Search

In this note, we consider a novel supervision algorithm for the distributed management of interconnected linear systems sharing coupling coordination constraints. The proposed supervision approach is based on the recently developed Turn-Based Command Governor strategy (Casavola et al. (2018)) where the agents in the networks follow a particular round-robin policy in updating their commands. Such a strategy is here extended to deal with so called Plug-and-Play (PnP) operations. Such a problem has been faced in a formal way by providing the conditions that allow PnP operations without violating existing constraints. In this respect, the notation of Pluggability of systems that aim at joining the same network is introduced as a structural property. A final example is presented to illustrate the effectiveness of the proposed strategy.

In underwater localization systems several sources of error may impact in different ways the accuracy of the final position estimates. Through simulations and statistical analysis it is possible to identify and characterize such sources of error and their relative importance. This is especially of use when an accurate localization system has to be designed within required accuracy prescriptions. This approach allows one to also investigate how much these sources of error influence the final position estimates achieved by an Extended Kalman Filter (EKF). This paper presents the results of experiments designed in a virtual environment used to simulate real acoustic underwater localization systems. The paper intends to analyze the main parameters that significantly influence the position estimates achieved by a Short Baseline (SBL) system. Specifically, the results of this analysis are presented for a proprietary localization system constituted by a surface platform equipped with four acoustic transducers used for the localization of an underwater target. The simulator here presented has the purpose of simulating the hardware system and modifying some of its design parameters, such as the base-line length and the errors on the GPS and Inertial Measurement Unit (IMU) units, in order to understand which parameters have to modify for improving the accuracy of the entire positioning system. It is shown that statistical analysis techniques can be of help in determining the best values of these parameters that permit to improve the performance of a real hardware system.

A fault detection design is proposed for addressing the Oscillatory Failure Case (OFC) detection problem, introduced in the joint Airbus-Stellenbosch university aerospace industrial-benchmark competition called at the IFAC 2020 World Congress 1 . The detection scheme is comprised of an output estimator, a wavelet decomposition and an energy-based denoising method, and the residual evaluation unit. The detection problem of wide frequency range OFCs is also addressed. According to the achieved simulation results, the proposed fault detection method is able to satisfy the competition prescriptions in the frequency range [1 10] Hz for those OFC’s having an amplitude greater than 2.3 mm for OFCs at rod position sensor, or 1.4 mA for OFCs at servo input current, regardless of disturbances level, uncertainties and load factor control input. In other cases, faults are detected slightly after the prescribed detection limit, with some interesting exceptions.

This paper reports the results of a recently concluded R&amp, D project, SCALS (Smart Cities Adaptive Lighting System), which aimed at the development of all hardware/software components of an adaptive urban smart lighting architecture allowing municipalities to manage and control public street lighting lamps. The system is capable to autonomously adjust street lamps&rsquo, brightness on the basis of the presence of vehicles (busses/trucks, cars, motorcycles and bikes) and/or pedestrians in specific areas or segments of the streets/roads of interest to reduce the energy consumption. The main contribution of this work is to design a low cost smart lighting system and, at same time, to define an IoT infrastructure where each lighting pole is an element of a network that can increase their amplitude. More generally, the proposed smart infrastructure can be viewed as the basis of a wider technological architecture aimed at offering value-added services for sustainable cities. The smart architecture combines various sub-systems (local controllers, motion sensors, video-cameras, weather sensors) and electronic devices, each of them in charge of performing specific operations: remote street segments lamp management, single street lamp brightness control, video processing for vehicles motion detection and classification, wireless and wired data exchanges, power consumptions analysis and traffic evaluation. Two pilot sites have been built up in the project where the smart architecture has been tested and validated in real scenarios. Experimental results show that energy savings of up to 80% are possible compared to a traditional street lamp system.

In scientific and technical diving, the survey of unknown or partially unexplored areas is a common task that requires an accurate planning for ensuring the optimal use of resources and the divers’ safety. In particular, in any kind of diving activity, it is essential to foresee the “dive profile” that represents the diver’s exposure to pressure over time, ensuring that the dive plan complies with the specific safety rules that have to be applied in accordance with the diver’s qualification and the environmental conditions. This paper presents a novel approach to dive planning based on an original underwater pathfinding algorithm that computes the best 3D path to follow during the dive in order to be able to maximise the number of points of interest (POIs) visited, while taking into account the safety limitations. The proposed approach, for the first time, considers the morphology of the 3D space in which the dive takes place to compute the best path, taking into account the decompression limits and avoiding the obstacles through the analysis of a 3D map of the site. Moreover, three different cost functions are proposed and evaluated to identify the one that could suit the divers’ needs better.

