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This review concerns quantum control results and methods that, over the years, have been used in the various operations involving quantum systems. Most of these methods have been originally developed outside the context of quantum physics, and, then, adapted to take into account the specificities of the various quantum physical platforms. Quantum control consists in designing adequate control signals required to efficiently manipulate systems conforming the laws of quantum mechanics in order to ensure the associated desired behaviours and performances. This work attempts to provide a thorough and self-contained introduction and review of the various quantum control theories and their applications. It encompasses issues spanning quantum control modelling, problem formulation, concepts of controllability, as well as a selection of the main control theories. Given the vastness of the field, we tried our best to be as concise as possible, and, for the details, the reader is pointed out to a profusion of references. The contents of the review are organized in the three major classes of control problems - open-loop control, closed-loop learning control, and feedback control - and, for each one of them, we present the main developments in quantum control theory. Finally, concerning the importance of attaining robustness and reliability due to inherent fragility of quantum systems, methods for quantum robust control are also surveyed.

Adaptive neuronal stimulation has a strong therapeutic potential for neurological disorders such as Parkinson’s disease and epilepsy. However, standard stimulation protocols mostly rely on continuous open-loop stimulation. We implement here, for the first time in neuronal populations, two different Delayed Feedback Control (DFC) algorithms and assess their efficacy in disrupting unwanted neuronal oscillations. DFC is a well-established closed-loop control technique but its use in neuromodulation has been limited so far to models and computational studies. Leveraging on the high spatiotemporal monitoring capabilities of specialized in vitro platforms, we show that standard DFC in fact worsens the neuronal population oscillatory behaviour and promotes faster bursting, which was never reported in silico. Alternatively, we present adaptive DFC (aDFC) that monitors ongoing oscillation periodicity and self-tunes accordingly. aDFC disrupts collective neuronal oscillations and decreases network synchrony. Furthermore, we show that the intrinsic population dynamics have a strong impact in the susceptibility of networks to neuromodulation. Experimental data was complemented with computer simulations to show how this network controllability might be determined by specific network properties. Overall, these results support aDFC as a better candidate for therapeutic neurostimulation and provide new insights regarding the controllability of neuronal systems.

DynamiCITY ( NORTE-01-0145-FEDER-000073 ), funded by NORTE2020/PORTUGAL2020 , through the European Regional Development Fund . Publisher Copyright: © 2022 The Author(s) This paper addresses the design problem of a resilient consensus algorithm for agents with continuous-time dynamics. The main proposal is that by incorporating a switching mechanism selecting the network topology to avoid malicious nodes from communicating, the remaining nodes will converge to a value closer to the original steady-state without the attacker being present. The switching occurs at discrete-time steps where each node evaluates the reputation score of the neighbors and deactivates/ignores edges in the network. We explore the proposed method with illustrative examples ranging from static topologies to dynamic ones, considering directed and undirected graphs, presenting several attacking scenarios that are successfully mitigated with our method. Finally, we compare the best undetectable attacking strategy and the commonly used approach named MSR, highlighting the advantages of our method. publishersversion published

Fractional-order dynamical networks are increasingly being used to model and describe processes demonstrating long-term memory or complex interlaced dependencies amongst the spatial and temporal components of a wide variety of dynamical networks. Notable examples include networked control systems or neurophysiological networks which are created using electroencephalographic (EEG) or blood-oxygen-level-dependent (BOLD) data. As a result, the estimation of the states of fractional-order dynamical networks poses an important problem. To this effect, this paper addresses the problem of minimum-energy state estimation for discrete-time fractional-order dynamical networks (DT-FODN), where the state and output equations are affected by an additive noise that is considered to be deterministic, bounded, and unknown. Specifically, we derive the corresponding estimator and show that the resulting estimation error is exponentially input-to-state stable with respect to the disturbances and to a signal that is decreasing with the increase of the accuracy of the adopted approximation model. An illustrative example shows the effectiveness of the proposed method on real-world neurophysiological networks., Comment: 9 pages, 7 figures

This chapter introduces an event-driven, logic-based communication system for decentralized control of a network of nonlinear systems (agents) with the objective of driving their outputs along predefined paths at desired speeds, while holding a desired formation pattern compatible with the paths. An extended cooperative path following (CPF) framework is adopted where communications among agents take place at discrete time instants, instead of continuously. The communication system takes into account explicitly the topology of the communications network, the fact that communications are discrete, and the cost of exchanging information among agents. The theoretical framework adopted allows for the consideration of communication losses and bounded delays. Conditions are derived under which the resulting multi-agent closed-loop system is input-to-state stable, that is stable and with guaranteed levels of performance in the presence of bounded external disturbances and measurement noise. The set-up derived is used to solve the problem of CPF control of multiple underactuated autonomous marine vehicles. The results of experimental field tests with a group of marine vehicles are presented and discussed.

