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1.	Adib Yaghmaie, Farnaz, et al. (author) A New Result on Robust Adaptive Dynamic Programming for Uncertain Partially Linear Systems 2019 In: 2019 IEEE 58TH CONFERENCE ON DECISION AND CONTROL (CDC). - : IEEE. - 9781728113982 - 9781728113999 ; , s. 7480-7485 Conference paper (peer-reviewed)abstract In this paper, we present a new result on robust adaptive dynamic programming for the Linear Quadratic Regulation (LQR) problem, where the linear system is subject to unmatched uncertainty. We assume that the states of the linear system are fully measurable and the matched uncertainty models unmeasurable states with an unspecified dimension. We use the small-gain theorem to give a sufficient condition such that the generated policies in each iteration of on-policy and off-policy routines guarantee robust stability of the overall uncertain system. The sufficient condition can be used to design the weighting matrices in the LQR problem. We use a simulation example to demonstrate the result.
2.	Adib Yaghmaie, Farnaz, et al. (author) Using Reinforcement Learning for Model-free Linear Quadratic Control with Process and Measurement Noises 2019 In: 2019 IEEE 58TH CONFERENCE ON DECISION AND CONTROL (CDC). - : IEEE. - 9781728113982 - 9781728113999 ; , s. 6510-6517 Conference paper (peer-reviewed)abstract In this paper, we analyze a Linear Quadratic (LQ) control problem in terms of the average cost and the structure of the value function. We develop a completely model-free reinforcement learning algorithm to solve the LQ problem. Our algorithm is an off-policy routine where each policy is greedy with respect to all previous value functions. We prove that the algorithm produces stable policies given that the estimation errors remain small. Empirically, our algorithm outperforms the classical Q and off-policy learning routines.
3.	Altafini, Claudio, 1969- (author) A dynamical approach to privacy preserving average consensus 2019 In: 2019 IEEE 58th Conference on Decision and Control (CDC). - 9781728113982 - 9781728113999 Conference paper (peer-reviewed)abstract In this paper we propose a novel method for achieving average consensus in a continuous-time multiagent network while avoiding to disclose the initial states of the individual agents. In order to achieve privacy protection of the state variables, we introduce maps, called output masks, which alter the value of the states before transmitting them. These output masks are local (i.e., implemented independently by each agent), deterministic, time-varying and converging asymptotically to the true state. The resulting masked system is also time-varying and has the original (unmasked) system as its limit system. It is shown in the paper that the masked system has the original average consensus value as its only attractor. However, in order to preserve privacy, it cannot share an equilibrium point with the unmasked system, meaning that in the masked system the attractor cannot be also stable.
4.	Altafini, Claudio, 1969- (author) Investigating stability of Laplacians on signed digraphs via eventual positivity 2019 In: 2019 IEEE 58th Conference on Decision and Control (CDC). - : IEEE. - 9781728113982 - 9781728113999 Conference paper (peer-reviewed)abstract Signed Laplacian matrices generally fail to be diagonally dominant and may fail to be stable. For both undirected and directed graphs, in this paper we present conditions guaranteeing the stability of signed Laplacians based on the property of eventual positivity, a Perron-Frobenius type of property for signed matrices. Our conditions are necessary and sufficient for undirected graphs, but only sufficient for digraphs, the gap between necessity and sufficiency being filled by matrices who have this Perron-Frobenius property on the right but not on the left side (i.e., on the transpose). An exception is given by weight balanced signed digraphs, where eventual positivity corresponds to positive semidefinitness of the symmetric part of the Laplacian. Analogous conditions are obtained for signed stochastic matrices.
5.	Andersson, Carl, et al. (author) Deep convolutional networks in system identification 2019 In: Proc. 58th IEEE Conference on Decision and Control. - : IEEE. - 9781728113982 ; , s. 3670-3676 Conference paper (peer-reviewed)abstract Recent developments within deep learning are relevant for nonlinear system identification problems. In this paper, we establish connections between the deep learning and the system identification communities. It has recently been shown that convolutional architectures are at least as capable as recurrent architectures when it comes to sequence modeling tasks. Inspired by these results we explore the explicit relationships between the recently proposed temporal convolutional network (TCN) and two classic system identification model structures; Volterra series and block-oriented models. We end the paper with an experimental study where we provide results on two real-world problems, the well-known Silverbox dataset and a newer dataset originating from ground vibration experiments on an F-16 fighter aircraft.
