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A fundamental problem that the bearing rigidity theory studies is to determine when a framework can be uniquely determined up to a translation and a scaling factor by its inter-neighbor bearings. While many previous works focused on the bearing rigidity of two-dimensional frameworks, a first contribution of this paper is to extend these results to arbitrary dimensions. It is shown that a framework in an arbitrary dimension can be uniquely determined up to a translation and a scaling factor by the bearings if and only if the framework is infinitesimally bearing rigid. In this paper, the proposed bearing rigidity theory is further applied to the bearing-only formation stabilization problem where the target formation is defined by inter-neighbor bearings and the feedback control uses only bearing measurements. Nonlinear distributed bearing-only formation control laws are proposed for the cases with and without a global orientation. It is proved that the control laws can almost globally stabilize infinitesimally bearing rigid formations. Numerical simulations are provided to support the analysis. [ABSTRACT FROM AUTHOR]

This paper proposes a new concurrent learning-based adaptive control algorithm. The main objective behind our proposition is to relax the persistent excitation requirement for the stability guarantee, while providing the ability to identify time-varying parameters. To achieve the objective, this paper designs a directional forgetting algorithm, which is then integrated with the adaptive law. The theoretical stability analysis shows that the tracking and parameter estimation error is exponentially stable with the signal only finitely excited, not persistently excited. The analysis also shows that the proposed algorithm can guarantee the stability under time-varying parameters. Moreover, the necessary and sufficient conditions for the stability given the time-varying parameters are derived. The results of numerical simulations confirm the validity of the theoretical analysis results and demonstrate the performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper addresses the recursive minimum-variance filtering problem for a class of two-dimensional shift-varying systems with stochastic nonlinearity and degraded measurements. The stochastic nonlinearity is governed by its statistical characteristics and the degraded measurements reflect the signal degradation obeying certain prescribed probabilistic distributions. The main objective of this paper is to construct a two-step recursive filter that achieves the minimum error variance of the state estimation at each step. Utilizing an inductive approach, unbiasedness of the proposed filter is first ensured and the parameters of the filter are then designed by resorting to the completing squares method. Subsequently, the filtering performances including the boundedness and the monotonicity are investigated with respect to the measurement degradations through mathematically rigorous analysis. Moreover, a computational algorithm is presented to facilitate the online implementation of the designed filter. Finally, numerical simulation illustrates the effectiveness and applicability of the proposed filtering scheme in the state estimation problem for monitoring a long transmission line in circuit systems. [ABSTRACT FROM AUTHOR]

This paper studies the optimal output-feedback control of a linear time-invariant system where a stochastic event-based scheduler triggers the communication between the sensor and the controller. The primary goal of the use of this type of scheduling strategy is to provide significant reductions in the usage of the sensor-to-controller communication and, in turn, improve energy expenditure in the network. In this paper, we aim to design an admissible control policy, which is a function of the observed output, to minimize a quadratic cost function while employing a stochastic event-triggered scheduler that preserves the Gaussian property of the plant state and the estimation error. For the infinite horizon case, we present analytical expressions that quantify the tradeoff between the communication cost and control performance of such event-triggered control systems. This tradeoff is confirmed quantitatively via numerical examples. Besides, numerical simulations justify that the event-triggered control provides better quadratic control performance than the (traditional) periodic time-triggered control at the same average sampling rate. [ABSTRACT FROM AUTHOR]

This paper presents a small-gain result for large-scale interconnected systems composed of hybrid subsystems. It is assumed that the subsystems are input-to-output practically stable (IOpS) with offset and unboundedness observability (UO), and the interconnected system can be proved to be IOpS with offset and UO as long as the proposed small-gain condition is satisfied. If, moreover, the assumption of IOpS on the subsystems is reduced to input-to-output stability (IOS), then the interconnected system is IOS. [ABSTRACT FROM AUTHOR]

In this paper, a full-order observer design method is proposed for port-Hamiltonian systems. The proposed method is based on the notion of contractive port-Hamiltonian systems. It is the first structure preserving observer design for a broad class of input-state-output port-Hamiltonian systems. The design procedure consists of solving a matching equation, similar to the Interconnection and Damping Assignment Passivity-Based Control (IDA-PBC) for controller design. The matching equation, as shown in the paper, has some closed-form solutions for general class of mechanical and electromechanical systems. As another feature of the proposed method, it is shown that the existence of solution of the corresponding matching equation for a linear port-Hamiltonian system is equivalent to the detectability property of that system. Upon these facts, the proposed method can be considered as a counterpart of IDA-PBC for observer design. Simulations for some benchmark examples, including ball and beam, magnetic levitation, and permanent magnetic synchronous motor, show the potency and applicability of the method. [ABSTRACT FROM AUTHOR]