We propose a numerical algorithm for multiple-vehicle motion planning that explicitly takes into account the vehicle dynamics, temporal and spatial specifications, and energy-related requirements. As a motivating example, we consider the case where a group of vehicles is tasked to reach a number of target points at the same time (simultaneous arrival problem) without colliding among themselves and with obstacles, subject to the requirement that the overall energy required for vehicle motion be minimized. With the theoretical setup adopted, the vehicle dynamics are explicitly taken into account at the planning level. This paper formulates the problem of multiple-vehicle motion planning in a rigorous mathematical setting, describes the optimization algorithm used to solve it, and discusses the key implementation details. The efficacy of the method is illustrated through numerical examples for the simultaneous arrival problem. The initial guess to start the optimization procedure is obtained from simple geometrical considerations, e.g., by joining the desired initial and final positions of the vehicles via straight lines. Even though the initial trajectories thus obtained may result in intervehicle and vehicle/obstacle collisions, we show that the optimization procedure that we employ in this paper will generate collision-free trajectories that also minimize the overall energy spent by each vehicle and meet the required temporal and spatial constraints. The method developed applies to a very general class of vehicles; however, for clarity of exposition, we adopt as an illustrative example the case of wheeled robots.

This paper addresses the problem of range-based autonomous underwater vehicle (AUV) localization in the presence of unknown ocean currents. In the setup adopted, the AUV is equipped with an attitude and heading reference system, a depth sensor, and an acoustic device that provides measurements of its distance to a set of stationary beacons. We consider the situation where the number of active beacons is not known in advance and may vary with time. The objective is to simultaneously localize the AUV and beacons, that is, to find their positions underwater. We start by deriving conditions under which it is possible to reconstruct the initial condition of the system under study. We consider the design model where the states evolve continuously with time, but the range measurements are only available at discrete instants of time, possibly in a nonuniform manner. For trimming maneuvers that correspond to AUV trajectories with constant linear and angular velocities expressed in the body frame, we show that if either the position of one of the beacons or the initial position of the AUV is known, then even without depth information the system is weakly observable (i.e., the set of states that are indistinguishable from a given initial configuration contains only a set of finite isolated points). If depth measurements are also available, then the system is observable even in the presence of unknown constant ocean currents. Equipped with these results, we then propose a novel observer for simultaneous AUV and beacon localization. The mathematical setup exploited borrows from minimum-energy estimation theory applied to continuous-time processes with discrete measurements, projection filters, and multiple-model estimation techniques. Convergence analysis of the resulting observer system yields conditions under which the estimation errors converge to a small neighborhood of the origin (whose size depends on the magnitude of the process and measurement noise). The results of field experiments with a robotic marine vehicle show the efficacy of the simultaneous AUV/multiple beacon localization system proposed.

Microgreens are a new class of edible vegetables harvested when seed-leaves have fully expanded and before true leaves have emerged. They are gaining increasing popularity as new culinary ingredients. However, no scientific data comparing the mineral content of microgreens and mature plants are available. Thus, the goal of this work was to perform a comparison between mineral profile and NO3– content of microgreens and mature lettuces. Results showed that microgreens possess a higher content of most minerals (Ca, Mg, Fe, Mn, Zn, Se and Mo) and a lower NO3– content than mature lettuces. Therefore, microgreens can be considered as a good source of minerals in the human diet, and their consumption could be an important strategy to meet children's minerals dietary requirements without exposing them to harmful NO3–.

We analyze the observability properties of the kinematic model of an autonomous underwater vehicle (AUV) moving in 3D, under the influence of ocean currents, using range and depth measurements. The results obtained shed light into the types of trajectories that an AUV may be requested to undergo in order to ensure observability, which is a crucial step in the design of single or multiple beacon positioning systems. We assume that the AUV is equipped with two sensor suites: the first computes the distance (range) of the AUV to single or multiple fixed transponders, while the second measures the vehicle's depth. In both situations, the vehicle has access to its heading angle. We further assume that the AUV undergoes maneuvers commonly known as trimming trajectories, that are naturally obtained when the inputs (thruster rpms and control surface deflections) are held constant. This is done for two main reasons: (i) the class of trajectories thus generated is sufficiently rich for a vast number of applications and (ii) from an observability-analysis standpoint they lead to mathematical tractability and allow for an intuitive physical interpretation. These facts stand in sharp contrast to common approaches adopted in the literature, where the characterization of trajectories that yield observability is only implicit and defies a simple interpretation. In the set-up adopted, the trimming trajectories are completely characterized by three variables: (a) linear body speed parallel to v parallel to; (b) flight-path angle gamma; and (c) yaw rate psi. We assume that parallel to v parallel to > 0, gamma, and psi are constant but otherwise arbitrary (within the constraints of the vehicle capabilities) and examine the observability of the resulting system with the two above mentioned sensor suites. We adopt definitions of observability and weak observability that seek inspiration from those proposed by Herman and Krener (1977) but reflect the fact that we consider specific kinds of maneuvers in 3D. We start with the single transponder case. For range measurements only, we show that in the absence of ocean currents the 3D kinematic model of an AUV undergoing trimming trajectories with nonzero flight-path angle and yaw rate is observable. In the case of non-zero but known ocean currents, identical results apply subject to the condition that the flight-path angle satisfies a current-related constraint. However, if the current is non-zero and unknown, the model is only weakly observable. The situation changes completely when both range and depth measurements are available. In this case, under the assumption that the yaw rate is different from zero, observability is obtained even when the flight-path angle is zero (vehicle moving in a horizontal plane) and there are non-zero unknown currents. These obvious advantages are lost if yaw rate is equal to zero, for in this case the model is only weakly observable. In all situations where the model is weakly observable we give a complete characterization of the sets of states that are indistinguishable from a given initial state. Finally, we show that the extended model that is obtained by considering multiple (at least two) transponders is observable in all situations if the yaw rate is different from zero. (C) 2015 International Federation of Automatic Control. Published by Elsevier Ltd. All rights reserved.