6.	Arnström, Daniel, et al. (author) Exact Complexity Certification of a Standard Primal Active-Set Method for Quadratic Programming 2019 In: 2019 IEEE 58TH CONFERENCE ON DECISION AND CONTROL (CDC). - : IEEE. - 9781728113982 - 9781728113999 ; , s. 4317-4324 Conference paper (peer-reviewed)abstract Model Predictive Control (MPC) requires an optimization problem to be solved at each time step. For real-time MPC, it is important to solve these problems efficiently and to have good upper bounds on how long time the solver needs to solve them. Often for linear MPC problems, the optimization problem in question is a quadratic program (QP) that depends on parameters such as system states and reference signals. A popular class of methods for solving QPs is primal active-set methods, where a sequence of equality constrained QP subproblems are solved. This paper presents a method for computing which sequence of subproblems a primal active-set method will solve, for every parameter of interest in the parameter space. Knowledge about exactly which sequence of subproblems that will be solved can be used to compute a worst-case bound on how many iterations, and ultimately the maximum time, the active-set solver needs to converge to the solution. Furthermore, this information can be used to tailor the solver for the specific control task. The usefulness of the proposed method is illustrated on a set of MPC problems, where the exact worst-case number of iterations a primal active-set method requires to reach optimality is computed.
7.	Barreiro-Gomez, J., et al. (author) Fractional Mean-Field-Type Games under Non-Quadratic Costs : A Direct Method 2019 In: Proceedings of the IEEE Conference on Decision and Control. - : Institute of Electrical and Electronics Engineers Inc.. - 9781728113982 ; , s. 293-298 Conference paper (peer-reviewed)abstract This work examines the solvability of fractional conditional mean-field-type games. The evolution of the state is described by a time-fractional stochastic dynamics driven by jump-diffusion-regime switching Gauss-Volterra processes which include fractional Brownian motion and multi-fractional Brownian motion. The cost functional is non-quadratic and includes a fractional-integral of an higher order polynomial. We provide semi-explicitly the equilibrium strategies in state-and-conditional mean-field-type feedback form for all decision-makers.
8.	Bro, Viktor, et al. (author) Identification of continuous Volterra models with explicit time delay through series of Laguerre functions 2019 In: Proc. 58th IEEE Conference on Decision and Control. - : IEEE. - 9781728113982 ; , s. 5641-5646 Conference paper (peer-reviewed)abstract The problem of estimating nonlinear time-delay dynamics captured by continuous Volterra models from input-output data is treated. The delayed Volterra kernels are seen as impulse responses of linear time-invariant systems with time delay. Analytical expressions for the Laguerre series, where the Laguerre coefficients of the finite-dimensional part are admixed with the terms due to the delay, are provided. By utilizing the linearity of Volterra-Laguerre models in the unknown parameters, the model is estimated by a nonlinear least-squares method. An application of the proposed approach to the problem of Volterra modelling of the human smooth pursuit system from eye-tracking data is provided. The proposed approach demonstrates consistently accurate performance on both simulated and experimental data.
9.	Čičić, Mladen, 1991-, et al. (author) Stop-and-go wave dissipation using accumulated controlled moving bottlenecks in multi-class CTM framework 2019 In: Proceedings of the IEEE Conference on Decision and Control. - : Institute of Electrical and Electronics Engineers Inc.. - 9781728113982 ; , s. 3146-3151 Conference paper (peer-reviewed)abstract Stop-and-go waves on freeways are a well known problem that has typically been addressed using dynamic speed limits. As connected automated vehicles enter the roads, new approaches to traffic control are becoming available, since the control actions can now be communicated to these vehicles directly. It is therefore important to consider automated vehicles independently from the rest of the traffic, using traffic models with multiple vehicle classes. In this paper, we use a multi-class CTM to capture the interaction between the controlled vehicles and the background traffic. Exploiting the nonlinear nature of the model, we are able to first collect enough controlled vehicles into an area, and then use them to actuate the rest of the traffic by acting as a controlled moving bottleneck. In this way, we are able to dissipate stop-and-go waves quicker, improving the throughput and homogenizing the traffic. The effectiveness of the approach is demonstrated in simulations.
10.	Fält, Mattias, et al. (author) QPDAS: Dual Active Set Solver for Mixed Constraint Quadratic Programming 2019 In: 2019 IEEE Conference on Decision and Control (CDC). - 9781728113982 - 9781728113999 ; , s. 4891-4897 Conference paper (peer-reviewed)abstract We present a method for solving the general mixed constrained convex quadratic programming problem using an active set method on the dual problem. The approach is similar to existing active set methods, but we present a new way of solving the linear systems arising in the algorithm. There are two main contributions; we present a new way of factorizing the linear systems, and show how iterative refinement can be used to achieve good accuracy and to solve both types of sub-problems that arise from semi-definite problems.
11.	Gao, Yulong, et al. (author) Stochastic Modeling and Optimal Control for Automated Overtaking 2019 In: Proceedings of the IEEE Conference on Decision and Control. - : Institute of Electrical and Electronics Engineers Inc.. - 9781728113982 ; , s. 1273-1278 Conference paper (peer-reviewed)abstract This paper proposes a solution to the overtaking problem where an automated vehicle tries to overtake a human-driven vehicle, which may not be moving at a constant velocity. Using reachability theory, we first provide a robust time-optimal control algorithm to guarantee that there is no collision throughout the overtaking process. Following the robust formulation, we provide a stochastic reachability formulation that allows a trade-off between the conservative overtaking time and the allowance of a small collision probability. To capture the stochasticity of a human driver's behavior, we propose a new martingale-based model where we classify the human driver as aggressive or nonaggressive. We show that if the human driver is nonaggressive, our stochastic time-optimal control algorithm can provide a shorter overtaking time than our robust algorithm, whereas if the human driver is aggressive, the stochastic algorithm will act on a collision probability of zero, which will match the robust algorithm. Finally, we detail a simulated example that illustrates the effectiveness of the proposed algorithms.
