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This paper presents a counterexample to the conjecture that the semi-explicit Lie-Newmark algorithm is variational. As a consequence the Lie-Newmark method is not well-suited for long-time simulation of rigid body-type mechanical systems. The counterexample consists of a rigid body in a static potential field.

In this paper we study the problem of estimating the channel parameters for a generic wireless sensor network (WSN) in a completely distributed manner, using consensus algorithms. Specifically, we first propose a distributed strategy to minimize the effects of unknown constant offsets in the reading ofthe Radio Strength Signal Indicator (RSSI) due to uncalibrated sensors. Then we show how the computation of the optimal wireless channels parameters, which are the solution of a global least-square optimization problem, can be obtained with a consensus-based algorithm. The proposed algorithms are general algorithms for sensor calibration and distributed least-square parameter identification,and do not require any knowledge on the global topology of the network nor the total number of nodes. Finally, we apply these algorithms to experimental data collected from an indoor wireless sensor network.

We extend several recent results on full-state feedback stabilization and state estimation of PDE–ODE cascades, where the PDEs are either of heat type or of wave type, from the previously considered cases where the interconnections are of Dirichlet type, to interconnections of Neumann type. The Neumann type interconnections constrain the PDE state to be subject to a Dirichlet boundary condition at the PDE–ODE interface, and employ the boundary value of the first spatial derivative of the PDE state to be the input to the ODE. In addition to considering heat-ODE and wave-ODE cascades, we also consider a cascade of a diffusion–convection PDE with an ODE, where the convection direction is “away” from the ODE. We refer to this case as a PDE–ODE cascade with “counter-convection.” This case is not only interesting because the PDE subsystem is unstable, but because the control signal is subject to competing effects of diffusion, which is in both directions in the one-dimensional domain, and counter-convection, which is in the direction that is opposite from the propagation direction of the standard delay (transport PDE) process. We rely on the diffusion process to propagate the control signal through the PDE towards the ODE, to stabilize the ODE.

We consider a distributed system of N agents, on which we define a quadratic optimization problem subject to a linear equality constraint. We assume that the nodes can estimate the gradient of the cost function by measuring the steady state response of the system. Even if the cost function cannot be decoupled into individual terms for the agents, and the linear constraint involves the whole system state, we are able to design a distributed, gradient-driven, algorithm, for the solution of the optimization problem. This algorithm belongs to the class of quasi-Newton methods and requires minimal knowledge of the system to behave fairly well. We proved finite time convergence of the algorithm in its centralized version, and we designed its distributed implementation in the case in which a communication graph is given. In this latter case, the tool of average consensus results to be fundamental for the distribution of the algorithm. As a testbed for the proposed method, we consider the problem of optimal distributed reactive power compensation in smart microgrids.

The distributed estimation of the number of active sensors in a network can be important for estimation and organization purposes. We propose a design methodology based on the following paradigm: some locally randomly generated values are exchanged among the various sensors and thus modified by known consensus-based strategies. Statistical analysis of the a-consensus values allows estimation of the number of participant sensors. The main features of this approach are: algorithms are completely distributed, since they do not require leader election steps; sensors are not requested to transmit authenticative information (for example identificative numbers or similar data), and thus the strategy can be implemented whenever privacy problems arise. After a rigorous formulation of the paradigma we analyze some practical examples, fully characterize them from a statistical point of view, and finally we provide some general theoretical results and asymptotic analyses.

We analyze the asymptotic behavior of discrete-time, Markovian quantum systems with respect to a subspace of interest. Global asymptotic stability of subspaces is relevant to quantum information processing, in particular for initializing the system in pure states or subspace codes. We provide a linear-algebraic characterization of the dynamical properties leading to invariance and attractivity of a given quantum subspace. We then construct a design algorithm for discrete-time feedback control that allows to stabilize a target subspace, proving that if the control problem is feasible, then the algorithm returns an effective control choice. In order to prove this result, a canonical QR matrix decomposition is derived, and also used to establish the control scheme potential for the simulation of open-system dynamics.

We study feedback-controlled, discrete-time quantum Markovian dynamics focusing on pure-state stabilization problem. Assuming that the system is unitarily controllable, and accessible via a given quantum measurement, we explicitly construct a choice of control actions conditioned on the measurement outcome that globally stabilizes the target state for the averaged dynamics. A key step in deriving this result is the definition of a canonical QR decomposition for complex matrices.

Nowadays, wireless communication technologies arebeing employed in an ever increasing number of different appli-cation areas, including industrial environments. Benefits derivingfrom such a choice are manifold and include, among the others, re-duced deployment costs, enhanced flexibility and support for mo-bility. Unfortunately, because of a number of reasons that havebeen largely debated in the literature, wireless systems cannot bethought of as a means able to fully replace wired networks in pro-ductionplants,inparticular,whenreal-timebehaviorisakeyissue.In this paper, an analysis of the real-time performance that canbe achieved in quality-of-service (QoS)-enabled 802.11 networkshas been carried out. In particular, a detailed analysis of latenciesand packet loss ratios for a typical enhanced distributed channelaccess(EDCA)infrastructurewirelesslocalareanetwork(WLAN)is presented, obtained through numerical simulations. A numberof aspects that may affect suitability for the use in control systemshave been taken into account, including the Transmission Oppor-tunity (TXOP) mechanism, the internal architecture of the AP, theuse of a time-division multiple access (TDMA)-based communica-tion scheme as well as the adoption of broadcast communications.

Wind energy is a random, uncontrollable, and highly variable source of energy, which will lead to serious challenges to grid integration at deep penetration levels. It is clear that the current extra-market approach to supporting wind integration (ex: California's Participating Intermittent Renewable Program (PIRP)) will not scale with an increase in installed wind capacity. At these deep penetration levels, forecasting, storage, demand response, and novel market mechanisms all become necessary ingredients to realize the promise of wind energy. In this paper, we examine the interplay between energy storage and market strategies to address the difficulties of wind integration. We attempt to answer the basic question: when faced with uncertainty in future market prices and wind power, how should a scheduling coordinator for a wind power plant exploit the ability to store energy so as to maximize revenue when scheduling wind power in a sequence of nested markets (day-ahead, hour-ahead, and real-time)? The problem is framed as a nonstandard chance-constrained model predictive control (MPC) problem. In formalizing the problem, we develop idealized models for electricity markets, wind energy revenue, energy storage, and the physical wind process. As chance constraints are, in general, nonlinear and difficult to handle in optimization problems, we suggest several stochastic models for wind that lead to tractable computations. This approach provides a contrast to scenario-based techniques for dealing with uncertainty in stochastic optimization problems.

Global asymptotic stabilization of quantum pure states is relevant to chemical process control, quantum cooling, state purification, and is crucial to the initialization of quantum information processing algorithms. We provide a linear-algebraic characterization of discrete-time Markovian dynamics leading to invariance and attractivity of a given quantum state. Assuming that the system is unitarily controllable, and accessible via a given quantum measurement, we provide a condition that that characterize the stabilizable target states. We also argue that if the control problem is feasible, then an effective control choice can be explicitly constructed. The result strongly relies on some remarlable properties of a canonical QR decomposition for complex matrices.