Predictive control presents an intrinsic capability to mitigate faulty events that can be achieved by properly defining operational constraints and/or running costs. Within this context this work presents a model predictive control (MPC) approach to deal with faults related to loss of actuators effectiveness. These undesired events occur when the associated degradation effects, whose evolution is assumed to be measurable and predictable, overcomes certain thresholds. The presented scheme consists of two modules: a prognostic device in charge of monitoring actuators health and a control reconfiguration unit whose action is based on actuators degradation information. The idea exploits a switching systems paradigm to formally model healthy and faulty plant configurations and to offline determine sequences of pre-computed inner approximations of one-step controllable sets. Such regions are then on-line selected by a switching logic to compute a proper control signal on the basis of the measured degradation level.

This paper illustrates the derivation of a linear parameter varying (LPV) model approximation of a turbocharged Spark-Ignition (SI) automotive engine and its usage in designing a model-based fault detection and isolation (FDI) scheme. The LPV approximation is derived from a detailed nonlinear mathematical model of the engine on the basis of the well known Jacobian approach. The resulting LPV representation is then exploited for synthesizing a bank of LPV-FDI H ∞ / H − Luenberger observers. Each observer is in charge of detecting a particular class of fault and is designed for having low sensitivity to all other exogenous inputs so as to allow an effective fault isolation. The adopted FDI scheme is gain-scheduled and exploits a set of engine variables, assumed to be measurable on-line, as a scheduling parameters. The goodness of the LPV approximation of the engine model and the effectiveness of the LPV-FDI architecture are demonstrated by several numerical simulations.

This paper proposes two fault-tolerant sensor reconciliation design methods for over-sensed plants. The aim of the reconciliator is to detect the presence of faults on the existing physical...

This paper presents a fault-tolerant sensor reconciliation design approach for over-sensed plants (see Fig. 1). The reconciliator is in charge of detecting, at each time instant, the possibly faulty physical sensors y and generating a virtual output z (with dimy ≥ dimz) where the corrupted measures coming from the pool of redundant physical output are removed. In this way the virtual output is always healthy and usable for control purposes without requiring the reconfiguration of the nominal control law. The approach is based on the use of Unknown Input Observers (UIO) with Linear Fractional Transformation (LFT) parameter dependency and works together with an "ad-hoc" parameters estimator that is designed to estimate on-line at each time instant the sensor effectiveness matrix. The sensor faults here considered are limited to variation of sensors’ gain and offset values. All main properties of the scheme are investigated and rigorously proved. A final simulation example is included to show the effectiveness of the proposed scheme.

In this paper, we propose a distributed sensor selection architecture for a class of networked systems characterized by three distinct groups of nodes: plants, sensors and computational agents. We address the problem of fault tolerant state estimation by fusing information on the plants provided by possibly faulty sensors via a formal exploitation of trustworthiness concepts. Specifically, the scheme uses the opinions (trust) that agents have on the quality of the measurements to select the most suitable sensors for state estimation purposes. The trust is evaluated via a novel and original adaptive reputation mechanism that is in charge of intercepting those sensors that minimize an ad-hoc criterion on the degradation level of the state measurements. Finally, it is formally proved that the resulting estimation scheme is always capable to select the healthy sensor in finite-time. Some numerical simulations instrumental to show the capability of the proposed reputation mechanism to efficiently select healthy sensors from a given set are finally reported.

High penetration of Distributed Generation (DG) plants in Medium Voltage (MV) / Load Voltage (LV) power grids may lead to abrupt voltage raises. Typical critical scenarios are represented by either low demand conditions or high power production from renewable sources. Traditional approaches used to face such a situation involve both the disconnection of the distributed generators and the curtailment of the generated power leading to several disadvantages. However, new technologies allow an active orchestration between some controllable devices of the grid, e.g. distributed generators, MV/HV transformers, storage devices in order to maintain relevant system variables within prescribed operative constraints in response to unexpected adverse conditions. This work addresses the online management of distributed generators and storage devices. The approach is based on Command Governor (CG) ideas and is based on the resolution at each time instant of an optimization problem containing explicit constraints related to voltage bounds and operational limits of adopted devices.