12.	Ghoddousiboroujeni, M., et al. (author) Privacy of Real-Time Pricing in Smart Grid 2019 In: Proceedings of the IEEE Conference on Decision and Control. - : Institute of Electrical and Electronics Engineers Inc.. - 9781728113982 ; 2019-December, s. 5162-5167 Conference paper (peer-reviewed)abstract Installing smart meters to publish real-time electricity rates has been controversial while it might lead to privacy concerns. Dispatched rates include fine-grained data on aggregate electricity consumption in a zone and could potentially be used to infer a household's pattern of energy use or its occupancy. In this paper, we propose Blowfish privacy to protect the occupancy state of the houses connected to a smart grid. First, we introduce a Markov model of the relationship between electricity rate and electricity consumption. Next, we develop an algorithm that perturbs electricity rates before publishing them to ensure users' privacy. Last, the proposed algorithm is tested on data inspired by household occupancy models and its performance is compared to an alternative solution.
13.	Haasler, Isabel, et al. (author) Estimating ensemble flows on a hidden Markov chain 2019 In: Proceedings of the IEEE Conference on Decision and Control. - : Institute of Electrical and Electronics Engineers Inc.. - 9781728113982 ; , s. 1331-1338 Conference paper (peer-reviewed)abstract We propose a new framework to estimate the evolution of an ensemble of indistinguishable agents on a hidden Markov chain using only aggregate output data. This work can be viewed as an extension of the recent developments in optimal mass transport and Schrödinger bridges to the finite state space hidden Markov chain setting. The flow of the ensemble is estimated by solving a maximum likelihood problem, which has a convex formulation at the infinite-particle limit, and we develop a fast numerical algorithm for it. We illustrate in two numerical examples how this framework can be used to track the flow of identical and indistinguishable dynamical systems.
14.	Iwaki, Takuya, 1986-, et al. (author) Event-based Switching for Sampled-data Output Feedback Control : Applications to Cascade and Feedforward Control 2019 In: Proceedings of the IEEE Conference on Decision and Control. - : Institute of Electrical and Electronics Engineers Inc.. - 9781728113982 ; , s. 2592-2597 Conference paper (peer-reviewed)abstract This paper studies sampled-data output feedback control where the states are monitored by multiple sensors. Asymptotic stability conditions for given sampling intervals for each sensor are derived. Based on these results, we then propose an event-based controller switching, in which one sensor transmits its measurement to the controller with a fixed sampling rate while another sensor transmits with a send-on-delta strategy. Such a set-up is motivated by the many potential cascade and feedforward control architectures in process industry, which could enhance performance if additional wireless sensors could be added without changing existing (wired) communication schedules. Asymptotic stability conditions of the switching event-based control systems are derived. Numerical examples illustrate how our framework reduces the effect of disturbances for both cascade and feedforward PI control systems.
15.	Jafarian, Matin, et al. (author) Synchronization of quadratic integrate-and-fire spiking neurons : Constant versus voltage-dependent couplings 2019 In: Proceedings of the IEEE Conference on Decision and Control. - : Institute of Electrical and Electronics Engineers Inc.. - 9781728113982 ; , s. 4711-4716 Conference paper (peer-reviewed)abstract This paper studies synchronization of a network of hybrid quadratic integrate-and-fire spiking neurons communicating over a complete graph and interconnected by means of bidirectional electrical couplings. Synchronization of the network of identical neurons with a common and constant coupling strength is studied using a Lyapunov-based argument for sufficiently large coupling strength. In addition, a voltage-dependent coupling law is proposed. It is assumed that each neuron is coupled to each of its neighbors by a coupling law which depends on the voltage of its neighboring neuron. For the voltage-dependent case, a sufficient condition for synchronization of two interconnected neurons is presented. Moreover, a comparison between the two mechanisms is given. Simulation results are provided to verify the theoretical analysis.