In many problems, agents cooperate locally so that a leader or fusion center can infer the state of every agent from probing the state of only a small number of agents. Versions of this problem arise when a fusion center reconstructs an extended physical field by accessing the state of just a few of the sensors measuring the field, or a leader monitors the formation of a team of robots. Given a link cost, the paper presents a polynomial time algorithm to design a minimum cost coordinated network dynamics followed by the agents, under an observability constraint. The problem is placed in the context of structural observability and solved even when up to $k$ agents in the coordinated network dynamics fail.

Machine-to-Machine (M2M) communications enable networked devices and services to exchange information and perform actions seamlessly without the need for human intervention. They are viewed as a key enabler of the Internet of Things (IoT) and ubiquitous applications, like mobile healthcare, telemetry, or intelligent transport systems. We survey existing work on mobile M2M communications, we identify open challenges that have a direct impact on performance and resource usage efficiency, especially the impact on energy efficiency, and we review techniques to improve communications. We review the ETSI standard and application protocols, and draw considerations on the impact of their use in constrained mobile devices. Nowadays, smartphones are equipped with a wide range of embedded sensors, with varied local and wide area connectivity capabilities, and thus they offer a unique opportunity to serve as mobile gateways for other more constrained devices with local connectivity. At the same time, they can gather context data about users and environment from the embedded sensors. These capabilities may be crucial for mobile M2M applications. Finally, in this paper, we consider a scenario where smartphones are used as gateways that collect and aggregate data from sensors in a cellular network. We conclude that, in order for their use to the feasible in terms of a normal depletion time of a smartphone's battery, it is a good advice to maximize the collection of data necessary to be transmitted from nearby sensors, and maximize the intervals between transmissions. More research is required to devise energy efficient transmission methods that enable the use of smartphones as mobile gateways.

Many wireless applications require a propagation model that describes the attenuation of the transmitted signal as a function of the distance between devices. Such channel models are derived commonly from signal strength measurements, and assume that the true distances between wireless terminals are known. In practice, however, the true distances may be unavailable or difficult to obtain, for instance in mobile scenarios or in the absence of line-of-sight. These conditions typically occur in forested environments, urban areas, etc. This paper addresses the problem of path loss model parameter estimation in presence of erroneous distance measurements, such as the ones derived from the GPS positions. We provide a model for the uncertainties, and study the impact of distance errors on the estimation of a log-distance channel model. Our main conclusion is that the path loss model can be estimated with a reasonable accuracy from unreliable distances, provided that the measurements are taken at distances beyond a few standard deviations of the GPS positioning error. In case the maximum communication range does not allow such large distances, we provide a method to correct the erroneous channel model. Real-world measurements are used in order to validate our approach.

The paper addresses observability issues related to the general problem of single and multiple Autonomous Underwater Vehicle (AUV) localization using only range measurements. While an AUV is submerged, localization devices, such as Global Navigation Satellite Systems, are ineffective, due to the attenuation of electromagnetic waves. AUV localization based on dead reckoning techniques and the use of affordable motion sensor units is also not practical, due to divergence caused by sensor bias and drift. For these reasons, localization systems often build on trilateration algorithms that rely on the measurements of the ranges between an AUV and a set of fixed transponders using acoustic devices. Still, such solutions are often expensive, require cumbersome calibration procedures and only allow for AUV localization in an area that is defined by the geometrical arrangement of the transponders. A viable alternative for AUV localization that has recently come to the fore exploits the use of complementary information on the distance from the AUV to a single transponder, together with information provided by on-board resident motion sensors, such as, for example, depth, velocity and acceleration measurements. This concept can be extended to address the problem of relative localization between two AUVs equipped with acoustic sensors for inter-vehicle range measurements. Motivated by these developments, in this paper, we show that both the problems of absolute localization of a single vehicle and the relative localization of multiple vehicles can be treated using the same mathematical framework, and tailoring concepts of observability derived for nonlinear systems, we analyze how the performance in localization depends on the types of motion imparted to the AUVs. For this effect, we propose a well-defined observability metric and validate its usefulness, both in simulation and by carrying out experimental tests with a real marine vehicle during which the performance of an Extended Kalman Filter state observer is shown to depend on the types of motion imparted to the vehicle.

