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In bioprocesses, it is important to model the kinetics of the macroscopic rates of reactions since these are required to catch the dynamical aspects of a process. In [Wang et al. 2020], a modeling method involving Gaussian processes has been developed, using a kernel especially designed for the modeling of Monod-type kinetics (activation, inhibition, double component, neutral effect). However, as will be illustrated in this paper, when the number of training data is limited or the metabolite concentration data do not have large variations (which is generally the case for real-life data), this kernel can yield inaccurate models for the kinetics. In this paper, we develop a new kernel better tailored for the modeling of Monod-type kinetics and we show that it has good modeling performances in the case of a limited number of data. The idea is to use the particular structure of Monod-type functions in the design of the kernel, i.e., we incorporate prior knowledge in the modeling., QC 20221110

We consider a cooperative system identification scenario in which an expert agent (teacher) knows a correct, or at least a good, model of the system and aims to assist a learner-agent (student), but cannot directly transfer its knowledge to the student. For example, the teacher's knowledge of the system might be abstract or the teacher and student might be employing different model classes, which renders the teacher's parameters uninformative to the student. In this paper, we propose correctional learning as an approach to the above problem: suppose that in order to assist the student, the teacher can intercept the observations collected from the system and modify them to maximize the amount of information the student receives about the system. We formulate a general solution as an optimization problem, which for a multinomial system instantiates itself as an integer program. Furthermore, we obtain finite-sample results on the improvement that the assistance from the teacher results in (as measured by the reduction in the variance of the estimator) for a binomial system. In numerical experiments, we illustrate the proposed algorithms and verify the theoretical results that have been derived in the paper., QC 20211022

The paper presents the problem of distributing potential games over communication graphs. Suppose a potential game can be designed for a group of agents (players) where each has access to all others' actions (strategies). The paper shows how to design a corresponding potential game for these agents if the full information assumption is replaced with communication over a network depicted by undirected graphs with certain properties. A state-based formulation for potential games is utilized. This provides degrees of freedom to handle the previous information limitation. Notions of Nash's equilibria for the developed game (called here distributed potential game) are presented, and relations between these equilibria and those of the full information game are studied. In part II of the paper learning Nash equilibria for the newly developed game is studied. The development focuses on providing a way to utilize available algorithms of the full information game. The motivation for the results comes from a platoon matching problem for heavy duty vehicles. Utilizing the newly developed distributed game, recent results based on potential games can be extended, providing a basis for an on-the-go strategy where platoon matching on road networks can be solved locally., QC 20210802

This study deals with security issues in dynamical networked control systems. The goal is to establish a unified framework of the attack detection stage, which includes the four processes of monitoring the system state, making a decision based on the monitored signal, disconnecting the corrupted subsystem, and operating the remaining system during restoration. This paper, in particular, considers a disconnection-aware attack detector design problem. Traditionally, observer-based attack detectors are designed based on the system model with a fixed network topology and cannot cope with a change of the topology caused by disconnection. The disconnection-aware design problem is mathematically formulated and a solution is proposed in this paper. A numerical example demonstrates the effectiveness of the proposed detector through an inverter-based voltage control system in a benchmark model., QC 20210629

This paper presents methods for experimental design, control, and optimization of perfusion bioreactors as well as the vision for their integration. After describing a generic model of perfusion bioreactors, the paper proposes a control scheme via rate estimation and feedback linearization with useful properties with respect to steady-state error, stability, and performance. The paper also introduces data-driven and hybrid procedures for experimental design that are tailored to the intended use of the model for steady-state optimization. Lastly, the methods are illustrated via a simulation example of a perfusion bioreactor., QC 20210720

Synthetic gene circuit behaviour can be qualitatively different from the intended one due to effects from unintended interactions with the host cell. Such unintended interactions are essentially inevitable, since the circuits rely on the cellular resources in the host cell. The paper considers the feasibility of reconstructing such unintended interactions from time-series perturbation experiments. Identifying the specific interactions with the host cell is a prerequisite to mitigate their effects on the circuit behaviour. Previously proposed methods either cannot distinguish such unintended interactions from the intended direct interactions between circuit genes or require perturbing all the circuit genes by unique perturbations in separate experiments, which typically imply very costly experiments. We show herein that it in principle is feasible to identify all unintended interactions with the host cell from one single perturbation experiment. We also discuss fundamental limitations of steady-state data. The results are illustrated using a recently implemented three-gene cascade synthetic circuit in E. coli., QC 20230425