16.	Kask, Nathalie, et al. (author) Data-driven modelling of fatigue in pelvic floor muscles when performing Kegel exercises 2019 In: 2019 IEEE 58th Conference On Decision And Control (CDC). - : IEEE. - 9781728113982 ; , s. 5647-5653 Conference paper (peer-reviewed)abstract This paper studies how to describe, using a piece-wise linear dynamical model, the short-term effects of fatigue and recovery on the strength of pelvic floor muscles. Specifically, we first adapt a known model that describes short-term fatigue in skeletal muscles to the specific problem of describing fatigue in pelvic floor muscles when performing Kegel exercises, and then propose a strategy to learn the modelSs parameters from field data. In details, we estimate the model parameters using a least squares approach starting from measurement data that has been obtained from three healthy women using a dedicated vaginal pressure sensor array and a connected mobile app which gamifies the Kegel exercising experience. We show that describing the pelvic floor muscles behaviour in terms of short-term fatigue and recovery factors plus learning the associated parameters from data from healthy women leads to the possibility of precisely forecasting how much pressure the players will exert while playing the game. By cross-learning and cross-testing individual models from the three volunteers we also discover that the models need to be individualized: indeed, the numerical results indicate that, generically, using data from one player to model another leads to potentially drastically lower forecasting capabilities.
17.	Matni, N., et al. (author) From self-tuning regulators to reinforcement learning and back again 2019 In: Proceedings of the IEEE Conference on Decision and Control. - : Institute of Electrical and Electronics Engineers Inc.. - 2576-2370 .- 0743-1546. - 9781728113982 - 9781728113999 ; , s. 3724-3740 Conference paper (peer-reviewed)abstract Machine and reinforcement learning (RL) are increasingly being applied to plan and control the behavior of autonomous systems interacting with the physical world. Examples include self-driving vehicles, distributed sensor networks, and agile robots. However, when machine learning is to be applied in these new settings, the algorithms had better come with the same type of reliability, robustness, and safety bounds that are hallmarks of control theory, or failures could be catastrophic. Thus, as learning algorithms are increasingly and more aggressively deployed in safety critical settings, it is imperative that control theorists join the conversation. The goal of this tutorial paper is to provide a starting point for control theorists wishing to work on learning related problems, by covering recent advances bridging learning and control theory, and by placing these results within an appropriate historical context of system identification and adaptive control.
18.	Milosevic, Jezdimir, et al. (author) A Network Monitoring Game with Heterogeneous Component Criticality Levels 2019 In: Proceedings of the IEEE Conference on Decision and Control. - : Institute of Electrical and Electronics Engineers Inc.. - 9781728113982 ; , s. 4379-4384 Conference paper (peer-reviewed)abstract We consider an attacker-operator game for monitoring a large-scale network that is comprised of components that differ in their criticality levels. In this zero-sum game, the operator seeks to position a limited number of sensors to monitor the network against the attacker who strategically targets a network component. The operator (resp. attacker) seeks to minimize (resp. maximize) the network loss. To study the properties of mixed-strategy Nash Equilibria of this game, we first study two simple instances: When component sets monitored from individual sensor locations are mutually disjoint; When only a single sensor is positioned, but with possibly overlapping monitoring component sets. Our analysis reveals new insights on how criticality levels impact the players equilibrium strategies. Next, we extend a previously developed approach to obtain an approximate Nash equilibrium in the general case. This approach uses solutions to minimum set cover and maximum set packing problems to construct an approximate Nash equilibrium. Finally, we implement a column generation procedure to improve this solution and numerically evaluate the performance of our approach.
19.	Pare, Philip E., et al. (author) Multi-Layer Disease Spread Model with a Water Distribution Network 2019 In: Proceedings of the IEEE Conference on Decision and Control. - : Institute of Electrical and Electronics Engineers Inc.. - 9781728113982 ; , s. 8335-8340 Conference paper (peer-reviewed)abstract This paper proposes a layered networked SIWS (Susceptible-Infected-Water-Susceptible) model, for an SIS-type waterborne disease spreading over a human contact network connected to a water distribution network that has a pathogen spreading in it. Conditions for local and global stability of the healthy state, where no one is sick and the water network is not contaminated, are provided. We also pose an observability problem, and show under certain conditions if you observe some of the human contact network you can recover the pathogen levels in the water network.
20.	Pates, Richard, et al. (author) On the Optimal Control of Relaxation Systems 2020 In: 2019 IEEE 58th Conference on Decision and Control, CDC 2019. - 0743-1546 .- 2576-2370. - 9781728113999 - 9781728113982 ; 2019-December, s. 6068-6073 Conference paper (peer-reviewed)abstract The relaxation systems are an important subclass of the passive systems that arise naturally in applications. We exploit the fact that they have highly structured state-space realisations to derive analytical solutions to some simple H-infinity type optimal control problems. The resulting controllers are also relaxation systems, and often sparse. This makes them ideal candidates for applications in large-scale problems, which we demonstrate by designing simple, sparse, electrical circuits to optimally control large inductive networks and to solve linear regression problems.
21.	Ren, Wei, et al. (author) Dynamic Quantization based Symbolic Abstractions for Nonlinear Control Systems 2019 In: Proceedings of the IEEE Conference on Decision and Control. - : Institute of Electrical and Electronics Engineers Inc.. - 9781728113982 ; , s. 4343-4348 Conference paper (peer-reviewed)abstract This paper studies the construction of dynamic symbolic abstractions for nonlinear control systems via dynamic quantization. Since computational complexity is a fundamental problem in the use of discrete abstractions, a dynamic quantizer with a time-varying quantization parameter is first applied to deal with this problem. Due to the dynamic quantizer, a dynamic approximation approach is proposed for the state and input sets. Based on the dynamic approximation, dynamic symbolic abstractions are constructed for nonlinear control systems, and an approximate bisimulation relation is guaranteed for the original system and the constructed dynamic symbolic abstraction. Finally, the obtained results are illustrated through a numerical example from path planning of mobile robots.
