2021 • Xinlei Yi, Xiuxian Li, Tao Yang, Lihua Xie, Tianyou Chai, Karl H. Johansson
A novel algorithm is first proposed and it achieves an $\mathcal{O}(T^{\max\{c, 1-c\}})$ bound for static regret and an $\mathcal{O}(T^{(1-c)/2})$ bound for cumulative constraint violation, where $c\in(0, 1)$ is a user-defined trade-off parameter, and thus has improved performance compared with existing results.
no code implementations • 10 Mar 2021 • Xingkang He, Yu Xing, Junfeng Wu, Karl H. Johansson
We show that given the step size, adjusting the decay speed of the triggering threshold can lead to a tradeoff between the convergence rate of the estimation error and the decay speed of the communication rate.
no code implementations • 20 Sep 2021 • Philip E. Pare, Axel Janson, Sebin Gracy, Ji Liu, Henrik Sandberg, Karl H. Johansson
We develop a layered networked spread model for a susceptible-infected-susceptible (SIS) pathogen-borne disease spreading over a human contact network and an infrastructure network, and refer to it as a layered networked susceptible-infected-water-susceptible (SIWS) model.
no code implementations • 21 Sep 2021 • Elis Stefansson, Karl H. Johansson
We present two algorithms obtaining low-complexity policies, where the first algorithm obtains a low-complexity optimal policy, and the second algorithm finds a policy maximising performance while maintaining local (stage-wise) complexity constraints.
no code implementations • 29 Sep 2021 • Jakob Nylöf, Apostolos I. Rikos, Sebin Gracy, Karl H. Johansson
It is shown that the proposed privacy-preserving resource allocation algorithm performs well with an appropriate convergence rate under privacy guarantees.
no code implementations • 1 Oct 2021 • Apostolos I. Rikos, Christoforos N. Hadjicostis, Karl H. Johansson
Motivated by these novel requirements, in this paper, we present and analyze a novel distributed average consensus algorithm, which (i) operates exclusively on quantized values (in order to guarantee efficient communication and data storage), and (ii) relies on event-driven updates (in order to reduce energy consumption, communication bandwidth, network congestion, and/or processor usage).
no code implementations • 8 Nov 2021 • Amr Alanwar, Muhammad Umar B. Niazi, Karl H. Johansson
The offline phase utilizes past input-output data to estimate a set of possible coefficients of the polynomial system.
no code implementations • 18 Feb 2022 • Alexander Johansson, Valerio Turri, Ehsan Nekouei, Karl H. Johansson, Jonas Mårtensson
The vehicles wait at the hub, and a platoon coordinator, at each time-step, decides whether to release the vehicles from the hub in the form of a platoon or wait for more vehicles to arrive.
no code implementations • 17 Jul 2022 • Apostolos I. Rikos, Christoforos N. Hadjicostis, Karl H. Johansson
Furthermore, we present topological conditions under which the proposed algorithm allows nodes to preserve their privacy.
no code implementations • 17 Jul 2022 • Apostolos I. Rikos, Christoforos N. Hadjicostis, Karl H. Johansson
In this paper, we focus on the problem of data sharing over a wireless computer network (i. e., a wireless grid).
no code implementations • 17 Jul 2022 • Apostolos I. Rikos, Gabriele Oliva, Christoforos N. Hadjicostis, Karl H. Johansson
The goal of $k$-means is to partition the network's agents in mutually exclusive sets (groups) such that agents in the same set have (and possibly share) similar information and are able to calculate a representative value for their group. During the operation of our distributed algorithm, each node (i) transmits quantized values in an event-driven fashion, and (ii) exhibits distributed stopping capabilities.
no code implementations • 25 Jul 2022 • Yuhao Yi, YuAn Wang, Xingkang He, Stacy Patterson, Karl H. Johansson
In this paper, we propose a sample-based algorithm to approximately test $r$-robustness of a digraph with $n$ vertices and $m$ edges.
no code implementations • 30 Aug 2022 • Mohammadreza Doostmohammadian, Alireza Aghasi, Apostolos I. Rikos, Andreas Grammenos, Evangelia Kalyvianaki, Christoforos N. Hadjicostis, Karl H. Johansson, Themistoklis Charalambous
This paper considers a network of collaborating agents for local resource allocation subject to nonlinear model constraints.