This paper addresses the tracking problem of the state variables of a nonlinear planar dynamic model of an overactuated electric vehicle with four-wheel independent drive (4WID) topology. In order to track the state variables of the system it is proposed a new sliding mode controller based on a nonlinear planar model. The controller explores the overactuated system in order to redistribute the control effort to the remaining actuators when a fault occurs. Although the system has multiple solutions due to the access of the torque applied in each wheel independently, there could be particular fault events where the remaining healthy actuators may not be able to maintain the system stability. In those particular cases the inclusion of the steering control variable is an important advantage as it allows the controller to manipulate the control effort in any directions. The proposed controller is validated in various driving scenarios with different fault schemes. The simulations are carried out with a high-fidelity vehicular model provided by the simulation software Carsim in co-simulation with Matlab/Simulink.

Motivated by applications in visual servoing, we consider the state estimation problem for a class of systems described by implicit outputs and whose state lives in the special Euclidean group SE(3). We propose an observer in the group of motion SE(3) that preserves invariance and therefore takes explicitly into consideration the geometry of the problem. We discuss conditions under which the linearized state estimation error converges exponentially fast. Furthermore, we analyze the problem when the system is subject to disturbances and noises and show that the estimate converges to a neighborhood of the real solution. The size of the neighborhood increases/decreases gracefully with the bound of the disturbance and noise. We apply and illustrate these results through an application of position and attitude estimation of a rigid body using measurements from a camera attached to the rigid body.

This paper addresses the problem of simultaneous depth tracking and attitude control of an underwater towed vehicle. The system proposed uses a two-stage towing arrangement that includes a long primary cable, a gravitic depressor, and a secondary cable. The towfish motion induced by wave driven disturbances in both the vertical and horizontal planes is described using an empirical model of the depressor motion and a spring-damper model of the secondary cable. A nonlinear, Lyapunov-based, adaptive output feedback control law is designed and shown to regulate pitch, yaw, and depth tracking errors to zero. The controller is designed to operate in the presence of plant parameter uncertainty. When subjected to bounded external disturbances, the tracking errors converge to a neighbourhood of the origin that can be made arbitrarily small. In the implementation proposed, a nonlinear observer is used to estimate the linear velocities used by the controller thus dispensing with the need for costly sensor suites. The results obtained with computer simulations show that the controlled system exhibits good performance about different operating conditions when subjected to sea-wave driven disturbances and in the presence of sensor noise. The system holds promise for application in oceanographic missions that require depth tracking or bottom-following combined with precise vehicle attitude control. published

This paper addresses the problem of steering a group of vehicles along given spatial paths while holding a desired time-varying geometrical formation pattern. The solution to this problem, henceforth referred to as the coordinated path-following (CPF) problem, unfolds in two basic steps. First, a path-following (PF) control law is designed to drive each vehicle to its assigned path, with a nominal speed profile that may be path dependent. This is done by making each vehicle approach a virtual target that moves along the path according to a conveniently defined dynamic law. In the second step, the speeds of the virtual targets (also called coordination states) are adjusted about their nominal values so as to synchronize their positions and achieve, indirectly, vehicle coordination. In the problem formulation, it is explicitly considered that each vehicle transmits its coordination state to a subset of the other vehicles only, as determined by the communications topology adopted. It is shown that the system that is obtained by putting together the PF and coordination subsystems can be naturally viewed as either the feedback or the cascade connection of the latter two. Using this fact and recent results from nonlinear systems and graph theory, conditions are derived under which the PF and the coordination errors are driven to a neighborhood of zero in the presence of communication losses and time delays. Two different situations are considered. The first captures the case where the communication graph is alternately connected and disconnected (brief connectivity losses). The second reflects an operational scenario where the union of the communication graphs over uniform intervals of time remains connected (uniformly connected in mean). To better root the paper in a nontrivial design example, a CPF algorithm is derived for multiple underactuated autonomous underwater vehicles (AUVs). Simulation results are presented and discussed.

This paper addresses the problem of simultaneous depth tracking and precise attitude control of an underwater towed vehicle integrated in a two-stage towing arrangement. A nonlinear Lyapunov-based output feedback controller is designed to operate in the presence of plant parameter uncertainty and proven to regulate pitch, yaw, and depth tracking errors to zero. When subjected to bounded external disturbances, the tracking errors converge to a neighborhood of the origin that can be made arbitrarily small. In the implementation proposed, a nonlinear observer is used to estimate the linear velocities used by the controller. The results obtained with computer simulations including sea wave driven disturbances and sensor noise, show that the controlled system exhibits good performance about different operating conditions and holds considerable potential for oceanographic missions that require simultaneous depth and attitude control.