22.	Rodrigues, Diogo, et al. (author) Toward Tractable Global Solutions to Maximum-Likelihood Estimation Problems via Sparse Sum-of-Squares Relaxations 2019 In: 2019 IEEE 58TH CONFERENCE ON DECISION AND CONTROL (CDC). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781728113982 - 9781728113975 - 9781728113999 ; , s. 3184-3189 Conference paper (peer-reviewed)abstract In system identification, the maximum-likelihood method is typically used for parameter estimation owing to a number of optimal statistical properties. However, in many cases, the likelihood function is nonconvex. The solutions are usually obtained by local numerical optimization algorithms that require good initialization and cannot guarantee global optimality. This paper proposes a computationally tractable method that computes the maximum-likelihood parameter estimates with posterior certification of global optimality via the concept of sum-of-squares polynomials and sparse semidefinite relaxations. It is shown that the method can be applied to certain classes of discrete-time linear models. This is achieved by taking advantage of the rational structure of these models and the sparsity in the maximum-likelihood parameter estimation problem. The method is illustrated on a simulation model of a resonant mechanical system where standard methods struggle.
23.	Sasahara, Hampei, et al. (author) Hierarchical Model Decomposition for Distributed Design of Glocal Controllers 2019 In: Proceedings of the IEEE Conference on Decision and Control. - : Institute of Electrical and Electronics Engineers Inc.. - 9781728113982 ; , s. 3540-3545 Conference paper (peer-reviewed)abstract Since modern network systems are managed by multiple operators, practical distributed controller design is required to be independently performed in a distributed manner. The independent design of distributed controllers, referred to as distributed design, enables the synthesis process to be scalable. Nevertheless, distributed design methods have not yet been fully developed because of its difficulty. As a novel scheme for control of network systems, this paper presents a distributed design method of glocal (global/local) controllers. In the glocal structure, a global controller is introduced into the controller to be designed in addition to local decentralized controllers. The key idea to realize distributed design is to represent the original network system as a hierarchical cascaded system composed of reduced-order models each of which stands for the dynamics of global and local behaviors, here referred to as hierarchical model decomposition. Distributed design is achieved by designing controllers for the reduced-order models owing to the cascade structure. A numerical example demonstrates the effectiveness of the proposed glocal control.
24.	Soleymani, Touraj, et al. (author) Stochastic Control with Stale Information-Part I : Fully Observable Systems 2019 In: Proceedings of the IEEE Conference on Decision and Control. - : Institute of Electrical and Electronics Engineers Inc.. - 9781728113982 ; , s. 4178-4182 Conference paper (peer-reviewed)abstract Timeliness is an emerging requirement for cyber-physical systems, where the value of information can quickly diminish with time. Nevertheless, there are different imperfections and constraints that hinder the immediate access of decision makers to the latest states of such systems. This obliges the designers of these systems to study the impact of information staleness on the control performance. In this paper, we focus on control with stale information and study a trade-off between the information staleness and control performance. To this purpose, we design a test channel in which the staleness of observations is chosen deliberatively. This test channel should be regarded as an abstract model that allows us to obtain the achievable region in our trade-off analysis. Based on this trade-off, the performance of any communication channel with time-varying delay used for control applications can be assessed, and the maximum staleness that is tolerable for stability can be specified.
25.	Sun, Zhiyong, et al. (author) On distributed high-gain adaptive stabilization 2019 In: 2019 IEEE 58th Conference on Decision and Control, CDC 2019. - 2576-2370 .- 0743-1546. - 9781728113999 - 9781728113982 ; 2019-December, s. 1083-1088 Conference paper (peer-reviewed)abstract In this paper we consider adaptive distributed stabilization for uncertain multivariable linear systems with a time-varying diagonal matrix gain. We show that an unknown system matrix being an M-matrix is a sufficient condition to ensure uncertain linear systems to be stabilizable by matrix high gains, and derive a threshold condition to ensure exponential stability of uncertain linear systems stabilized by a monotonically increasing diagonal gain matrix. When each individual gain function in the matrix gain is updated by state-dependent functions, the boundedness and convergence of both system states and adaptive matrix gains are guaranteed. We apply the matrix gain stabilization approach to adaptive synchronization control for complex networks with time-varying coupling weights.