no code implementations • 6 Sep 2022 • Zifan Wang, Yi Shen, Zachary I. Bell, Scott Nivison, Michael M. Zavlanos, Karl H. Johansson
Specifically, the agents use the conditional value at risk (CVaR) as a risk measure and rely on bandit feedback in the form of the cost values of the selected actions at every episode to estimate their CVaR values and update their actions.
no code implementations • 10 Nov 2022 • Zishuo Li, Muhammad Umar B. Niazi, Changxin Liu, Yilin Mo, Karl H. Johansson
At each time step, the local estimates of sensors are fused by solving an optimization problem to obtain a secure estimation, which is then followed by a local detection-and-resetting process of the decentralized observers.
no code implementations • 20 Nov 2022 • Mahmoud Selim, Amr Alanwar, M. Watheq El-Kharashi, Hazem M. Abbas, Karl H. Johansson
If there is an intersection between the reachable set of the robot using the proposed action, we call the data-driven predictive controller to find the closest safe action to the proposed unsafe action.
no code implementations • 20 Nov 2022 • Apostolos I. Rikos, Wei Jiang, Themistoklis Charalambous, Karl H. Johansson
For solving this distributed optimization problem, we combine a gradient descent method with a distributed quantized consensus algorithm (which requires the nodes to exchange quantized messages and converges in a finite number of steps).
no code implementations • 29 Nov 2022 • Apostolos I. Rikos, Themistoklis Charalambous, Christoforos N. Hadjicostis, Karl H. Johansson
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.
no code implementations • 7 Dec 2022 • Elis Stefansson, Karl H. Johansson
The algorithm consists of an offline and an online step.
no code implementations • 15 Mar 2023 • Ting Bai, Yuchao Li, Karl H. Johansson, Jonas Mårtensson
We assume that a collection of charging and rest stations is given along a pre-planned route with known detours and that the problem data are deterministic.
no code implementations • 16 Mar 2021 • Pian Yu, Yulong Gao, Frank J. Jiang, Karl H. Johansson, Dimos V. Dimarogonas
It is shown that when the STL formula is robustly satisfiable and the initial state of the system belongs to the initial root node of the tTLT, it is guaranteed that the trajectory generated by the control synthesis algorithm satisfies the STL formula.
no code implementations • 23 Mar 2023 • Zifan Wang, Yulong Gao, Siyi Wang, Michael M. Zavlanos, Alessandro Abate, Karl H. Johansson
Distributional reinforcement learning (DRL) enhances the understanding of the effects of the randomness in the environment by letting agents learn the distribution of a random return, rather than its expected value as in standard RL.
no code implementations • 29 Mar 2023 • Miguel Aguiar, Amritam Das, Karl H. Johansson
We show that the proposed architecture is able to approximate the flow function by exploiting the system's causality and time-invariance.
no code implementations • 29 Mar 2023 • Elis Stefansson, Karl H. Johansson
In this paper, we consider a planning problem for a large-scale system modelled as a hierarchical finite state machine (HFSM) and develop a control algorithm for computing optimal plans between any two states.
no code implementations • 30 Mar 2023 • Zishuo Li, Anh Tung Nguyen, André Teixeira, Yilin Mo, Karl H. Johansson
To deal with such attacks, we propose the design of local estimators based on observability space decomposition, where each local estimator updates the local state and sends it to the fusion center after sampling a measurement.
no code implementations • 2 Apr 2023 • Apostolos I. Rikos, Andreas Grammenos, Evangelia Kalyvianaki, Christoforos N. Hadjicostis, Themistoklis Charalambous, Karl H. Johansson
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.
no code implementations • 1 Apr 2023 • Miguel Aguiar, Amritam Das, Karl H. Johansson
In this paper, we prove that an architecture based on discrete-time recurrent neural networks universally approximates flows of continuous-time dynamical systems with inputs.
no code implementations • 7 Apr 2023 • Muhammad Umar B. Niazi, Karl H. Johansson
In this paper, we present an observer design approach for estimating the state of nonlinear systems, without requiring any parameterization of the system's nonlinearities.
no code implementations • 18 Apr 2023 • Junsoo Kim, Jin Gyu Lee, Henrik Sandberg, Karl H. Johansson
As a result, although a portion of measurements are compromised, they can be locally identified and excluded from the state estimation, and thus the true state can be recovered.
no code implementations • 24 Apr 2023 • Yu Xing, Karl H. Johansson
Moreover, it is shown that the expected states of the agents in the same community concentrate around the initial average opinion of that community, if the weights within communities are larger than between.