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. Copyright, QC 20230309

In this paper, we analyze the problem of optimally allocating resources in a distributed and privacy-preserving manner. We propose a novel distributed optimal resource allocation algorithm with privacy-preserving guarantees, which operates over a directed communication network. Our algorithm converges in finite time and allows each node to process and transmit quantized messages. Our algorithm utilizes a distributed quantized average consensus strategy combined with a privacy-preserving mechanism. We show that the algorithm converges in finite-time, and we prove that, under specific conditions on the network topology, nodes are able to preserve the privacy of their initial state. Finally, to illustrate the results, we consider an example where test kits need to be optimally allocated proportionally to the number of infections in a region. It is shown that the proposed privacy-preserving resource allocation algorithm performs well with an appropriate convergence rate under privacy guarantees. Copyright, QC 20230309

In this paper a platoon-actuated mainstream traffic control is proposed to decongest bottlenecks due to recurrent and nonrecurrent events. Indeed, differently from traditional mainstream control strategies, i.e., control strategies applied with fixed actuators, platoon-actuated control can be applied at any location on the freeway. In this work, the control actions to be communicated to the platoons, i.e., speed and configuration, are defined by means of a predictive control law based on traffic and platoon state detected in an area identified immediately upstream of the bottleneck. The main peculiarity of this scheme is that the size of the controlled area is dynamically adjusted based on the predicted congestion at the bottleneck. This approach keeps the control law computation burden low, while not sacrificing much control performance. Specifically, the number of platoons to be controlled and the time at which the platoons begin to be controlled depend on the size of the controlled area. Simulation results reported in the paper show the effectiveness of the proposed scheme, eliminating from 60% to 80% of the delay incurred from congestion compared with the uncontrolled case, depending on the level of traffic., QC 20230125

In this paper, we analyse the consistency of the Instrumental-Variable-based State Variable Filter (IVSVF) estimator by taking into account the intersample behaviour of the input and output signals. It is found that when only sampled input and output data are available for estimation, the IVSVF estimator is not consistent for any fixed sampling period due to the interpolation error that arises from constructing the filtered output. A Bias-Eliminated IVSVF (BEIVSVF) estimator is then proposed and shown to be consistent. The theoretical results developed in the paper are also discussed from a practical standpoint. Simulations are performed to verify the performance of the proposed method as well as to support the theoretical results., QC 20220830

This paper proposes a novel discrete-time multi-virus SIR (susceptible-infected-recovered) model that captures the spread of competing SIR epidemics over a population network. First, we provide a sufficient condition for the infection level of all the viruses over the networked model to converge to zero in exponential time. Second, we propose an observation model which captures the summation of all the viruses' infection levels in each node, which represents the individuals who are infected by different viruses but share similar symptoms. We present a sufficient condition for the model to be locally observable. We propose a Luenberger observer for the system state estimation and show via simulations that the estimation error of the Luenberger observer converges to zero before the viruses die out. Copyright, QC 20220930

The paper proposes a modular-based approach to constraint handling in process optimization and control. This is partly motivated by the recent interest in learning-based methods, e.g., within bioproduction, for which constraint handling under uncertainty is a challenge. The proposed constraint handler, called predictive filter, is combined with an adaptive constraint margin to minimize the cost of violating soft constraints due to uncertainty and disturbances. The module can be combined with any controller and is based on modifying the controller output, in a least squares sense, such that constraints are satisfied within the considered horizon. The proposed method is computationally efficient and suitable for real-time applications. The effectiveness of the method is illustrated by a simple heater example and a nonlinear and time-varying example in penicillin fed-batch production optimization. Copyright, QC 20220920

This paper discusses the challenges associated with the reliable and optimal operation of perfusion bioreactors and presents methods for modeling and identification of perfusion bioreactors as well as the vision for their integration. After presenting ageneric model of perfusion bioreactors, the paper shows how to use the concept of basis flux modes to uniquely compute reaction rates. The advantage of this concept with respect to elementary flux nodes and similar concepts in metabolic flux analysis is the reduced number of flux modes that need to be modeled. In addition, a procedure to identify the model and estimate the parameters for each reaction using Monod-type kinetics is presented. It is shown that the rational structure of these kinetic models results in optimization problems that are amenable to tractable computation of globally optimal parameter estimates. The methods are illustrated via examples with simulated or experimental data., QC 20210621