This paper addresses the problem of underwater navigation, using a single beacon (or a transponder). We propose a solution to estimate the underwater vehicle position in the presence of unknown ocean currents. The main idea is to combine the dead-reckoning information with multiple range measurements taken at different instants of time from the vehicle to a single beacon. Then, applying a multilateration based algorithm and using a Kalman filter, the unknown velocity of ocean current and a more accurate estimation of vehicle's position is estimated. The stability and convergence of the position estimation error are analysed taken explicitly into account the presence of ocean currents, disturbances, measurement noise and discretization errors. Simulation results are presented and discussed. In particular, we show that the implementation of the Kalman filter to estimate the ocean current is crucial to achieve convergence of the estimated position.

We address the problem of steering multiple unmanned air vehicles (UAVs) along given paths (path-following) under strict temporal coordination constraints requiring, for example, that the vehicles arrive at their final destinations at exactly the same time. Path-following relies on a nonlinear Lyapunov based control strategy derived at the kinematic level with the augmentation of existing autopilots with ℒ1 adaptive output feedback control laws to obtain inner-outer loop control structures with guaranteed performance. Multiple vehicle time-critical coordination is achieved by enforcing temporal constraints on the speed profiles of the vehicles along their paths in response to information exchanged over a dynamic communication network. We consider that each vehicle transmits its coordination state to only a subset of the other vehicles, as determined by the communications topology adopted. We address explicitly the case where the communication graph that captures the underlying communication network topology may be disconnected during some interval of time (or may even fail to be connected at any instant of time) and provide conditions under which the closed-loop system is stable. Flight test results obtained at Camp Roberts, CA in 2008 and hardware-in-the-loop (HITL) simulations demonstrate the benefits of the algorithms developed.

We address the problem of position trajectory-tracking and path-following control design for underactuated autonomous vehicles in the presence of possibly large modeling parametric uncertainty. For a general class of vehicles moving in either 2- or 3-D space, we demonstrate how adaptive switching supervisory control can be combined with a nonlinear Lyapunov-based tracking control law to solve the problem of global boundedness and convergence of the position tracking error to a neighborhood of the origin that can be made arbitrarily small. The desired trajectory does not need to be of a particular type (e.g., trimming trajectories) and can be any sufficiently smooth bounded curve parameterized by time. We also show how these results can be applied to solve the path-following problem, in which the vehicle is required to converge to and follow a path, without a specific temporal specification. We illustrate our design procedures through two vehicle control applications: a hovercraft (moving on a planar surface) and an underwater vehicle (moving in 3-D space). Simulations results are presented and discussed.

This paper addresses the problem of dynamic positioning and way-point tracking of underactuated autonomous underwater vehicles (AUVs) in the presence of constant unknown ocean currents and parametric modelling uncertainty. A non-linear adaptive controller is proposed that steers an AUV along a sequence of way-points consisting of desired positions (x, y) in a inertial reference frame, followed by vehicle positioning at the final target point. The controller is first derived at the kinematic level assuming that the ocean current disturbance is known. An exponential observer for the current is then designed and convergence of the resulting closed-loop system trajectories is analysed. Finally, integrator backstepping and Lyapunov based techniques are used to extend the kinematic controller to the dynamic case and to deal with model parameter uncertainty. Simulation results with a dynamic model of an underactuated autonomous underwater shuttle for the transport of benthic labs are presented and discussed.

This brief presents a decentralized coordinated strategy for multirobot patrolling missions. Patrolling is here interpreted within the framework of the sampling problem. To be applied in practice, several realistic constraints and the time/spatial variance of the information are explicitly considered. The proposed approach is well rooted in the concepts of Voronoi tessellations and Gaussian Processes. Each robot, based only on local information, computes the next point to visit according to a given performance criteria. Numerical simulations and experiments involving three autonomous marine surface robots in a harbor scenario at the Parque Expo site in Lisbon, Portugal, are presented.

This chapter proposes a sampled-data model predictive control (MPC) architecture to solve the decentralized cooperative path-following (CPF) problem of multiple unmanned aerial vehicles (UAVs). In the cooperative path-following proposed scenario, which builds on previous work on CPF, multiple vehicles are required to follow pre-specified paths at nominal speed profiles (that may be path dependent) while keeping a desired, possibly time-varying, geometric formation pattern. In the proposed framework, we exploit the potential of optimization-based control strategies with significant advantages on explicitly addressing input and state constraints and on the ability to allow the minimization of meaningful cost functions. An example consisting of three fixed wing UAVs that are required to follow a given desired maneuver illustrates the proposed framework. We highlight and discuss some features of the UAVs trajectories.

This paper describes a multi-vehicle motion control framework for time-critical cooperative missions and evaluates its performance by considering two case studies: a simultaneous arrival mission scenario and a sequential auto-landing of a fleet of UAVs. In the adopted setup, the UAVs are assigned nominal spatial paths and speed profiles along those paths; the vehicles are then tasked to execute cooperative path following, rather than “open-loop” trajectory tracking. This cooperative strategy yields robust behavior against external disturbances by allowing the UAVs to negotiate their speeds along the paths in response to information exchanged over a supporting communications network.