26.	Suttner, Raik, et al. (author) Exponential and practical exponential stability of second-order formation control systems 2020 In: 2019 IEEE 58th Conference on Decision and Control, CDC 2019. - 2576-2370 .- 0743-1546. - 9781728113982 - 9781728113999 ; 2019-December, s. 3521-3526 Conference paper (peer-reviewed)abstract We study the problem of distance-based formation shape control for autonomous agents with double-integrator dynamics. Our considerations are focused on exponential stability properties. For second-order formation systems under the standard gradient-based control law, we prove local exponential stability with respect to the total energy by applying Chetaev's trick to the Lyapunov candidate function. We also propose a novel formation control law, which does not require measurements of relative positions but instead measurements of distances. The distance-only control law is based on an approximation of symmetric products of vector fields by sinusoidal perturbations. A suitable averaging analysis reveals that the averaged system coincides with the multi-agent system under the standard gradient-based control law. This allows us to prove practical exponential stability for the system under the distance-only control law.
27.	Teixeira, André (author) Optimal stealthy attacks on actuators for strictly proper systems 2019 In: 2019 IEEE 58th Conference on Decision and Control (CDC). - 9781728113982 ; , s. 4385-4390 Conference paper (peer-reviewed)abstract In this paper, we consider stealthy data injection attacks against control systems, and develop security sensitivity metrics to quantify their impact on the system. The final objective of this work is to use such metrics as objective functions in the design of optimal resilient controllers against stealthy attacks, akin to the classical design of optimal ℋ ∞ robust controllers. As a first metric, the recently proposed ℓ 2 output to output gain is first examined, and fundamental limitations of this gain for systems with strictly proper dynamics are uncovered and characterized. To circumvent such limitations, a new security sensitivity metric is proposed, namely the truncated ℓ 2 gain. Necessary and sufficient conditions for this gain to be finite are derived, which we show can cope with strictly proper systems. Finally, we report preliminary investigations on the design of optimal resilient controllers, which are supported and illustrated through numerical examples.
28.	Theodosis, Dionysios, et al. (author) Distributed Event-Based Control and Stability of Interconnected Systems 2019 In: Proceedings of the IEEE Conference on Decision and Control. - : Institute of Electrical and Electronics Engineers Inc.. - 9781728113982 ; , s. 1668-1673 Conference paper (peer-reviewed)abstract This paper presents sufficient conditions that characterize the stability properties of certain classes of interconnected systems. The considered classes of systems include autonomous, continuous and discrete time nonlinear systems coupled with linear or nonlinear interconnection terms. These conditions are then exploited for the decentralized event-based control of interconnected systems. Examples illustrate the theoretical results and simulations show the effectiveness of the proposed event-based techniques.
29.	Umenberger, Jack, et al. (author) Bayesian identification of state-space models via adaptive thermostats 2019 In: 2019 IEEE 58th conference on decision and control (CDC). - : IEEE. - 9781728113982 ; , s. 7382-7388 Conference paper (peer-reviewed)abstract Bayesian modeling has been recognized as a powerful approach to system identification, not least due to its intrinsic uncertainty quantification. However, despite many recent developments, Bayesian identification of nonlinear state space models still poses major computational challenges. We propose a new method to tackle this problem. The technique is based on simulating a so-called thermostat, a stochastic differential equation constructed to have the posterior parameter distribution as its limiting distribution. Simulating the thermostat requires access to unbiased estimates of the gradient of the log-posterior. To handle this, we make use of a recent method for debiasing particle-filter-based smoothing estimates. Numerical results show a clear benefit of this approach compared to a direct application of (biased) particle-filter-based gradient estimates within the thermostat.
30.	Vreman, Nils, et al. (author) Minimizing Side-Channel Attack Vulnerability Via Schedule Randomization 2019 In: 2019 IEEE 58th Conference on Decision and Control (CDC). - 9781728113982 - 9781728113999 ; , s. 2928-2933 Conference paper (peer-reviewed)abstract Control systems can be vulnerable to security threats where an attacker gathers information about the execution of the system. In particular, side-channel attacks exploit the predictability of real-time control systems and of their schedules. To counteract their action, a scheduler can randomize the temporal execution of tasks and limit the amount of information the attacker can gather. Schedule randomization is aimed at achieving the highest possible schedule diversity (measured using the upper-approximated entropy metric) during the real-time execution of the controller. This paper investigates fundamental limitations of schedule randomization for a generic taskset. The constructed schedule set has minimal size and achieves the highest possible upper-approximated entropy.
31.	Xing, Y., et al. (author) Network Weight Estimation for Binary-Valued Observation Models 2019 In: Proceedings of the IEEE Conference on Decision and Control. - : Institute of Electrical and Electronics Engineers Inc.. - 9781728113982 ; , s. 2278-2283 Conference paper (peer-reviewed)abstract This paper studies the estimation of network weights for a class of systems with binary-valued observations. In these systems only quantized observations are available for the network estimation. Furthermore, system states are coupled with observations, and the quantization parts are unknown inherent components, which hinder the design of inputs and quantizers. In order to deal with the temporal dependency of observations and achieve the recursive estimation of network weights, a deterministic objective function is constructed based on the likelihood function by extending the dimension of observations and applying ergodic properties of Markov chains. By imposing an independent Gaussian assumption on disturbances, we show that the function is strictly concave and has a unique maximum identical to the true parameter vector, so in this way the estimation problem is transformed to an optimization problem. A recursive algorithm based on stochastic approximation techniques is proposed to solve this problem, and the strong consistency of the algorithm is established. Our recursive algorithm can be applied to online tasks like real-time decision-making and surveillance for networked systems. This work also provides a new scheme for the identification of systems with quantized observations.