In this paper, we consider data driven control of Hammerstein systems. For such systems a common control structure is a transfer function followed by a static output nonlinearity that tries to cancel the input nonlinearity of the system, which is modeled as a polynomial or piece-wise linear function. The linear part of the controller is used to achieve desired disturbance rejection and tracking properties. To design a linear part of the controller, we propose a weighted average risk criterion with the risk being the average of the squared L2 tracking error. Here the average is with respect to the observations used in the controller and the weighting is with respect to how important it is to have good control for different impulse responses. This criterion corresponds to the average risk criterion leading to the Bayes estimator and we therefore call this approach Bayes control. By parametrizing the weighting function and estimating the corresponding hyperparameters we tune the weighting function to the information regarding the true impulse response contained in the data set available to the user for the control design. The numerical results show that the proposed methods result in stable controllers with performance comparable to the optimal controller, designed using the true input nonlinearity and true plant., QC 20220602

This paper discusses the challenges associated with the reliable and optimal operation of perfusion bioreactors and presents methods for modeling and identification of perfusion bioreactors as well as the vision for their integration. After presenting ageneric model of perfusion bioreactors, the paper shows how to use the concept of basis flux modes to uniquely compute reaction rates. The advantage of this concept with respect to elementary flux nodes and similar concepts in metabolic flux analysis is the reduced number of flux modes that need to be modeled. In addition, a procedure to identify the model and estimate the parameters for each reaction using Monod-type kinetics is presented. It is shown that the rational structure of these kinetic models results in optimization problems that are amenable to tractable computation of globally optimal parameter estimates. The methods are illustrated via examples with simulated or experimental data., QC 20210621

This paper discusses the challenges associated with the reliable and optimal operation of perfusion bioreactors and presents methods for modeling and identification of perfusion bioreactors as well as the vision for their integration. After presenting ageneric model of perfusion bioreactors, the paper shows how to use the concept of basis flux modes to uniquely compute reaction rates. The advantage of this concept with respect to elementary flux nodes and similar concepts in metabolic flux analysis is the reduced number of flux modes that need to be modeled. In addition, a procedure to identify the model and estimate the parameters for each reaction using Monod-type kinetics is presented. It is shown that the rational structure of these kinetic models results in optimization problems that are amenable to tractable computation of globally optimal parameter estimates. The methods are illustrated via examples with simulated or experimental data., QC 20210621

This paper discusses the challenges associated with the reliable and optimal operation of perfusion bioreactors and presents methods for modeling and identification of perfusion bioreactors as well as the vision for their integration. After presenting ageneric model of perfusion bioreactors, the paper shows how to use the concept of basis flux modes to uniquely compute reaction rates. The advantage of this concept with respect to elementary flux nodes and similar concepts in metabolic flux analysis is the reduced number of flux modes that need to be modeled. In addition, a procedure to identify the model and estimate the parameters for each reaction using Monod-type kinetics is presented. It is shown that the rational structure of these kinetic models results in optimization problems that are amenable to tractable computation of globally optimal parameter estimates. The methods are illustrated via examples with simulated or experimental data., QC 20210621

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, QC 20220124

Modern engineering systems include many components of different types and functions. Verifying that these systems satisfy given specifications can be an arduous task, as most formal verification methods are limited to systems of moderate size. Recently, contract theory has been proposed as a modular framework for defining specifications. In this paper, we present a contract theory for discrete-time dynamical control systems relying on assume/guarantee contracts, which prescribe assumptions on the input of the system and guarantees on the output. We then focus on contracts defined by linear constraints, and develop efficient computational tools for verification of satisfaction and refinement based on linear programming. We exemplify these tools in a simulation example, proving a certain safety specification for a two-vehicle autonomous driving setting. Copyright, QC 20220124

This paper considers the susceptible-infected-susceptible (SIS) epidemic model with an underlying network structure and focuses on the effect of social distancing to regulate the epidemic level. We demonstrate that if each subpopulation is informed of its infection rate and reduces interactions accordingly, the fraction of the subpopulation infected stays below half for all time instants. To this end, we first modify the basic SIS model by introducing a state dependent parameter representing the frequency of interactions between subpopulations. Thereafter, we show that for this modified SIS model, the spectral radius of a suitably-defined matrix being not greater than one causes all the agents, regardless of their initial sickness levels, to converge to the healthy state; assuming non-trivial disease spread, the spectral radius being greater than one leads to the existence of a unique endemic equilibrium, which is also asymptotically stable. Finally, by leveraging the aforementioned results, we show that the fraction of (sub)populations infected never exceeds half. Copyright, QC 20211201