This paper proposes a Model Predictive Control (MPC) sche-me to solve the target estimation and tracking problem. The objective is to derive a feedback law that drives an autonomous robotic vehicle to follow a target vehicle using an on-line estimate of the target’s state. In this scenario, when the target is observed through a nonlinear observation model, e.g., bearing only or range only sensors, it is possible to show that solving the tracking problem independently from the estimation problem can lead to an unsatisfactory result where the follower-target system is driven by the controller through unobservable or weakly observable trajectories and, as result, the state of the target vehicle cannot be recovered or cannot be recovered with high accuracy leading to the failure of the control strategy. In this paper, we propose an optimization based scheme that embeds, in a seamless way, an index of observability in the design of the target tracking controller resulting in a closed loop behavior that balances the objective of target tracking with the competing objective of maintaining a good estimate of the state of the target. Numerical results are presented that illustrate this type of behavior.

This chapter concerns a discrete-time sampling state feedback control optimizing framework for dynamic impulsive systems. This class of control systems differs from the conventional ones in that the control space is enlarged to contain measures and, thus, the associated trajectories are merely of bounded variation. In other words, it may well exhibit jumps. We adopt the most recent impulsive control solution concept that pertains to important classes of engineering systems and, in this context, present impulsive control theory results on invariance, stability , and sampled data trajectories having in mind the optimization-based framework that relies on an MPC-like scheme. The stability of the proposed MPC scheme is addressed.

This paper tackles the on-line unsupervised learning problem of Gaussian mixture models in the presence of uncertain dynamic environments. In particular, we assume that the number of Gaussian components (clusters) is unknown and can change over time. We propose a multi-hypothesis adaptive algorithm that continuously updates the number of components and estimates the model parameters as the measurements (sample data) are being acquired. This is done by incrementally maximizing the likelihood probability associated to the estimated parameters and keeping/creating/removing in parallel a number of hypothesis models that are ranked according to the minimum description length (MDL), a well-known concept in information theory. The proposed algorithm has the additional feature that it relaxes “the sufficiently large data set” restriction by not requiring in fact any initial batch of data. Simulation results illustrate the performance of the proposed algorithm.

2015 54th IEEE Conference on Decision and Control (CDC), ISBN:978-1-4799-7884-7, ISBN:978-1-4799-7886-1, ISBN:978-1-4799-7885-4

The NMR structure of the oxidised wild-type cytochrome c3 from Desulfovibrio vulgaris Hildenborough was determined in solution. Using a newly developed methodology, NMR data from the K45Q mutant was then grafted onto data from the wild-type protein to determine the structure in the region of the mutation. The structural origins of the redox-Bohr effect and haem–haem cooperativities are discussed with respect to the redox-related conformational changes observed in solution.

This paper addresses the problem of dynamic positioning of an underactuated autonomous underwater vehicle (AUV) in the horizontal plane. A nonlinear adaptive controller is proposed that yields convergence of the trajectories of the closed loop system to a desired target point in the presence of a constant unknown ocean current disturbance and parametric model uncertainty. The controller is first derived at the kinematic level assuming that the ocean current disturbance is known. An exponential observer is then designed and convergence of the resulting closed loop system is analyzed. Finally, integrator backstepping and Lyapunov based techniques are used to extend the kinematic controller to the dynamic case and deal with model parameters uncertainties. Simulation results are presented and discussed.

This paper derives a hybrid control law for an extended nonholonomic double integrator (ENDI) that captures the dynamics of a wheeled robot subject to force and torque inputs. A simple logic-based hybrid controller is proposed which yields global stability and convergence of the closed-loop system to an arbitrarily small neighborhood of the origin. This is achieved by mapping the state-space into a two dimensional closed positive quadrant space and dividing it into three overlapping regions where, for each region, a feedback law is conveniently designed. Convergence and stability of the closed-loop hybrid system are analyzed theoretically. An application is made to the control of a wheeled mobile robot of the unicycle-type. Simulation results are presented that illustrate the performance of the hybrid control law.

The paper describes the design, implementation, and testing at sea of the vehicle and mission control systems of Sirene, an autonomous underwater shuttle for the automatic deployment of benthic stations down to depths of 4000 meters.

NMR spectra of ferro- and ferricytochrome c 549 from Synechocystis sp. PCC 6803 have been analysed. Two-dimensional spectra were used to assign proton resonances from the haem in the reduced form and corresponding signals in the oxidised form were located by saturation transfer experiments with a partially oxidised sample. Further assignments of 1 H and 13 C resonances from the haem and its axial ligands were obtained from NOESY, TOCSY and HMQC spectra of the ferricytochrome. Characteristic signals from two His residues confirm that the haem has bis-histidinyl axial ligation. Analysis of the paramagnetic shifts of the carbon nuclei positioned α to the haem shows that the two imidazoles are nearly parallel and that they are oriented such that the projections of the normals to the rings onto the haem plane lie close to a line drawn between haem methyl 18CH 3 and thioether methine 8CH.