32.	Yamalova, Diana, et al. (author) Nonlinear dynamics of a positive hybrid observer for the impulsive Goodwin's oscillator : a design study 2019 In: 2019 IEEE 58th conference on decision and control (CDC). - : IEEE. - 9781728113982 ; , s. 1893-1898 Conference paper (peer-reviewed)abstract The impulsive Goodwin oscillator (IGO) is nowadays an established mathematical model of pulsatile regulation that is suitable for e.g. capturing non-basal regulation of testosterone, cortisol, and growth hormone. The model consists of a continuous linear time-invariant block closed by a nonlinear pulse-modulated feedback. The hybrid closed-loop dynamics are highly nonlinear. The endocrine feedback is biologically implemented by the bursts of a release hormone secreted by the hypothalamus and not accessible for measurement. This poses a particular state estimation problem, where both the continuous states of the IGO and the firings of the impulsive feedback have to be reconstructed from the continuous outputs, i.e. the hormone concentrations measurable in the blood stream. A hybrid observer with two output error feedback loops, one for the continuous state estimates and another for the discrete one, is considered. Positivity of the observer estimates is demonstrated. The observer design problem at hand is, for all feasible initial conditions, to guarantee the asymptotic convergence of the observer estimates at highest possible rate to the state vector of the IGO. To solve the design problem, bifurcation analysis of the observer dynamics is performed and the basin of attraction for the stationary solution with a zero state estimation error is evaluated. The observer convergence rate is evaluated through the largest Lyapunov exponent. The efficacy of the design approach is confirmed by simulation.
33.	Yi, Xinlei, et al. (author) A Distributed Algorithm for Online Convex Optimization with Time-Varying Coupled Inequality Constraints 2019 In: Proceedings of the IEEE Conference on Decision and Control. - : Institute of Electrical and Electronics Engineers Inc.. - 9781728113982 ; , s. 555-560 Conference paper (peer-reviewed)abstract This paper considers distributed online optimization with time-varying coupled inequality constraints. The global objective function is composed of local convex cost and regularization functions and the coupled constraint function is the sum of local convex constraint functions. A distributed online primal-dual mirror descent algorithm is proposed to solve this problem, where the local cost, regularization, and constraint functions are held privately and revealed only after each time slot. We first derive regret and constraint violation bounds for the algorithm and show how they depend on the stepsize sequences, the accumulated variation of the comparator sequence, the number of agents, and the network connectivity. As a result, we prove that the algorithm achieves sublinear dynamic regret and constraint violation if the accumulated variation of the optimal sequence also grows sublinearly. We also prove that the algorithm achieves sublinear static regret and constraint violation under mild conditions. In addition, smaller bounds on the static regret are achieved when the objective functions are strongly convex. Finally, numerical simulations are provided to illustrate the effectiveness of the theoretical results.
34.	Yue, Zuogong, et al. (author) Network Stability, Realisation and Random Model Generation 2019 In: 2019 IEEE 58th Conference on Decision and Control (CDC). - New York, NY : IEEE. - 9781728113982 - 9781728113975 - 9781728113999 ; , s. 4539-4544 Conference paper (peer-reviewed)abstract Dynamical structure functions (DSFs) provide means for modelling networked dynamical systems and exploring interactive structures thereof. There have been several studies on methods/algorithms for reconstructing (Boolean) networks from time-series data. However, there are no methods currently available for random generation of DSF models with complex network structures for benchmarking. In particular, it may be desirable to generate stable DSF models or require the presence of feedback structures while keeping topology and dynamics random up to these constraints. This work provides procedures to obtain such models. On the path of doing so, we first study essential properties and concepts of DSF models, including realisation and stability. Then, the paper suggests model generation algorithms, whose implementations are now publicly available. © 2019 IEEE.
35.	Zhang, Han, 1989-, et al. (author) Inverse Optimal Control for Finite-Horizon Discrete-time Linear Quadratic Regulator under Noisy Output 2019 In: Proceedings of the IEEE Conference on Decision and Control. - : Institute of Electrical and Electronics Engineers Inc.. - 9781728113982 ; , s. 6663-6668 Conference paper (peer-reviewed)abstract In this paper, the problem of inverse optimal control for finite-horizon discrete-time Linear Quadratic Regulators (LQRs) is considered. The goal of the inverse optimal control problem is to recover the corresponding objective function by the noisy observations. We consider the problem of inverse optimal control in two scenarios: 1) the distributions of the initial state and the observation noise are unknown, yet the exact observations on the initial states and the noisy observations on system output are available; 2) the exact observations on the initial states are not available, yet the observation noises are known white Gaussian and the distribution of the initial state is also Gaussian (with unknown mean and covariance). For the first scenario, we formulate the problem as a risk minimization problem and show that its solution is statistically consistent. For the second scenario, we fit the problem into the framework of maximum-likelihood and Expectation Maximization (EM) algorithm is used to solve this problem. The performance for the estimations are shown by numerical examples.