This paper considers the problem of selecting the top-m designs using simulation with input uncertainty. The performance of each design is measured by its worst case performance. The objective of this paper is to maximize the probability of correctly selecting the top-m designs given a fixed simulation budget. Due to the complexity of probability of correct selection (PCS), we develop a lower bound for the PCS and derive an asymptotically optimal budget allocation rule. Useful insights on characterizing the efficient budget allocation rule with input uncertainty are provided. Meanwhile, a sequential simulation procedure is suggested to implement the allocation rule. A series of numerical experiments indicate that the proposed simulation budget allocation rule can outperform all existing selection rules. [ABSTRACT FROM AUTHOR]

In this paper, the issue of stochastic stabilization of nonlinear systems via intermittent Brownian motion under random disturbance is investigated. Two types of noise are considered. One is Brownian noise that is regarded as an intermittent control input. The other is the random disturbance caused by the unknown external environment, which can lead to time fluctuations of the intermittent Brownian noise stabilization. We first present an intermittent stochastic system with random disturbance. We then propose an almost sure stability condition for the underlying system. The stability results are then used to stabilize the memristive Lorenz system. Numerical simulations are also given to validate the effectiveness of the theoretic results obtained. [ABSTRACT FROM AUTHOR]

Average consensus underpins key functionalities of distributed systems ranging from distributed information fusion, decision-making, distributed optimization, to load balancing and decentralized control. Existing distributed average consensus algorithms require each node to exchange and disclose state information to its neighbors, which is undesirable in cases where the state is private or contains sensitive information. In this paper, we propose a novel approach that avoids disclosing individual state information in average consensus by letting each node decompose its state into 2 substates. For each node, one of the two substates involves in computation and internode interactions as if it were the original state, while the other substate interacts only with the first substate of the same node, being completely invisible to other nodes. The initial values of the two substates are chosen randomly but with their mean fixed to the initial value of the original state, which is key to guarantee convergence to the desired consensus value. In direct contrast to differential-privacy based privacy-preserving average-consensus approaches, which enable privacy by compromising accuracy in the consensus value, the proposed approach can guarantee convergence to the exact desired value without any error. Not only is the proposed approach able to prevent the disclosure of a node's initial state to honest-but-curious neighbors, it can also provide protection against inference by external eavesdroppers able to wiretap communication links. Numerical simulations demonstrate the effectiveness of the approach and its advantages over state-of-the-art counterparts. [ABSTRACT FROM AUTHOR]

Consensus is fundamental for distributed systems since it underpins key functionalities of such systems ranging from distributed information fusion, decision making, to decentralized control. In order to reach an agreement, existing consensus algorithms require each agent to exchange explicit state information with its neighbors. This leads to the disclosure of private state information, which is undesirable in cases where privacy is of concern. In this paper, we propose a novel approach for undirected networks, which can enable secure and privacy-preserving average consensus in a decentralized architecture in the absence of an aggregator or third party. By leveraging partial homomorphic cryptography to embed secrecy in pairwise interaction dynamics, our approach can guarantee convergence to the consensus value (subject to a quantization error) in a deterministic manner without disclosing a node's state to its neighbors. We provide a new privacy definition for dynamical systems, and give a new framework to rigorously prove that a node's privacy can be protected as long as it has at least one legitimate neighbor, which follows the consensus protocol faithfully without attempts to infer other nodes’ states. In addition to enabling resilience to passive attackers aiming to steal state information, the approach also allows easy incorporation of defending mechanisms against active attackers who try to alter the content of exchanged messages. Furthermore, in contrast to existing noise-injection-based privacy-preserving mechanisms that have to reconfigure the entire network when the topology or number of nodes varies, our approach is applicable to dynamic environments with time-varying coupling topologies. This secure and privacy-preserving approach is also applicable to weighted average consensus as well as maximum/minimum consensus under a new update rule. Numerical simulations and comparison with existing approaches confirm the theoretical results. Experimental results on a Raspberry-Pi board based microcontroller network are also presented to verify the effectiveness and efficiency of the approach. [ABSTRACT FROM AUTHOR]

