Presents the table of contents for this issue of the publication.

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

Presents the table of contents for this issue of the publication.

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

Presents the table of contents for this issue of the publication.

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

Presents the table of contents for this issue of the publication.

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

This article deals with the codesign of an output-dependent switching function and a full-order affine filter for discrete-time switched affine systems. More specifically, from the measured output, the switched filter has the role of providing essential information for the switching function, which must assure global practical stability of a desired equilibrium point. The design conditions are based

In this article, we address the problem of inferring direct influences in social networks from partial samples of a class of opinion dynamics. The interest is motivated by the study of several complex systems arising in social sciences, where a population of agents interacts according to a communication graph. These dynamics over networks often exhibit an oscillatory behavior, given the stochastic

We show that the pair (rotor position, resistance) of a non-salient Permanent Magnet Synchronous Motor (PMSM) is not observable in the sensorless setting, namely when only the electrical signals (voltages and intensities) are measured. However, if the rotation speed ω and the current in rotating frame id are simultaneously non zero, we prove that there can only exist six indistinguishable solutions

Motivated by (approximate) dynamic programming and model predictive control problems, we analyse the stability of deterministic nonlinear discrete-time systems whose inputs minimize a discounted finite-horizon cost. We assume that the system satisfies stabilizability and detectability properties with respect to the stage cost. Then, a Lyapunov function for the closed-loop system is constructed and

Autonomous optimization refers to the design of feedback controllers that steer a physical system to a steady state that solves a predefined, possibly constrained, optimization problem. As such, no exogenous control inputs such as setpoints or trajectories are required. Instead, these controllers are modeled after optimization algorithms that take the form of dynamical systems. The interconnection

While the classic Vickrey-Clarke-Groves mechanism ensures incentive compatibility for a static one-shot game, it does not appear to be feasible to construct a dominant truth-telling mechanism for agents that are stochastic dynamic systems. The contribution of this paper is to show that for a set of LQG agents a mechanism consisting of a sequence of layered payments over time decouples the intertemporal

We consider the problem of designing distributed controllers to guarantee dissipativity of a networked system comprised of dynamically coupled subsystems. We require that the control synthesis is carried out locally at the subsystem-level, without explicit knowledge of the dynamics of other subsystems in the network. We solve this problem in two steps. First, we provide distributed subsystem-level

In this paper, we present a nonlinear robust model predictive control (MPC) framework for general (state and input dependent) disturbances. This approach uses an online constructed tube in order to tighten the nominal (state and input) constraints. To facilitate an efficient online implementation, the shape of the tube is based on an offline computed incremental Lyapunov function with a corresponding

In this work we formulate the problem of satisfying a linear temporal logic formula on a linear plant with output feedback, through a recent hybrid systems formalism. We relate this problem to the notion of recurrence introduced for the considered formalism, and we then extend Lyapunov-like conditions for recurrence of an open, unbounded set. One of the proposed relaxed condition allows certifying

The problem of quickly diagnosing an unknown change in a stochastic process is studied. We establish novel bounds on the performance of misspecified diagnosis algorithms designed for changes that differ from those of the process, and pose and solve a new robust quickest change diagnosis problem in the asymptotic regime of few false alarms and false isolations. Simulations suggest that our asymptotically

This paper addresses the behavior analysis problems for directed signed networks that involve cooperative-antagonistic interactions among agents. Of particular interest is to explore the convergence behaviors of directed signed networks with agents of mixed first-order and second-order dynamics. Further, the agents are subject to nonidentical topologies represented by two different signed digraphs

In this work, we consider the controllability of a discrete-time linear dynamical system with sparse control inputs. Sparsity constraints on the input arises naturally in networked systems, where activating each input variable adds to the cost of control. We derive algebraic necessary and sufficient conditions for ensuring controllability of a system with an arbitrary transfer matrix. The derived conditions

This paper studies an optimal dynamic formation problem for heterogeneous affine nonlinear systems. The nonidenticality in agents and the requirement for dynamic spatial reconfiguration, make it a challenging task to coordinate different types of agents to maintain an optimized formation shape. In an architecture of event-triggered decision and control, this paper investigates how to fulfill dynamic

We propose a constructive design method for linear systems with a non-trivial drift term, guaranteeing robust global asymptotic stability of the origin of the closed-loop system, as well as robust obstacle avoidance. To obtain discontinuous input actions, our controller is designed in the framework of hybrid systems. Using our proposed hybrid controller, we demonstrate that solutions do not enter a

Explicit communication channels in the feedback loop introduce new key elements such as channel input power, channel variable gain, channel bandwidth, additive channel noise, transmission rate, quantization, packet losses, etc. In this work, we propose a stationary signal-to-noise ratio (SNR) analysis for the fading channel model. The channel SNR can be connected with most communication issues such