Based on the analysis of the characteristics and operation status of the process industry, as well as the development of the global intelligent manufacturing industry, a new mode of intelligent manufacturing for the process industry, namely, deep integration of industrial artificial intelligence and the Industrial Internet with the process industry, is proposed. This paper analyzes the development status of the exist-ing three-tier structure of the process industry, which consists of the enterprise resource planning, the manufacturing execution system, and the process control system, and examines the decision-making, control, and operation management adopted by process enterprises. Based on this analysis, it then describes the meaning of an intelligent manufacturing framework and presents a vision of an intelligent optimal decision-making system based on human-machine cooperation and an intelligent autonomous control system. Finally, this paper analyzes the scientific challenges and key technologies that are crucial for the successful deployment of intelligent manufacturing in the process industry., QC 20211206

This paper discusses the challenges associated with the reliable and optimal operation of perfusion bioreactors and presents methods for modeling and identification of perfusion bioreactors as well as the vision for their integration. After presenting ageneric model of perfusion bioreactors, the paper shows how to use the concept of basis flux modes to uniquely compute reaction rates. The advantage of this concept with respect to elementary flux nodes and similar concepts in metabolic flux analysis is the reduced number of flux modes that need to be modeled. In addition, a procedure to identify the model and estimate the parameters for each reaction using Monod-type kinetics is presented. It is shown that the rational structure of these kinetic models results in optimization problems that are amenable to tractable computation of globally optimal parameter estimates. The methods are illustrated via examples with simulated or experimental data., QC 20210621

This paper investigates the problem of controller identification given the data from a linear quadratic Model Predictive Controller (MPC) with constraints. We propose an approach for learning MPC that explicitly uses the gradient information in the training process. This is motivated by the observation that recent differentiable convex optimization MPC solvers can provide both the optimal feedback law from the state to control input as well as the corresponding gradient. As a proof of concept, we apply this approach to explicit MPC (eMPC), for which the feedback law is a piece-wise affine function of the state, but the number of pieces grows rapidly with the state dimension. Controller identification can here be used to find an approximate low complexity functional approximation of the controller. The eMPC is modelled using a Neural Network (NN) with Rectified Linear Units (ReLUs), since such NNs can represent any piece-wise affine function. A key motivation is to replace on-line solvers with neural networks to implement MPC and to simplify the evaluation of the function in larger input dimensions. We also study experimental design and model evaluation in this framework, and propose a hit-and-run sampling algorithm for input design. The proposed algorithms are illustrated and numerically evaluated on a second order MPC problem., QC 20211022

In this paper, we propose a methodology to detect the topology of a dynamic network that is based on the analysis of the uncertainty of the static characteristic of the matrix of transfer functions between the external excitations and the node signals., QC 20211026

This paper investigates the rendezvous problem for the autonomous cooperative landing of an unmanned aerial vehicle (UAV) on an unmanned surface vehicle (USV). Such heterogeneous agents, with nonlinear dynamics, are dynamically decoupled but share a common cooperative rendezvous task. The underlying control scheme is based on distributed Model Predictive Control (MPC). The main contribution is a rendezvous algorithm with an online update rule of the rendezvous location. The algorithm only requires the agents to exchange information when they can not guarantee to rendezvous. Hence, the exchange of information occurs aperiodically, which reduces the necessary communication between the agents. Furthermore, we prove that the algorithm guarantees recursive feasibility. The simulation results illustrate the effectiveness of the proposed algorithm applied to the problem of autonomous cooperative landing., QC 20211015

This article investigates the use of a model-based neural network for the traffic reconstruction problem using noisy measurements coming from Probe Vehicles (PV). The traffic state is assumed to be the density only, modeled by a partial differential equation. There exist various methods for reconstructing the density in that case. However, none of them perform well with noise and very few deal with lagrangian measurements. This paper introduces a method that can reduce the processes of identification, reconstruction, prediction, and noise rejection into a single optimization problem. Numerical simulations, based either on a macroscopic or a microscopic model, show good performance for a moderate computational burden. Copyright, QC 20210830