This paper is concerned with the winner-take-all (WTA) problem on networks. We propose the first distributed protocol to address this problem dynamically. This protocol features strong nonlinearity. Theoretical analysis reveals that it contains invariant quantities, symmetric solutions, and multiple equilibrium points. By leveraging these properties, this work proves the instability of its non-WTA solutions, and global convergence to the WTA solution via Lyapunov theory. Two simulations over networks with 10 and 200 nodes, respectively, are conducted. Simulation results have well verified the theoretical conclusions drawn in this paper. [ABSTRACT FROM PUBLISHER]

This paper studies distributed stochastic approximation algorithms based on broadcast gossip on communication networks represented by digraphs. Weak convergence of these algorithms is proved, and an associated ordinary differential equation (ODE) is formulated connecting convergence points with local objective functions and network properties. Using these results, a methodology is proposed for network design, aimed at achieving the desired asymptotic behavior at consensus. Convergence rate of the algorithm is also analyzed and further improved using an attached stochastic differential equation. Simulation results illustrate the theoretical concepts. [ABSTRACT FROM AUTHOR]

This paper deals with a novel Plug-and-Play (PnP) architecture for the control and monitoring of Large-Scale Systems (LSSs). The proposed approach integrates a distributed Model Predictive Control (MPC) strategy with a distributed Fault Detection (FD) architecture and methodology in a PnP framework. The basic concept is to use the FD scheme as an autonomous decision support system: once a fault is detected, the faulty subsystem can be unplugged to avoid the propagation of the fault in the interconnected LSS. Analogously, once the issue has been solved, the disconnected subsystem can be re-plugged-in. PnP design of local controllers and detectors allow these operations to be performed safely, i.e., without spoiling stability and constraint satisfaction for the whole LSS. The PnP distributed MPC is derived for a class of nonlinear LSSs and an integrated PnP distributed FD architecture is proposed. Simulation results in two paradigmatic examples show the effectiveness and the potential of the general methodology. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel distributed method to stabilize a set of agents moving in a two dimensional environment to a desired rigid formation. In our approach, each agent computes its control input using the relative positions of a set of formation neighbors but, contrary to most existing works, this information is expressed in the agent's own independent local coordinate frame, without requiring any common reference. The controller is based on the minimization of a Lyapunov function that includes locally computed rotation matrices, which are required due to the absence of a common orientation. Our contribution is that the proposed distributed coordinate-free method achieves global stabilization to a rigid formation with the agents using only partial information of the team, does not require any leader units, and is applicable to both single-integrator or unicycle agents. To guarantee global stability, we require that the network induced by the agent interactions belongs to a certain class of undirected rigid graphs in two dimensions, which we explicitly characterize. The performance of the proposed method is illustrated with numerical simulations. [ABSTRACT FROM AUTHOR]

Presents abstracts for the articles comprising this issue of the publication. [ABSTRACT FROM AUTHOR]

Compositional control methods are well-suited for the design of large-scale control systems. QSR-dissipativity and passivity are closely related to stability and may be utilized to show Lyapunov and finite-gain stability for dynamical systems under certain conditions. Additionally, passivity is preserved under feedback and parallel interconnections of passive systems and may be utilized to build larger stable systems. In this paper, we present a QSR-dissipativity and passivity based analysis for event-triggered networked control systems (NCSs). We characterize QSR-dissipativity, passivity, and stability conditions for an event-triggered NCS in cases where an input–output event-triggering sampler is located on the plant's side, controller's side, or both sides leading to a considerable decrease in the communication load among subunits. We calculate passivity levels for the entire NCS. We show that the general passivity and stability conditions of the NCS depend on passivity properties of the plant and controller. Our results illustrate the tradeoffs amongst passivity levels, stability, and the rates of communication between subsystems. The motivation behind our work is that by designing and connecting individual stable passive event-triggered NCSs with desired passivity levels, one can design larger stable passive compositional networked systems that are suitable for the design and control of large-scale systems, such as cyber-physical systems. [ABSTRACT FROM AUTHOR]