This paper seeks to understand and characterize policies that exploit multiple available technologies and/or het- erogeneous communication routes simultaneously with the goal of improving throughput and energy performance or economi- cal costs. Such policies may be instrumental towards allowing improved services to mobile devices in converged networks. We present an optimization framework and a set of allocation policies to provide efficient, channel-aware load allocation for multi- homed devices under different cost criteria. Network coding is used as a key enabler of these techniques as coding across packets requires less coordination, simpler and less frequent feedback mechanisms, and higher resiliency to changes in the transmission channel. Our formulation incorporates bursty erasure channels, multiple simultaneous communication routes, and different chan- nel coding and modulations available to each technology as part of the resource allocation optimization. Numerical results are pro- vided showing that dynamic allocation policies are instrumental to improving resource efficiency by reducing energy consumption and/or channel utilization. The energy gains of our proposed policies outperform the best fixed network coding multi-homing policies by a factor of 2 in some scenarios.

The article of record as published may be found at https://doi.org/10.2514/1.56538 This paper addresses the problem of steering a fleet of unmanned aerial vehicles along desired three-dimensional paths while meeting stringent spatial and temporal constraints. A representative example is the challenging mission scenario where the unmanned aerial vehicles are tasked to cooperatively execute collision-free maneuvers and arrive at their final destinations at the same time. In the proposed framework, the unmanned aerial vehicles are assigned nominal spatial paths and speed profiles along those, and then the vehicles are requested to execute cooperative path following, rather than open loop trajectory tracking maneuvers. This strategy yields robust behavior against external disturbances by allowing the unmanned aerial vehicles to negotiate their speeds along the paths in response to information exchanged over the supporting communications network. The paper considers the case where the graph that captures the underlying time-varying communications topology is disconnected during some interval of time or even fails to be connected at all times. Conditions are given under which the cooperative path-following closed-loop system is stable. Flight test results of a coordinated road-search mission demonstrate the efficacy of the multi-vehicle cooperative control framework developed in the paper.

International audience; This paper introduces Mean Field Games (MFG) as a framework to develop optimal estimators in some sense for a general class of nonlinear systems. We show that under suitable conditions the estimation error converges exponentially fast to zero. Computer simulations are performed to illustrate the method. In particular we provide an example where the proposed estimator converges whereas both extended Kalman filter and particle filter diverge.

This paper addresses the problem of observability of the relative motion of two AUVs equipped with velocity and depth sensors, and inter-vehicle ranging devices. We start by exploiting nonlinear observability concepts to analyze, using observability rank conditions, some types of relative AUV motions. Because rank conditions only provide binary information regarding observability, we then derive a specific observability index (metric) and study its dependence on the types of relative motions executed by the vehicles. In particular, it is shown that the degradation of observability depends on the range and angle between the relative velocity and position vectors. The problem addressed bears affinity with that of single beacon localization. For this reason, the results derived are validated experimentally in a equivalent, single beacon navigation scenario.

This paper considers the state estimation problem of a class of systems described by implicit outputs and whose state lives in the special Euclidean group SE(3). This type of systems are motivated by applications in dynamic vision such as the estimation of the motion of a camera from a sequence of images. We propose an observer in the group of motion SE(3) and discuss conditions under which the linearized state estimation error converges exponentially fast. We also analyze the problem when the system is subject to disturbances and noises. We show that the estimate converges to a neighborhood of the real solution. The size of the neighborhood increases/decreases gracefully with the bound of the disturbance and noise.

A soluble monoheme c-type cytochrome (cytochrome c6) has been isolated from the green alga Monoraphidium braunii. It has a molecular mass of 9.3 kDa, an isoelectric point of 3.6 and a reduction potential of 358 mV at pH 7. The determined amino acid sequence allows its classification as a class-I c-type cytochrome. The ferric and ferrous cytochrome forms and their pH equilibria have been studied using 1H-NMR, ultraviolet/visible, EPR and Mossbauer spectroscopies. The pH equilibria are complex, several pKa values and pH-dependent forms being observed. The amino acid sequence, the reduction-potential value and the visible and NMR spectroscopies data in the pH range 4-9 indicate that the heme iron has a methionine-histidine axial coordination. However, the EPR and Mossbauer data obtained for the ferricytochrome show that in this pH range two distinct forms are present: form I, gz = 3.27, gy = 2.05 and gx = 1.05; form II, gz = 2.95, gy = 2.29 and gx = 1.43. While form I has crystal-field parameters typical of a methionine-histidine coordination, those associated with form II would suggest a histidine-histidine axial ligation. This possibility was extensively analyzed by spectroscopic methods and by chemical modification of a histidine residue. It was concluded that form II actually corresponds to an unusual type of methionine-histidine axial coordination. Straightforward examples of this type of coordination have recently been found in other c-type hemeproteins [Teixeira, M., Campos, A. P., Aguiar, A. P., Costa, H. S., Santos, H., Turner, D. L. & Xavier, A. V. (1993) FEBS Lett. 317, 233-236], corroborating our proposal. Since both forms, with very distinct crystal-field parameters, are shown to have the same reduction potential, it may be concluded that the axial and rhombic distortions of the heme-iron ligand field cannot be directly correlated with the heme-reduction potential. The pH-dependence studies have also shown that the form I and form II are interconvertible, with pKa approximately 5. To establish a possible physiological significance for this process, in particular for the interaction of the cytochrome with the membrane-bound electron-transfer complexes b6f and photosystem I, the effect of surfactants on the spectroscopic characteristics of cytochrome c6 has been studied.