This paper aims to design a robust internal model-based voltage control (RIMVC) scheme for DC Microgrids (DCMGs) in the presence of unknown external disturbances. Maintaining voltage reference tracking under measurement noise, delays, model parameter uncertainties and unknown external disturbances while the load changes simultaneously is a severe challenge for the DC–DC converters in DCMGs. By developing a modified internal model-based voltage control for DC–DC converters, this work proposes a plug-and-play (PnP) robust voltage control scheme to address the abovementioned challenge. The proposed control method has a cascade structure. In the first step, a modified IMC control is designed to achieve desired tracking performance for a nominal dynamical system. In the next step, the output feedback H∞ control part is added to improve the performance robustness under external disturbances and parameter uncertainties. The efficiency of the proposed control scheme is evaluated using a real-time MATLAB/Simulink testbed, taking into account unknown internal and external disturbances under various rapid voltage reference changes, model parameter uncertainties, constant power loads, system delays and normal load profile changes in multiple case study scenarios., QC 20230823

In this paper, we focus on the solution of online optimization problems that arise often in signal processing and machine learning, in which we have access to streaming sources of data. We discuss algorithms for online optimization based on the prediction-correction paradigm, both in the primal and dual space. In particular, we leverage the typical regularized least-squares structure appearing in many signal processing problems to propose a novel and tailored prediction strategy, which we call extrapolation-based. By using tools from operator theory, we then analyze the convergence of the proposed methods as applied both to primal and dual problems, deriving an explicit bound for the tracking error, that is, the distance from the time-varying optimal solution. We further discuss the empirical performance of the algorithm when applied to signal processing, machine learning, and robotics problems., QC 20230712

This paper studies the class of logarithmically completely monotonic (LCM) functions. These functions play an important role in characterizing externally positive linear systems which find applications in important control problems such as non-overshooting reference tracking. Conditions are proposed to ensure a rational function is LCM, a result that enables the known space of linear continuous-time externally positive systems to be enlarged and an efficient and optimal pole-placement procedure for the monotonic tracking controller synthesis problem to be developed. The presented conditions are shown to be less conservative than existing approaches whilst being computationally tractable., QC 20230710

Resource allocation plays a central role in networked systems such as smart grids, communication networks, and urban transportation systems. In these systems, many constraints have physical meaning and infeasible allocations can result in a system breakdown. Hence, algorithms with asymptotic feasibility guarantees can be insufficient since they cannot ensure that an implementable solution is found in finite time. This paper proposes a distributed feasible method (DFM) for resource allocation based on barrier functions. In DFM, every iterate is feasible and safe to implement since it does not violate the physical constraints. We prove that, under mild conditions, DFM converges to an arbitrarily small neighborhood of the optimum. Numerical experiments demonstrate the competitive performance of DFM., QC 20230705

This paper addresses the aggregated monitoring problem for large-scale network systems with a few dedicated sensors. Full state estimation of such systems is often infeasible due to unobservability and/or computational infeasibility; therefore, through clustering and aggregation, a tractable representation of a network system, called a projected network system, is obtained for designing a minimum-order average state observer. This observer estimates the average states of the clusters, which are identified under explicit consideration of estimation error. Moreover, given the clustering, the proposed observer design algorithm exploits the structure of the estimation error dynamics to achieve computational tractability. Simulations show that the computation of the proposed algorithm is significantly faster than the usual H2/H infinity observer design techniques. On the other hand, compromise on the estimation error characteristics is shown to be marginal., QC 20230404

Data informativity is a crucial property to ensure the consistency of the prediction error estimate. This property has thus been extensively studied in the open-loop and in the closed-loop cases, but has only been briefly touched upon in the dynamic network case. In this paper, we consider the prediction error identification of the modules in a row of a dynamic network using the full input approach. Our main contribution is to propose a number of easily verifiable data informativity conditions for this identification problem. Among these conditions, we distinguish a sufficient data informativity condition that can be verified based on the topology of the network and a necessary and sufficient data informativity condition that can be verified via a rank condition on a matrix of coefficients that are related to a full-order model structure of the network. These data informativity conditions allow to determine different situations (i.e., different excitation patterns) leading to data informativity. In order to be able to distinguish between these different situations, we also propose an optimal experiment design problem that allows to determine the excitation pattern yielding a certain pre-specified accuracy with the least excitation power., QC 20230317