This paper is concerned with the $H_2$ control of linear systems with multiple quantization channels. The quantization parameters of each channel are not required to be identical. The resultant mismatches are represented by polytopic uncertainties. A composite controller composed of linear and nonlinear parts is designed to meet the required $H_2$ performance and offset the quantization error. Resorting to a vertex separation technique and Finsler lemma instead of matrix inverse operations, new synthesis conditions for the desired linear part are derived in terms of linear matrix inequalities, which are further extended to treat systems with norm-bounded uncertainties. A comparison of conservativeness between the proposed methods and the existing ones is demonstrated by two numerical examples. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new approach to state estimation of multiple state-space models. Unlike the traditional methods (including the interacting multiple-model algorithm) that approximate a Gaussian mixture distribution with a single Gaussian distribution, the proposed method approximates the joint probability density functions of the state and model identity through Bayesian inference. It is shown that the proposed method reduces the approximation error considerably, and improves estimation accuracy without increasing computational cost. Analysis of its specific features as well as a potential extension is also presented. Numerical examples with a practically oriented simulation are employed to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper investigates the state feedback stabilizability problem for multi-input multi-output systems over memoryless fading noisy channels under stationary signal-to-noise ratio constraints. The channel is modeled as a cascade of a multiplicative noise and an additive white Gaussian noise. The aim of the addressed problem is to find the minimum overall quality of service required to render the stabilization possible. The essential idea of our approach is to view the stabilization from the perspective of a demand/supply balance. Specifically, each control input is considered as the demand side for communication resource while the channels are considered as the supply side. The supply resource of the channels is characterized by their respective quality of service. The stabilization of the networked systems requires the demand/supply balance of the communication resource. Depending on whether the channel resource is configurable or not, two different approaches are adopted for realizing the required balance. If the channel resource is configurable, one can tailor the supply to meet the demand via channel resource allocation; otherwise, one can shape the demand to meet the supply via certain transceivers design mechanism. Explicit conditions on the minimum overall quality of service for networked stabilization are established in both scenarios. [ABSTRACT FROM AUTHOR]

The goal of this paper is to design a stabilizing feedback boundary control for a reaction–diffusion partial differential equation (PDE), where the boundary control is subject to a constant delay while the equation may be unstable without any control. For this system, which is equivalent to a parabolic equation coupled with a transport equation, a prediction-based control is explicitly computed by splitting the infinite-dimensional system into two parts: a finite-dimensional unstable part and a stable infinite-dimensional part. A finite-dimensional delayed controller is computed for the unstable part, and it is shown that this controller stabilizes the whole PDE. The proof is based on an explicit expression of the classical Artstein transformation combined with an adequately designed Lyapunov function. A numerical simulation illustrates the constructive feedback design method. [ABSTRACT FROM AUTHOR]

This paper presents a discrete-time distributed and noncooperative routing algorithm, which is proved, via Lyapunov arguments, to asymptotically converge to a specific equilibrium condition among the traffic flows over the network paths, known as Wardrop equilibrium. This convergence result improves the discrete-time algorithms in the literature, which achieve approximate convergence to the Wardrop equilibrium. Numerical simulations show the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper proposes a resource-aware control approach for discrete-time linear uncertain systems. The approach uses a self-trigger condition to determine the sampling time to guarantee that the system is uniformly ultimately bounded and an event-trigger condition to determine control updates. The self-trigger condition is constructed using the skewed structured singular value to treat uncertainties in the prediction, and the event-trigger condition is constructed by considering the costs of sampling and control updates. A numerical example is provided to illustrate the approach. [ABSTRACT FROM AUTHOR]

Separable nonlinear least-squares (SNLLS) problems arise frequently in many research fields, such as system identification and machine learning. The variable projection (VP) method is a very powerful tool for solving such problems. In this paper, we consider the regularization of ill-conditioned SNLLS problems based on the VP method. Selecting an appropriate regularization parameter is difficult because of the nonlinear optimization procedure. We propose to determine the regularization parameter using the weighted generalized cross-validation method at every iteration. This makes the original objective function changing during the optimization procedure. To circumvent this problem, we use an inequation to produce a consistent demand of decreasing at successive iterations. The approximation of the Jacobian of the regularized problem is also discussed. The proposed regularized VP algorithm is tested by the parameter estimation problem of several statistical models. Numerical results demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper considers a singular problem in optimal control of continuous-time bimodal switched linear systems. A relaxed switched system with a continuous-valued switching signal is considered and the representations of singular control and singular arcs are derived. The similarity in the structure between the singular control and a stabilizing switching law is revealed and an approximation of the singular control by a well-defined switching signal is addressed. The results are demonstrated by numerical simulations. [ABSTRACT FROM AUTHOR]