This paper addresses the problem of a coordinated road search by multiple Unmanned Air Vehicles (UAVs). The key requirement is for the road to be searched by the optical sensors residing on each UAV in such a way as to guarantee a non empty intersection of their respective fields of view. This problem is cast in the framework of coordinated path generation and path following for multiple UAVs developed in the earlier papers by the authors. Thus, initially the paper provides a brief overview of the coordinated path generation and path following framework. This is followed by the presentation of the flight test results where this framework was applied to a coordinated road search scenario.

Motivated by challenging mission scenarios, this paper tackles the problem of multiUnmanned Aerial Vehicle (UAV) cooperative control in the presence of time-varying communication networks. Specifically, we address the problem of steering a fleet of UAVs along given paths (path following) so as to meet spatial and/or temporal constraints. One possible scenario is the situation where a fleet of vehicles is tasked to execute collision-free maneuvers under strict spatial constraints and arrive at their final destinations at exactly the same time. The paper builds on previous work by the authors on coordinated path following and extends it to allow for time-varying communication topologies. Path following control in 3D builds on a nonlinear control strategy that is first derived at the kinematic level (outer-loop control). This is followed by the design of an L1 adaptive output feedback control law that effectively augments an existing autopilot and yields an inner-outer loop control structure with guaranteed performance. Multiple vehicle timecritical coordination is achieved by enforcing temporal constraints on the speed profiles of the vehicles along their paths in response to information exchanged over a dynamic communication network. We address explicitly the situation where each vehicle transmits its coordination state to only a subset of the other vehicles, as determined by the communications topology adopted. Further, we consider the case where the communication graph that captures the underlying communication network topology may be disconnected during some interval of time (or may even fail to be connected at any instant of time) and provide conditions under which the complete coordinated path following closed-loop system is stable. Hardware-in-the-Loop (HITL) simulation results demonstrate the benefits of the developed algorithms.

We address the problem of designing decentralized feedback laws to force the outputs of decoupled nonlinear systems (agents) to follow geometric paths while holding a desired formation pattern. To this effect we propose a general framework that takes into account i) the topology of the communication links among the agents, ii) the fact that communications do not occur in a continuous manner, and iii) the cost of exchanging information. We provide conditions under which the resulting overall closed loop system is input-to-state stable and apply the methodology for two cases: agents with nonlinear dynamics in strict feedback form and a class of underactuated vehicles. Furthermore, we address explicitly the case where the communications among the agents occur with non-homogenous, possibly varying delays. A coordinated path-following algorithm is derived for multiple underactuated autonomous underwater vehicles. Simulation results are presented and discussed.

We study the synchronization problem for n single state agents with linear continuous time dynamics. The agent states are required to synchronize and travel at a desired common speed. This problem arises naturally in the design of coordinated path-following algorithms problem [9] and in studies on the synchronization of Kuramoto oscillator networks [17]. When the desired speed is zero or there are no time delays, it has been shown in the literature that a so-called neighboring control rule makes the states synchronize asymptotically under some connectivity conditions on the union of the underlying communication graphs. We will show that when both the desired speed and the communication delay are non-zero, the behavior of the synchronization system changes significantly. We start by considering asymmetric networks and switching topologies with homogeneous time delays. We then address some issues related to the behavior of the synchronization system in the presence of heterogeneous time delays. We provide connectivity conditions under which the synchronization problem is solved and introduce synchronization laws that compensates for the effect of non-zero speed and time delays. Simulations illustrate the synchronization of three agents.

This paper addressed the state estimation of a system with implicit output. We formulate the problem in the so-called deterministic H ∞ filtering setting by computing the value of the state that minimizes the induced L 2 -gain from disturbances to estimation error, while remaining compatible with the past observations. To avoid weihting the distant past as much as the present, a forgetting factor is also introduced. We show that, under appropriate observability assumptions, the optimal estimate converges globally asymptotically to the true value of the state in the absence of noise and disturbance. In the presence of noise, the esimate converges to a nieghborhood of the true value of the state. We apply these result to the estimation of position and attiude of an autonomous vehicle using measurements from an inertial measurement unit (IMU) and a monocular charged-coupled-device (CCD) camera attached to the vehicle.