Summary A novel distributed command governor (CG) supervision strategy relying on iterative optimization procedure is presented for multi-agent interconnected linear systems subject to pointwise-in-time set-membership coordination constraints. Unlike non-iterative distributed CG schemes, here all agents undertake several optimization iterations and data exchange before arriving to the optimal solution. As a result, these methods are able to achieve Pareto-optimal solutions not only in steady-state conditions as the ones based on non-iterative optimization procedures but also during transients and are not hampered by the presence of undesirable Nash-equilibria or deadlock situations. The main properties of the method are fully investigated and in particular its optimality, stability, and feasibility properties rigorously proved. A final example is presented where the proposed distributed solution is contrasted with existing centralized and distributed non-iterative CG solutions. Copyright © 2017 John Wiley & Sons, Ltd.

A significant amount of energy induced by road unevenness and vehicle roll and pitch motions is usually dissipated by conventional shock-absorbers. In this paper, a novel active multiobjective H∞ c...

A novel supervision scheme for the distributed management of decoupled interconnected linear systems sharing coupling coordination constraints is here proposed. It is based on the recently developed Turn-Based Command Governor strategy where the agents in the network, not jointly involved in any coupling constraint, are grouped into same particular subsets (Turns). On-line, on the basis of a round-robin policy, all agents belonging to the same turn are allowed to update simultaneously their commands while other agents keep applying their current commands. Such a strategy is here extended to safely allow Plug-and-Play (PnP) operations among the sub-systems. Formal conditions that guarantee PnP activities without violating existing constraints are provided. In this respect, the notation of Pluggability of systems that aim at joining the network is introduced as a structural property. Moreover, it is shown how to exploit the existing link to graph colorability theory to online re-configure the turns and the local CG units in response to a PnP request. A final example is presented to illustrate the effectiveness of the proposed strategy.

The paper presents the MOLUX project which aims to design and develop a system capable of supporting the underwater operators by means of innovative technologies for the diagnosis, recovery, management, conservation, and valorization of the archaeological and naturalistic assets in the marine environment. This system allows to increase the efficiency and the safety of the operations carried out in the underwater environment thanks to its capabilities: to make the diver aware of his/her position; to acquire geolocated data (images, videos, notes, environmental parameters); to optimize and monitor the planned dive route.

The Air-To-Fuel ratio (AFR) is a crucial parameter to ensure good quality combustion and high catalyst efficiency. This work addresses the problem of determining continuous-time estimates of AFR in turbocharged Spark Ignition (SI) engines on the basis of only binary (on/off) sparse measurements of the exhaust gas Oxygen determined by using a HEGO (Heated Exhaust Gas Oxygen) sensor placed at the catalytic converter input in place of a more expensive linear UEGO (Universal Exhaust Gas Oxygen) sensor, as nowadays standard in commercial cars. In this context, the HEGO sensor, providing at its output terminal only two voltage values, can be considered a binary sensor with irregular and sparse measurements in that the useful information in only present at the instants of the on/off and off/on transitions. The theory of linear hybrid observers with quantized outputs is used to single out an estimation scheme based on a recursive least-squares algorithm. A detailed convergence analysis of the state reconstruction error is also provided. The proposed hybrid observer scheme is then used in a PI control loop finalized to maintain the AFR close to a desired value. The effectiveness of the proposed method is demonstrated by numerical simulations.

In underwater localization several sources of error may impact in different ways the final estimation of the position. This applies to the Long Baseline (LBL), the Short Baseline (SBL) and the Ultra-Short Baseline (USBL) localization systems. Through simulations and statistical analysis, it is possible to identify and experimentally characterize such sources of error. It could be also investigated how much these sources of error influence the estimation results of an Extended Kalman Filter (EKF). This paper presents the results of experiments designed in a virtual environment, useful to represent real localization systems. Specifically, the system used for the simulation is constituted by a surface platform equipped with four acoustic transducers used for the localization of an underwater target.