The note considers the problem of computing pure Nash equilibrium (NE) strategies in distributed (i.e., network-based) settings. The paper studies a class of inertial best-response dynamics based on the fictitious play (FP) algorithm. It is shown that inertial best-response dynamics are robust to informational limitations common in distributed settings. Fully distributed variants of FP with inertia and joint strategy FP (JSFP) with inertia are developed and convergence is proven to the set of pure NE. The distributed algorithms developed in the paper rely on consensus methods. Results are validated using numerical simulations. [ABSTRACT FROM AUTHOR]

The paper aims to establish a general framework for robust output synchronization of a group of networked agents. The agents under investigation have nonlinear, uncertain and heterogeneous dynamics. Output synchronization denotes that all agents, through collaborative control, achieve output agreement and follow a desired pattern. In particular, the agreed trajectory is not defined by nor known to any agent in advance. Collaborative control is achieved using only output information from neighboring agents. Two concurrent actions are revealed in the proposed synchronization framework. This framework involves design strategies for both perturbed consensus and perturbed regulation problems, subject to a class of small gain conditions. The success of the framework is verified by constructive proof and numerical simulation. [ABSTRACT FROM AUTHOR]

The paper deals with the design of sampled-data controllers which preserve the stabilizing performance of a continuous-time backstepping control strategy. This is achieved through matching of the control Lyapunov functions evolutions at the sampling instants. The method is developed for systems in strict-feedback form. The results are discussed and compared with similar strategies through simulated examples. [ABSTRACT FROM AUTHOR]

This paper introduces a methodology for modelling, analysis, and control design of a large-scale system of agents deployed in 3-D space. The agents' communication graph is a mesh-grid disk 2-D topology in polar coordinates. Treating the agents as a continuum, we model the agents' collective dynamics by complex-valued reaction-diffusion 2-D partial differential equations (PDEs) in polar coordinates, whose states represent the position coordinates of the agents. Due to the reaction term in the PDEs, the agents can achieve a rich family of 2-D deployment manifolds in 3-D space which correspond to the PDEs' equilibrium as determined by the boundary conditions. Unfortunately, many of these deployment surfaces are open-loop unstable. To stabilize them, a heretofore open and challenging problem of PDE stabilization by boundary control on a disk has been solved in this paper, using a new class of explicit backstepping kernels that involve the Poisson kernel. A dual observer, which is also explicit, allows to estimate the positions of all the agents, as needed in the leaders' feedback, by only measuring the position of their closest neighbors. Hence, an all-explicit control scheme is found which is distributed in the sense that each agent only needs local information. Closed-loop exponential stability in the L^2, H^1, and H^2 spaces is proved for both full state and output feedback designs. Numerical simulations illustrate the proposed approach for 3-D deployment of discrete agents. [ABSTRACT FROM AUTHOR]

Several convex relaxations of the optimal power flow (OPF) problem have recently been developed using both bus injection models and branch flow models. In this paper, we prove relations among three convex relaxations: a semidefinite relaxation that computes a full matrix, a chordal relaxation based on a chordal extension of the network graph, and a second-order cone relaxation that computes the smallest partial matrix. We prove a bijection between the feasible sets of the OPF in the bus injection model and the branch flow model, establishing the equivalence of these two models and their second-order cone relaxations. Our results imply that, for radial networks, all these relaxations are equivalent and one should always solve the second-order cone relaxation. For mesh networks, the semidefinite relaxation and the chordal relaxation are equally tight and both are strictly tighter than the second-order cone relaxation. Therefore, for mesh networks, one should either solve the chordal relaxation or the SOCP relaxation, trading off tightness and the required computational effort. Simulations are used to illustrate these results. [ABSTRACT FROM PUBLISHER]

This paper deals with the performance optimization of resource allocation systems with the aim of maximizing the system's throughput under a given budget for acquiring resources. Resources are assumed to be renewable, i.e., they are not consumed by the operations and become available again after they have been released. The systems under consideration are modeled by a subclass of timed Petri nets called deterministic timed weighted marked graphs. In addition, we take into account infinite server semantics, i.e., the degree of self-concurrency of each transition is infinite. We propose an approach that provides an optimal solution, but has a high computational cost. For this reason, we also present two different approaches that can find suboptimal solutions with a reduced computational cost. The performances of the proposed approaches are compared by means of numerical simulations. [ABSTRACT FROM AUTHOR]