This paper proposes a network continuous-time susceptible-infected-susceptible (SIS) model coupled with individual opinion dynamics, where the opinion dynamics models an individual's perceived severity of illness or perceived susceptibility. The effects of opinion dynamics on the network SIS model are studied by analyzing the limiting behaviors of the system model, equilibria of the system and their stability., QC 20210719

The paper considers the problem of inferring individual network edges from timeseries data. This is the problem faced in target identification, but also important in cases where it is of interest to learn whether two specific network nodes interact directly as well as in cases where there is insufficient information to infer the full network. The proposed inference method is based on taking a geometric perspective on a corresponding regression problem. We show that, by considering the span of individual node response vectors in sample space, it is possible to identify a given edge with a label of confidence even if the available data are not informative to infer other parts of the network. Furthermore, the method points to what further experiments are needed to infer edges for which the available response data are not sufficiently informative. We demonstrate the results on a target identification problem of a nonlinear 20-gene network and show that targets can be identified independently from a single time-series experiment using significantly fewer samples than the number of nodes in the network., QC 20211216

In this paper, we propose a framework for the safe teleoperation of connected vehicles by remote human operators. The proposed framework combines model-checking, reachability analysis, and human factor design into a unified approach that checks the feasibility of linear temporal logic (LTL) specified teleoperation tasks, guarantees safety throughout the execution of the tasks, and keeps the remote operator as the primary decision maker in the control loop. We apply the general approach to a remote parking example to illustrate the framework. Then, to evaluate the framework's benefits and usability, we conduct a user study on an experimental remote control operator's room and a 1/10th scale vehicle. In the user study, we validate the approach's ability to ensure the completion of LTL-specified tasks and gain initial insight into whether human users find the teleoperation system intuitive., QC 20210629

In part I of the paper the problem of distributing potential games over undirected graphs was formulated. A restricted information potential game was designed using state-based formulation. Here, learning Nash equilibria for this game is studied. An algorithm is developed with mainly two phases, an estimation phase and a learning phase. The setting allows for available learning methods of the full information game to be directly incorporated in the learning phase. The result matches the outcome (i.e. converges to the same equilibria) of the full information game. In addition, the design takes into account considerations of convergence time, and synchrony of actions update. The developed distributed game and learning algorithm are used to solve a platoon matching problem for heavy duty vehicles. This serves two objectives. First, it provides a motivation for the presented gaming results. Second, the problem addressed can facilitate platoon matching where it provides a basis for an on-the-go strategy., QC 20210630

We study the distributed average consensus problem in multi-agent systems with directed communication links that are subject to quantized information flow. The goal of distributed average consensus is for the agents, each associated with some initial value, to obtain the average (or some value close to the average) of these initial values. In this paper, we present and analyze a distributed averaging algorithm which operates exclusively with quantized values (specifically, the information stored, processed and exchanged between neighboring agents is subject to deterministic uniform quantization) and relies on event-driven updates (e.g., to reduce energy consumption, communication bandwidth, network congestion, and/or processor usage). We characterize the properties of the proposed distributed averaging protocol and show that its execution, on any time-invariant and strongly connected digraph, will allow all agents to reach, in finite time, a common consensus value that is equal to the quantized average. We conclude with comparisons against existing quantized average consensus algorithms that illustrate the performance and potential advantages of the proposed algorithm., QC 20210809

This paper studies supervisory control of periodic event-triggered control (PETC) systems based on the construction of symbolic abstractions. To this end, we first construct symbolic abstractions for PETC systems, and establish feedback refinement relation from the PETC system to its symbolic models. Here, the constructed symbolic models are represented by the form of discrete event systems (DESs), including extended finite state machines, finite state machines, and classic DESs. With the constructed symbolic models, we study the supervisory control of PETC systems to achieve the desired specification. Since the constructed symbolic models are nondeterministic, we first transfer the symbolic models into deterministic versions, and then verify the existence of the supervisor. Finally, the obtained results are illustrated via a numerical example., QC 20210809

This paper studies how to control an agent in an uncertain environment over a connected sensor network, such that the agent is able to finish a sequence of tasks, namely, reaching certain sets in order. Based on multiple offline reference trajectories and constrained communication between the agent and the sensor network, an event-triggered task-switching control framework is proposed, so that the agent state remains in each task set for the desired time and then switches to the next task. Employing a local predicted control law and the messages from neighboring sensors, a two time-scale distributed filter is proposed for each sensor to estimate the agent state. Under mild system conditions (i.e., stabilization and collective detectability), the estimation error and trajectory tracking error are shown to be asymptotically upper bounded., QC 20210804