no code implementations • 31 May 2023 • Xinlei Yi, Xiuxian Li, Tao Yang, Lihua Xie, Yiguang Hong, Tianyou Chai, Karl H. Johansson
Moreover, if the loss functions are strongly convex, then the network regret bound is reduced to $\mathcal{O}(\log(T))$, and the network cumulative constraint violation bound is reduced to $\mathcal{O}(\sqrt{\log(T)T})$ and $\mathcal{O}(\log(T))$ without and with Slater's condition, respectively.
no code implementations • 25 Jun 2023 • Yu Xing, Xudong Sun, Karl H. Johansson
We study joint learning of network topology and a mixed opinion dynamics, in which agents may have different update rules.
no code implementations • 13 Jul 2023 • Nicola Bastianello, Apostolos I. Rikos, Karl H. Johansson
Online distributed learning refers to the process of training learning models on distributed data sources.
no code implementations • 21 Aug 2023 • August Söderlund, Frank J. Jiang, Vandana Narri, Amr Alanwar, Karl H. Johansson
Previous approaches to predicting the future state sets of pedestrians either do not provide safety guarantees or are overly conservative.
no code implementations • 1 Sep 2023 • Nicola Bastianello, Diego Deplano, Mauro Franceschelli, Karl H. Johansson
The recent deployment of multi-agent systems in a wide range of scenarios has enabled the solution of learning problems in a distributed fashion.
no code implementations • 8 Sep 2023 • Apostolos I. Rikos, Wei Jiang, Themistoklis Charalambous, Karl H. Johansson
Distributed methods in which nodes use quantized communication yield a solution at the proximity of the optimal solution, hence reaching an error floor that depends on the quantization level used; the finer the quantization the lower the error floor.
no code implementations • 14 Sep 2023 • Linbin Huang, Dan Wang, Xiongfei Wang, Huanhai Xin, Ping Ju, Karl H. Johansson, Florian Dörfler
This paper proposes decentralized stability conditions for multi-converter systems based on the combination of the small gain theorem and the small phase theorem.
no code implementations • 10 Nov 2023 • Chuanghong Weng, Ehsan Nekouei, Karl H. Johansson
In our setup, a private process, modeled as a first-order Markov chain, derives the states of the system, and the state estimates are shared with an untrusted party who might attempt to infer the private process based on the state estimates.
no code implementations • 5 Dec 2023 • Yu Xing, Karl H. Johansson
It is shown that, when the influence of stubborn agents is small and the link probability within communities is large, an algorithm based on clustering transient agent states can achieve almost exact recovery of the communities.
no code implementations • 19 Dec 2023 • Miguel Aguiar, Amritam Das, Karl H. Johansson
We propose a framework for surrogate modelling of spiking systems.
no code implementations • 28 Jan 2024 • Mohammadreza Doostmohammadian, Alireza Aghasi, Mohammad Pirani, Ehsan Nekouei, Houman Zarrabi, Reza Keypour, Apostolos I. Rikos, Karl H. Johansson
This survey paper provides a comprehensive analysis of distributed algorithms for addressing the distributed resource allocation (DRA) problem over multi-agent systems.
no code implementations • 28 Feb 2024 • Apostolos I. Rikos, Themistoklis Charalambous, Karl H. Johansson
Our proposed algorithm is the first algorithm that achieves max-consensus in a deterministic manner (i. e., nodes always calculate the maximum of their states regardless of the nature of the probability distribution of the packet drops).
no code implementations • 26 Mar 2024 • Nicola Bastianello, Changxin Liu, Karl H. Johansson
In this paper we propose the federated private local training algorithm (Fed-PLT) for federated learning, to overcome the challenges of (i) expensive communications and (ii) privacy preservation.
no code implementations • 3 Apr 2024 • Siyi Wang, Zifan Wang, Xinlei Yi, Michael M. Zavlanos, Karl H. Johansson, Sandra Hirche
Considering non-stationary environments in online optimization enables decision-maker to effectively adapt to changes and improve its performance over time.
no code implementations • 12 Apr 2024 • Frank J. Jiang, Kaj Munhoz Arfvidsson, Chong He, Mo Chen, Karl H. Johansson
By ensuring a temporal logic tree has no leaking corners, we know the temporal logic tree correctly verifies the existence of control policies that satisfy the specified task.