This paper tackles the controllability problem of Boolean control networks (BCNs). By resorting to the semi-tensor product technique and the Warshall algorithm, several improved novel reachability and controllability criteria are obtained for the BCNs. Through constructing a sequence of rigorous Boolean matrices, controllability matrix of the considered BCNs is derived iteratively. It is worth pointing out that the proposed method has lower computational complexity, and thus facilitates the reachability and controllability analysis for BCNs. In addition, the issue concerning controllability of BCNs with undesirable interior states is investigated by checking a set of designated Boolean matrices. Finally, to test the effectiveness of the obtained theoretical results, a genetic network model known as the $\lambda$ switch is used as an example for numerical simulation. [ABSTRACT FROM AUTHOR]

This paper is devoted to the problem of nonlinear state estimation under multirate sampling in presence of disturbance inputs. Considering a general description of a nonlinear sampled-data system, we establish a prescriptive framework for multirate observer design via an approximate discrete-time model of the plant. This framework is shown to be input-to-state stable in a semiglobal practical sense with respect to the estimation error for the unknown exact discrete-time model. A numerical example of an aerospace vehicle with input and output channels of various sampling rates demonstrates how the multirate observer can drastically improve performance compared with the single-rate observer. [ABSTRACT FROM PUBLISHER]

The paper deals with the control and regulation by integral controllers for the nonlinear systems governed by scalar quasi-linear hyperbolic partial differential equations. Both the control input and the measured output are located on the boundary. The closed-loop stabilization of the linearized model with the designed integral controller is proved first by using the method of spectral analysis and then by the Lyapunov direct method. Based on the elaborated Lyapunov function, we prove local exponential stability of the nonlinear closed-loop system with the same controller. The output regulation to the set-point with zero static error by the integral controller is shown upon the nonlinear system. Numerical simulations by the Preissmann scheme are carried out to validate the robustness performance of the designed controller to face to unknown constant disturbances. [ABSTRACT FROM AUTHOR]

In existing work of sliding mode observers (SMOs) for descriptor systems, a necessary condition is that the system must be infinitely observable. This paper presents a scheme that circumvents that condition, by reformulating the system as a reduced-order system where certain structures in the system matrix are manipulated and certain states are treated as unknown inputs. Following that, an SMO is implemented on the reduced-order system where state and fault estimation can be achieved. Necessary and sufficient conditions of this scheme are also presented. Finally, a simulation example shows the effectiveness of the proposed scheme. [ABSTRACT FROM PUBLISHER]

In the existing control problems for the leader–follower multiagent systems, the leader's dynamics are independent of the followers. In this paper, we consider a tracking control problem for an interconnected leader–follower multiagent system, where the leader's dynamics are interconnected with the states of the followers. It is shown that such a problem can be used to model the autonomous power sharing problem of grid-connected microgrids. In order to solve this problem, a distributed control law using only local information is synthesized. Simulation results are provided to show the effectiveness of the proposed control law. [ABSTRACT FROM PUBLISHER]

In this paper, we are concerned with the stability of a Schrödinger equation through boundary coupling with a wave equation, where an internal dissipative damping is designed at the wave equation. The energy decay of the Schrödinger equation is obtained by the boundary transmission between the Schrödinger and wave equations. By a detailed spectral analysis, we show that all the eigenvalues of both the Schrödinger and wave equations have negative real parts, and the whole system is exponentially stable. A numerical simulation is presented for the distributions of the spectrum of the whole system, and it is found that the spectrum of the Schrödinger equation depends largely on the boundary transmission parameter and the decay of the wave equation. [ABSTRACT FROM AUTHOR]

This paper deals with the analysis of the nonisothermal axial dispersion tubular reactor. The existence of equilibrium profiles is investigated. In particular, for equal Peclet numbers, it is shown that one or three equilibria can be exhibited, depending on the parameters of the system, especially on the diffusion coefficient. In addition, different and close Peclet numbers are also considered. In these cases, it is also shown that the reactor has one or three equilibrium profiles. Some numerical simulations support the theoretical results. [ABSTRACT FROM AUTHOR]

This paper concentrates on coordinate-free formation control for directed networks, for which the dynamic motion of each agent is assumed to be governed only by a local control. We develop a graph Laplacian approach to solve the global and exponential formation stablization problem using merely relative position measurements between neighbors. First, to capture the sensing and control architectures that are needed to maintain the shape of a formation, a necessary and sufficient topological condition is proposed. Second, a Laplacian-based control law is developed for the stablization problem of a group of mobile agents to a desired formation shape under both fixed and switching topologies due to temporal node failures. Simulation results are provided to demonstrate that our Laplacian-based formation control strategy is inherently fault-tolerant and robust to node failures. [ABSTRACT FROM AUTHOR]