This paper studies input-to-state stability of networked and quantized control systems with external disturbances. Since the external disturbance is considered, the state may escape from the quantization regions, which thus leads to the introduction of the quantization mechanism with zooming-out and zooming-in stages. We first propose a novel hybrid system to incorporate both the open-loop and closed-loop systems. Based on the developed hybrid model, we establish the boundedness of the state in the zooming-out stage, and the convergence of the state in the zooming-in stage. Furthermore, sufficient conditions are established for input-to-state stability in the case of switching between the zooming-in and zooming-out stages. Finally, a numerical example is presented to illustrate the obtained results., QC 20210802

This paper proposes an event-triggered add-on safety mechanism to adjust the control parameters for timely braking in a networked vehicular system while maintaining maneuverability. Passenger vehicle maneuverability is significantly affected by the combined slip friction effect, in which larger longitudinal tire slips result in considerable drop in lateral tire forces. This is of higher importance when unexpected dangerous situations occur on the road and immediate actions, such as braking, need to be taken to avoid collision. Harsh braking can lead to high-slip and loss of maneuverability; hence, timely braking is essential to reduce high-slip scenarios. In addition to the vehicles own active safety systems, the proposed event-triggered add-on safety is activated upon being informed about dangers by the road-side infrastructure. The aim is to incorporate the add-on safety feature to adjust the automatic control parameters for smooth and timely braking such that a collision is avoided while vehicle's maneuverability is maintained. We study two different wireless technologies for communication between the infrastructure and the vehicles, the Long-Term Evolution (LTE) and the fifth generation (5G) schemes. The safety advantages of the proposed framework is validated through high-fidelity software simulations. Copyright, QC 20210710

In this paper, we introduce a continuous-time competing virus model with a shared resource. We say that the system is in the healthy state if all the agents are healthy, and the shared resource is not contaminated. If the epidemic remains persistent, and the shared resource is contaminated, we say that the system is in the endemic state. First of all we show, under appropriate assumptions, that the model we introduce is well-posed. Secondly, we establish sufficient conditions for exponential (resp. asymptotic) eradication of a virus. Thirdly, for the single-virus case with a shared resource, we establish conditions that lead to existence of an endemic equilibrium. Finally, we provide a necessary and sufficient condition for uniqueness of the healthy state., QC 20210802

Improving the powertrain control of heavy-duty vehicles can be an efficient way to reduce the fuel consumption and thereby reduce both the operating cost and the environmental impact. One way of doing so is by using information about the upcoming driving conditions, known as look-ahead information, in order to coast in gear or to use freewheeling. Controllers using such techniques today mainly exist for vehicles in highway driving. This paper therefore targets how such control can be applied to vehicles with more variations in their velocity. The driving mission of such a vehicle is here formulated as an optimal control problem. The control variables are the tractive force, the braking force, and a Boolean variable representing closed or open powertrain. The problem is solved by a model predictive controller, which at each iteration solves a mixed integer quadratic program. The fuel consumption is compared for four different control policies: a benchmark following the reference of the driving cycle, look-ahead control without freewheeling, freewheeling with the engine idling, and freewheeling with the engine turned off. Simulations on a driving cycle with a varying velocity profile show the potential of saving 11 %, 19 %, and 23% respectively for the control policies compared with the benchmark, in all cases without increasing the trip time. Copyright, QC 20210710

This paper formulates and studies the problem of controlling a networked SIS model using a single input in which the network structure is described by a connected undirected graph. A necessary and sufficient condition on the values of curing and infection rates for the healthy state to be exponentially stable is obtained via the analysis of signed Laplacians when the control input is the curing budget of a single agent. In the case when the healthy state is stabilizable, an explicit expression for the minimum curing budget is provided. The utility of the algorithm is demonstrated using a simulation over a network of cities in the northeastern United States., QC 20210629