We propose a redesign paradigm for stable estima-tors by introducing a saturation or a dead-zone nonlinearity with adaptive thresholds on the output injection term. Such nonlin-earities allow improving the sensitivity to measurement noise in different scenarios (impulsive disturbances or persistent noise such as sensor bias), while preserving the asymptotic convergence properties of the original observer

This paper considers the problem of output feedback control for non-square multi-input multi-output systems with arbitrary relative degree. The proposed controller, based on the L1 adaptive control architecture, is designed using the right interactor matrix and a suitably defined projection matrix. A state-output predictor, a low-pass filter, and adaptive laws are introduced that achieve output tracking

This paper studies the infinite-horizon adaptive optimal control of continuous-time linear periodic (CTLP) systems. A novel value iteration (VI) based off-policy ADP algorithm is proposed for a general class of CTLP systems, so that approximate optimal solutions can be obtained directly from the collected data, without the exact knowledge of system dynamics. Under mild conditions, the proofs on uniform

In this paper, we study the impact of stealthy attacks on the Cyber-Physical System (CPS) modeled as a stochastic linear system. An attack is characterised by a malicious injection into the system through input, output or both, and it is called stealthy (resp.~strictly stealthy) if it produces bounded changes (resp.~no changes) in the detection residue. Correspondingly, a CPS is called vulnerable (resp

We consider a dynamic traffic routing game over an urban road network involving a large number of drivers in which each driver selecting a particular route is subject to a penalty that is affine in the logarithm of the number of drivers selecting the same route. We show that the mean-field approximation of such a game leads to the so-called linearly solvable Markov decision process, implying that its

Contagious processes, such as spread of infectious diseases, social behaviors, or computer viruses, affect biological, social, and technological systems. Epidemic models for large populations and finite populations on networks have been used to understand and control both transient and steady-state behaviors. Typically it is assumed that after recovery from an infection, every agent will either return

Cloud computing platforms are being increasingly used for closing feedback control loops, especially when computationally expensive algorithms, such as model-predictive control, are used to optimize performance. Outsourcing of control algorithms entails an exchange of data between the control system and the cloud, and, naturally, raises concerns about the privacy of the control system's data (e.g.

We consider time synchronization attack against multi-system scheduling in a remote state estimation scenario where a number of sensors monitor different linear dynamical processes and schedule their transmissions through a shared collision channel. We show that by randomly injecting relative time offsets on the sensors, the malicious attacker is able to make the expected estimation error covariance

This work presents a fairly complete account on various topological and metrical aspects of feedback stabilization for single-input-single-output (SISO) continuous and discrete time linear-time-invariant (LTI) systems. In particular, we prove that the set of stabilizing output feedback gains for a SISO system with n states has at most $\lceil{\frac{n}{2}}\rceil$ connected components. Furthermore, our

This technique note aims at distributed design for nuclear norm (the sum of all singular values) minimization with linear operator equality constraints over a multi-agent network. The problem is reformulated as a standard distributed structure by introducing substitutional variables. Based on projected primal-dual method and derivative feedback techniques, a distributed continuous-time algorithm for

In this paper, we consider an online distributed composite optimization problem over a time-varying multi-agent network that consists of multiple interacting nodes, where the objective function of each node consists of two parts: a loss function that changes over time and a regularization function. This problem naturally arises in many real-world applications ranging from wireless sensor networks to

This paper proposes a data-driven iterative feedback control method to efficiently solve finite time horizon, nonlinear, input constrained optimal control problems. The proposed method introduces a novel approach to combine an inexact system model with measured state information to reduce the cost and provide near-optimal control by approximately solving the optimal control problem along the trajectory

In this article, the reachability analysis is investigated for the linear discrete-time systems in the presence of malicious cyber attacks. Moreover, the system disturbance and measurement noise are assumed to be unknown-but-bounded (UBB). Then, the attack model is built and a novel attack detector, which depends on the estimate residue is developed. Based on the stealthy cyber-attack set, reachability

Reinforcement learning (RL) methods have demonstrated their efficiency in simulation environments. However, many applications for which RL offers great potential, such as autonomous driving, are also safety critical and require a certified closed-loop behavior in order to meet safety specifications in the presence of physical constraints. This paper introduces a concept, called probabilistic model

Despite the increasing interest in multi-agent reinforcement learning (MARL) in multiple communities, understanding its theoretical foundation has long been recognized as a challenging problem. In this work, we address this problem by providing a finite-sample analysis for decentralized batch MARL with networked agents. Specifically, we consider two decentralized MARL settings, where teams of agents