This paper investigates the output consensus problem for heterogeneous linear multi-agent systems via event-triggered control. By introducing a dynamic compensator for each agent, a fully distributed event-triggered control strategy with an adaptive event-triggering mechanism is proposed. It is shown that under the proposed control strategy, all agents asymptotically achieve output consensus with intermittent communication in a fully distributed manner. Moreover, with the proposed event-triggering mechanism, Zeno behavior is strictly excluded for each agent. Compared with existing mechanisms, the proposed event-triggering mechanism is independent of any global information and avoids the continuous monitoring issue. Finally, a numerical example is provided to illustrate the effectiveness of the proposed event-triggered control strategy. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of identifiability of dynamical networks in the case where the vector of noises on the nodes does not have full rank. In the full-rank noise case, network identifiability is defined as the capability of uniquely identifying the transfer function matrices describing the network from informative data. This includes the noise model, which can be uniquely defined when the noise vector has full rank. When the noise vector has a singular spectrum, it admits an infinite number of different noise models and the definition of network identifiability must be adapted to demand that the correct noise spectrum be identified from informative data rather than a specific noise model. With this new definition, we show that a network with rank reduced noise is identifiable under the same conditions that apply to a network with full-rank noise. [ABSTRACT FROM AUTHOR]

This paper studies bearing-based formation control of a group of autonomous agents with the leader-first follower (LFF) structure in an arbitrary dimensional space. First, the bearing-based Henneberg construction and some properties of the LFF formation are introduced. Then, we propose and analyze bearing-only control laws that almost globally stabilize LFF formations to a desired formation. Further strategies to rotate and rescale the target formation are also discussed. Finally, simulation results are provided to support the analysis. [ABSTRACT FROM AUTHOR]

Stochastic Trust-Region Response-Surface method (STRONG) is a new response-surface-based framework for simulation optimization. The appeal of STRONG lies in that it preserves the advantages, yet eliminates the disadvantages, of traditional response surface methodology (RSM) that has been used for more than 50 years. Specifically, STRONG does not require human involvement in the search process and can guarantee to converge to the true optimum with probability one (w.p.1). In this paper, we propose an improved framework, called STRONG-X, that enhances the efficiency and efficacy of STRONG to widen its applicability to more practical problems. For efficiency improvement, STRONG-X includes a newly-developed experimental scheme that consists of construction of optimal simulation designs and an assignment strategy for random number streams to obtain computational gains. For efficacy improvement, a new variant, called STRONG-XG, is developed to achieve convergence under generally-distributed responses, as opposed to STRONG and STRONG-X where convergence is guaranteed only when the response is normal. An extensive numerical study is conducted to evaluate the efficiency and efficacy of STRONG-X and STRONG-XG. Moreover, two illustrative examples are provided to show the viability of STRONG-X and STRONG-XG in practical settings. [ABSTRACT FROM AUTHOR]

This paper is concerned with a multi-period portfolio management problem over a finite horizon. The objective is to seek the optimal investment policy series which maximizes the weighted sum of a linear combination of the expected return and the variance of portfolio over all the investment periods. This formulation enables the investor to adjust weights for any period and have full freedom and control over their best tradeoff between return and risk over each period. We show that such a problem is a convex quadratic programming problem in terms of the decision variables, regardless of price dynamic nature (either linear or nonlinear cases). By solving the stationary equation directly, an optimal solution is developed for its original problem without using embedding method. The solution is simplified for a general linear price model with high-order and coupled asset dynamics and shown to be implementable with historical price data. Simulation is carried out on USA and China stock markets with real data, which demonstrates feasibility and better performance of the proposed solution than the special case considered in the literature. In particular, the proposed solution with suitable nonzero weights on intermediate time periods offers higher return at the same risk level, compared with one involving the terminal wealth only in the objective function. [ABSTRACT FROM AUTHOR]