This paper proposes a framework to assess the stability of an Ordinary Differential Equation (ODE) which is coupled to a 1D-partial differential equation (PDE). The stability theorem is based on a new result on Integral Quadratic Constraints (IQCs) and expressed in terms of two linear matrix inequalities with a moderate computational burden. The IQCs are not generated using dissipation inequalities involving the whole state of an infinite-dimensional system, but by using projection coefficients of the infinite-dimensional state. This permits to generalize our robustness result to many other PDEs. The proposed methodology is applied to a time-delay system and numerical results comparable to those in the literature are obtained., QC 20210628

This paper studies sampled-data implementation of event-triggered PI control for time-delay systems with parametric uncertainties. The systems are given by continuous-time linear systems with parameter uncertainty polytopes. We propose an event-triggered PI controller, in which the controller transmits its signal to the actuator when its relative value goes beyond a threshold. A state-space formulation of the Smith predictor is used to compensate the time-delay. An asymptotic stability condition is derived in the form of LMIs using a Lyapunov-Krasovskii functional. Numerical examples illustrate that our proposed controller reduces the communication load without performance degradation and despite plant uncertainties., QC 20210628

This paper investigates the output-based event-triggered communication and control for linear multi-agent consensus under a directed graph based on a co-design method. The communication among the agents, as well as the controller updates, are determined by some new event-triggering mechanisms, in order to reduce the use of the network resources. To simultaneously guarantee the significant properties including the asymptotic consensus, and strong Zeno-freeness (strictly positive inter-event times), a novel dis-tributed dynamic event-triggered protocol is proposed. Unlike the most-existing emulation-based approaches in which the control gain is previously decided, a systematic co-design procedure is proposed to design the controller gain, the observer gain, and the event-triggering mechanisms altogether in terms of solving a linear matrix inequality opti-mization problem. Based on the resulting hybrid system framework, a hybrid model is established for the distributed closed-loop system and the asymptotic consensus is achieved. Finally, a numerical example is presented to verify our systematic design methodology., QC 20230127

This paper considers the problem of distributed motion-and task-planning of multi-agent and multi-agent -object systems under temporal-logic-based tasks and uncertain dynamics. We focus on manipulator-endowed robotic agents that can interact with their surroundings. We present first continuous control algorithms for multi-agent navigation and cooperative object manipulation that exhibit the following properties. First, they are distributed in the sense that each agent calculates its own control signal from local interaction with the other agents and the environment. Second, they guarantee safety properties in terms of inter-agent collision avoidance and obstacle avoidance. Third, they adapt on-the-fly to dynamic uncertainties and are robust to exogenous disturbances. The aforementioned algorithms allow the abstraction of the underlying system to a finite-state representation. Inspired by formal-verification techniques, we use such a representation to derive plans for the agents that satisfy the given temporal-logic tasks. Various simulation results and hardware experiments verify the efficiency of the proposed algorithms., QC 20221205

This paper studies the stabilization and safety problems of nonlinear time-delay systems. Following both Razumikhin and Krasovskii approaches, we propose novel control Lyapunov functions/functionals for the stabilization problem and novel control barrier functions/functionals for the safety problem. The proposed control Lyapunov and barrier functions/functionals extend the existing ones from the delay-free case to the time-delay case, and allow for designing the stabilizing and safety controllers in closed-form. Since analytical solutions to time-delay optimal control problems are hard to be achieved, a sliding mode control based approach is developed to merge the proposed control Lyapunov and barrier functions/functionals. Based on the sliding surface functional, a feedback control law is established to investigate the stabilization and safety objectives simultaneously. In particular, the properties of the sliding surface functional are analyzed, and further how to construct the sliding surface functional is discussed. Finally, the proposed approaches are illustrated via two numerical examples from the connected cruise control problem of automotive systems and the synchronization problem of multi-agent systems., QC 20221129

Symbolic models or abstractions are known to be powerful tools for the control design of cyber- physical systems (CPSs) with logic specifications. In this paper, we investigate a novel learning-based approach to the construction of symbolic models for nonlinear control systems. In particular, the symbolic model is constructed based on learning the un-modeled part of the dynamics from training data based on state-space exploration, and the concept of an alternating simulation relation that represents behavioral relationships with respect to the original control system. Moreover, we aim at achieving safe exploration, meaning that the trajectory of the system is guaranteed to be in a safe region for all times while collecting the training data. In addition, we provide some techniques to reduce the computational load, in terms of memory and computation time,of constructing the symbolic models and the safety controller synthesis, so as to make our approach practical. Finally, a numerical simulation illustrates the effectiveness of the proposed approach., QC 20221111