36.	Zhang, Kuize, et al. (author) K-delayed strong detectability of discrete-event systems 2019 In: Proceedings of the IEEE Conference on Decision and Control. - : Institute of Electrical and Electronics Engineers Inc.. - 9781728113982 ; , s. 7647-7652 Conference paper (peer-reviewed)abstract Among notions of detectability for a discrete-event system (DES), strong detectability implies that after a finite number of observations to every infinitely long output/label sequence generated by the DES, the current state can be uniquely determined. This notion is strong so that by using it the current state can be easily determined. In order to keep the advantage of strong detectability and weaken its disadvantage, we can additionally take some subsequent outputs into account in order to determine the current state. Such a modified observation will make some DES that is not strongly detectable become strongly detectable in a weaker sense, which we call K-delayed strong detectability if we observe at least K outputs after the time at which the state need to be determined. In this paper, we study K-delayed strong detectability for DESs modeled by finite-state automata (FSAs), and give a polynomial-time verification algorithm by using a novel concurrent-composition method. Note that the algorithm applies to all FSAs. Also by the method, an upper bound for K has been found, and we also obtain polynomial-time verification algorithms for (k1, k)-detectability and (k1, k)-D-detectability of FSAs firstly studied by [Shu and Lin, 2013]. Our algorithms strength the corresponding polynomial-time verification algorithms given by Shu and Lin based on the usual assumptions of deadlock-freeness and promptness (i.e., there is no reachable unobservable cycle).
37.	Zhang, Kuize, et al. (author) Synthesis for controllability and observability of logical control networks 2019 In: Proceedings of the IEEE Conference on Decision and Control. - : Institute of Electrical and Electronics Engineers Inc.. - 9781728113982 ; , s. 108-113 Conference paper (peer-reviewed)abstract Finite-state systems have applications in systems biology, formal verification and synthesis problems of infinite-state (hybrid) systems, etc. As deterministic finite-state systems, logical control networks (LCNs) consist of a finite number of nodes and their update states, where these nodes can be in a finite number of states. In this paper, we investigate the synthesis problem for controllability and observability of LCNs by state feedback under the semitensor product framework. We show that state feedback can never enforce controllability of an LCN, but sometimes can enforce its observability. We prove that for an LCN Σ and another LCN Σ′ obtained by feeding a state-feedback controller into Σ, (1) if Σ is controllable, then Σ′ can be either controllable or not; (2) if Σ is not controllable, then Σ′ is not controllable either; (3) if Σ is observable, then Σ′ can be either observable or not; (4) if Σ is not observable, Σ′ can also be observable or not.
38.	Zhao, Di, et al. (author) A Convex Approach to Frisch-Kalman Problem 2020 In: 2019 IEEE 58th Conference on Decision and Control, CDC 2019. - 2576-2370 .- 0743-1546. - 9781728113982 - 9781728113999 ; 2019-December, s. 7154-7158 Conference paper (peer-reviewed)abstract This paper proposes a convex approach to the Frisch-Kalman problem that identifies the linear relations among variables from noisy observations. The problem was proposed by Ragnar Frisch in 1930s, and was promoted and further developed by Rudolf Kalman later in 1980s. It is essentially a rank minimization problem with convex constraints. Regarding this problem, analytical results and heuristic methods have been pursued over a half century. The proposed convex method in this paper is demonstrated to outperform several commonly adopted heuristics when the noise components are relatively small compared with the underlying data.
39.	Zhu, B., et al. (author) Fusion of Sensors Data in Automotive Radar Systems : A Spectral Estimation Approach 2019 In: Proceedings of the IEEE Conference on Decision and Control. - : Institute of Electrical and Electronics Engineers Inc.. - 9781728113982 ; , s. 5088-5093 Conference paper (peer-reviewed)abstract To accurately estimate locations and velocities of surrounding targets (cars) is crucial for advanced driver assistance systems based on radar sensors. In this paper we derive methods for fusing data from multiple radar sensors in order to improve the accuracy and robustness of such estimates. First we pose the target estimation problem as a multivariate multidimensional spectral estimation problem. The problem is multivariate since each radar sensor gives rise to a measurement channel. Then we investigate how the use of the cross-spectra affects target estimates. We see that the use of the magnitude of the cross-spectrum significantly improves the accuracy of the target estimates, whereas an attempt to compensate the phase lag of the cross-spectrum only gives marginal improvement. This paper may be viewed as a first step towards applying high-resolution methods that builds on multidimensional multivariate spectral estimation for sensor fusion.

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