This paper presents a new adaptive attitude tracking controller for rigid body systems, with unknown inertia and unknown gyro-bias, using inertial vector measurements. The proposed control scheme guarantees almost global asymptotic convergence of the attitude and angular velocity to their desired values. Simulation results are provided to illustrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of controlling a network of mobile sensors so that a set of hidden states are estimated up to a user-specified accuracy. The sensors take measurements and fuse them online using an information consensus filter (ICF). At the same time, the local estimates guide the sensors to their next best configuration. This leads to an LMI-constrained optimization problem that we solve by means of a new distributed random approximate projections method. The new method is robust to the state disagreement errors that exist among the robots as the ICF fuses the collected measurements. Assuming that the noise corrupting the measurements is zero-mean and Gaussian and that the robots are self-localized in the environment, the integrated system converges to the next best positions from where new observations will be taken. This process is repeated with the robots taking a sequence of observations until the hidden states are estimated up to the desired user-specified accuracy. We present simulations of sparse landmark localization, where the robotic team achieves the desired estimation tolerances while exhibiting interesting emergent behavior. [ABSTRACT FROM PUBLISHER]

Secure state estimation is the problem of estimating the state of a dynamical system from a set of noisy and adversarially corrupted measurements. Intrinsically a combinatorial problem, secure state estimation has been traditionally addressed either by brute force search, suffering from scalability issues, or via convex relaxations, using algorithms that can terminate in polynomial time but are not necessarily sound. In this paper, we present a novel algorithm that uses a satisfiability modulo theory approach to harness the complexity of secure state estimation. We leverage results from formal methods over real numbers to provide guarantees on the soundness and completeness of our algorithm. Moreover, we discuss its scalability properties, by providing upper bounds on the runtime performance. Numerical simulations support our arguments by showing an order of magnitude decrease in execution time with respect to alternative techniques. Finally, the effectiveness of the proposed algorithm is demonstrated by applying it to the problem of controlling an unmanned ground vehicle. [ABSTRACT FROM AUTHOR]

The disturbance estimate is the central idea of active disturbance rejection control (ADRC), where the disturbance is estimated via extended state observer (ESO). However, the conventional ESO requires the disturbance to have slow variation and in order to counteract such disturbance, the ESO must use high gain or discontinuous function. In this paper, we demonstrate for the first time, through a one-dimensional anti-stable wave system, the online disturbance estimation by designing an infinite-dimensional disturbance estimator. We go back to the starting point of ADRC that the exactly observable output allows identification of the disturbance which can thereupon be estimated. This realizes the estimation/cancellation strategy of ADRC in a very different way without resorting conventional ESO. We do not regard the disturbance as an “extra-state” variable in the ESO, and hence avoid the two limitations of the ESO. Since the disturbance is estimated, it can be compensated in the feedback loop. It is shown that the disturbance estimator-based feedback control can successfully stabilize the PDE system and at the same time guarantees that all subsystems involved are uniformly bounded. A numerical simulation is presented to illustrate the effectiveness of the the proposed scheme. [ABSTRACT FROM AUTHOR]

In this paper, the problem of time-varying parameter identification is studied. To this aim, two identification algorithms are developed in order to identify time-varying parameters in a finite time or prescribed time (fixed-time). The convergence proofs are based on a notion of finite-time stability over finite intervals of time, i.e., short-finite-time stability, homogeneity for time-varying systems, and Lyapunov-based approach. The results are obtained under injectivity of the regressor term, which is related to the classical identifiability condition. The case of bounded disturbances (noise of measurements) is analyzed for both algorithms. Simulation results illustrate the feasibility of the proposed algorithms. [ABSTRACT FROM AUTHOR]

In this paper, a strict linear Lyapunov function is developed in order to investigate the exponential stability of a linear hyperbolic partial differential equation with positive boundary conditions. Based on the method of characteristics, some properties of the positive solutions are derived for the hyperbolic initial boundary value problems. The dissipative boundary condition in terms of linear inequalities is proven to be not only sufficient but also necessary under an extra assumption on the velocities of the hyperbolic systems. An application to control of the freeway traffic modeled by the Aw–Rascle traffic flow equation illustrates and motivates the theoretical results. The boundary control strategies are designed by integrating the on-ramp metering with the mainline speed limit. Finally, the proposed feedback laws are tested under simulation, first in the free-flow case and then in the congestion mode, which show adequate performance to stabilize the local freeway traffic. [ABSTRACT FROM PUBLISHER]

Several abstracts for articles are presented on topics which include resource allocation by V. Joseph and colleagues, distributed learning of distributions via social sampling by A. Sarwate and others, and manufacturing consent by V. Borkar and others.