We propose a new method to obtain feedback controllers of an unknown dynamical system directly from noisy input/state data. The key ingredient of our design is a new matrix S-lemma that will be proven in this paper. We provide both strict and non-strict versions of this S-lemma, that are of interest in their own right. Thereafter, we will apply these results to data-driven control. In particular, we

We consider aggregative games with affine coupling constraints, where agents have partial information on the aggregate value and can only communicate with neighbouring agents. We propose a single-layer distributed algorithm that reaches a variational generalized Nash equilibrium, under constant step sizes. The algorithm works on a single timescale, i.e., does not require multiple communication rounds

This paper focuses on synthesizing control policies for discrete-time stochastic control systems together with a lower bound on the probability that the systems satisfy the complex temporal properties. The desired properties of the system are expressed as linear temporal logic (LTL) specifications over finite traces. In particular, our approach decomposes the given specification into simpler reachability

In this article we introduce an approach for modeling and analyzing collective behavior of a group of agents using moments. We represent the group of agents via their distribution and derive a method to estimate the dynamics of the moments. We use this to predict the evolution of the distribution of agents by first computing the moment trajectories and then use this to reconstruct the distribution

This article considers the problem of selecting sensors in a large-scale system to minimize the error in estimating its states, more specifically, the state estimation mean-square error (MSE) and worst-case error for Kalman filtering and smoothing. Such selection problems are in general NP-hard, i.e., their solution can only be approximated in practice even for moderately large problems. Due to its

We consider the problem of controlling the precision of the multiple-model multiple-hypothesis filter with Gaussian mixture reduction. The controller adaptively chooses the number of hypotheses kept by the filter to (sub)optimally seek a tradeoff between filter precision and computational effort. In order to quantify the approximation error due to hypotheses truncation, the controller employs probability

This article presents tools for the design of control laws inducing robust controlled forward invariance of a set for hybrid dynamical systems modeled as hybrid inclusions. A set has the robust controlled forward invariance property via a control law if every solution to the closed-loop system that starts from the set stays within the set for all future time, regardless of the value of the disturbances

In this article, we propose a passivity-based methodology for the analysis and design of reinforcement learning dynamics and algorithms in multiagent finite games. Starting from a known, first-order reinforcement learning scheme, we show that convergence to a Nash distribution can be attained in a broader class of games than previously considered in the literature—namely, in games characterized by

In this article, we consider the minimum data rate problem for linear system stabilization under noiseless communication channels. Previous results indicated that having a data rate very approaching the entropy bound leads to large delays and data buffer sizes. In analogy, the entropy bound in Shannon's source coding theorem in traditional information theory displays this behavior, where the data rate

We consider a multiagent framework for distributed optimization where each agent has access to a local smooth strongly convex function, and the collective goal is to achieve consensus on the parameters that minimize the sum of the agents’ local functions. We propose an algorithm wherein each agent operates asynchronously and independently of the other agents. When the local functions are strongly convex

In this article, we consider the synchronization problem in a network of nonlinear multiagent systems having the same relative degree $r$. The agents do not have access to their state or output and only have relative output information from their neighbors. The controller structure is decentralized in nature and, therefore, depends only on the relative information available to it. The agent dynamics

This article studies the event-triggered control problem for nonholonomic systems in the chained form with disturbances and drift uncertain nonlinearities. Both the cases of state-feedback and output-feedback are investigated. To address the effects of nonholonomic constraints in event-triggered control, a new systematic design integrating a state-scaling technique and set-valued maps are proposed

We consider remote state estimation in the presence of an active eavesdropper. A sensor forward local state estimates to a remote estimator over a network, which may be eavesdropped by an intelligent adversary. Aiming at improving the eavesdropping performance efficiently, the adversary may adaptively alternate between an eavesdropping and an active mode. In contrast to eavesdropping, the active attack

This article proposes a relax-and-discretize approach for optimal control of continuous-time differential algebraic systems. It works by relaxing the algebraic equations and penalizing the violation into the objective function using the augmented Lagrangian, which converts the original problem into a sequence of optimal control problems (OCPs) of ordinary differential equations (ODEs). The relax-and-discretize

The boundary stabilization problem of a star-shaped type II thermoelastic system is considered, where there is a nonuniform bounded disturbance at the control end. A time-varying extended state observer is proposed to estimate and cancel the disturbance, thereby largely removing the uncertainties and simplifying the feedback control law design. The exponential stability is shown for the extended state

In this article, we extend the stability results of cascaded closed-loop predictors for deterministic systems to stochastic linear systems with additive noise and arbitrarily large time-varying input delay, under suitable hypothesis on the delay function. We propose an output-feedback controller design based on a set of differential delay equations with size depending on the delay bound and we show