An effective Air-to-Fuel Ratio (AFR) control is paramount to ensure a good combustion and high catalyst efficiency. This work addresses the problem of determining continuous-time estimates of AFR in turbocharged Spark Ignition (SI) engines on the basis of binary sparse measurements of the exhaust gas Oxygen. The latter are provided by a HEGO (Heated Exhaust Gas Oxygen) sensor installed at the catalytic converter input in place of a more expensive linear UEGO (Universal Exhaust Gas Oxygen) sensor, as nowadays common in commercial cars. The HEGO sensor outputs two voltage values only, corresponding respectively to low or high concentration of the residual Oxygen in the exhaust gas (on/off behavior). In view of this, it can be classified as a binary sensor generating irregular and sparse measurements in that the useful information is only present at the instants of the on/off and off/on transitions. An estimation scheme based on the use of a recursive least-squares algorithm has been designed by resorting to the theory of linear hybrid observers with quantized inputs. A detailed convergence analysis of the state reconstruction error is also provided. The proposed hybrid observer scheme is employed in a PI control-loop designed to maintain the AFR close to a desired value. The effectiveness of the proposed method is demonstrated by several numerical simulations based on both synthetic and real data.

This paper introduces a novel parallel distributed supervision strategy for interconnected linear systems subject to pointwise-in-time set-membership coordination constraints. The scheme is based on the Feed Forward Command Governor paradigm. It is assumed that the systems are locally regulated, possibly dynamically coupled, and connected via data links. The supervisor is in charge of enforcing coordination constraints by modifying, whenever necessary, the nominal reference signals so that the global coordination goal is achieved. The proposed method extends in a substantial way the sequential Distributed Command Governor previously developed by the authors by allowing each agent to modify its local reference in parallel with the others. The main idea consists in bounding each subsystem with a set of local artificial constraints able to ensure global constraints satisfaction. These virtual constraints are dynamically generated in a distributed way on the basis of the common information available to all agents. The proposed strategy is described and its properties analyzed. The performance of the obtained scheme is compared with previous solutions in the final example.

High penetration of Distributed Generation (DG) in Medium Voltage (MV) power grids is usually a reason of abrupt voltage raises. In this respect critical scenarios are represented by both low demand conditions and high power production from renewable sources. In order to cope with the possibly resulting voltage limits violation, a typical adopted approach is the disconnection of the distributed generators or the curtailment of the generated power leading to several disadvantages. To address this issue an online management of the reactive power of distributed generators is presented. The approach is based on Command Governor (CG) ideas and is aimed at solving a constrained voltage regulation problem in both centralized and distributed ways. The approach foresees an active coordination between some controllable devices of the grid, e.g. distributed generators, MV/HV transformers, in order to maintain relevant system variables within prescribed operative constraints in response to unexpected adverse conditions. Simulation results show that the proposed approach is more effective than approaches suggested by the current Italian norms on reactive power compensation.

This paper presents the initial stage of the development of an underwater localization system suitable for a flexible number of users. Multiple Autonomous Underwater Vehicles (AUVs) can work as a team and cooperate with other teams of AUVs without costly and acoustically active components, which saves energy and allows AUVs to remain silent. The main building blocks for such a system are: spiral wavefront beacon in conjunction with a standard (circular) acoustic modem, Chip Scale Atomic Clocks (CSAC), acoustic modems, state-of-the-art adaptive underwater networking and Cooperative Localization (CL) algorithms. Using the difference in time of arrival between the spiral wavefront and the modem circular wavefront, receivers will be able to determine the bearing to the source using only one hydrophone. Synchronizing vehicles Chip Scale Atomic Clocks (CSAC) with the beacon at the beginning of the mission and during the longer missions will ensure the vehicles ability to also calculate their distance from the beacon upon every message reception.

this paper the link between Graph colorability theory and the design of a distributed Command Governor (CG) strategy is investigated. The first link between distributed supervision of systems with constraints and colorability properties was highlighted in a previous work where the idea of Turnbased CG was introduced for dynamically decoupled systems subject to coupling constraints. In this paper we will extend such a scheme to the case of dynamically coupled systems and we will show that the problem of determining the the minimal number of turns along with maximization of the agents appearance in turns is equivalent to a particular graph coloring problem. Such a problem is presented in a formal way and its complexity properties deeply discussed. A final example is presented to illustrate the effectiveness of the proposed strategy.

This paper presents a fault-tolerant sensor reconciliation scheme for systems subject to sensor's gain and offset faults. The reconciliator is in charge of fault accommodation without requiring the reconfiguration of the nominal control law. Amongst many, the solution proposed here is based on the use of a Linear Parameter Varying Unknown Input Observers (LPV-UIO) that exploit a Taylor linearization to arrive to a convex optimization formulation for the observer gain design. The LPV-UIO observer is therefore used to hide possibly faulty measures from the pool of physical outputs in the generation of arguably healthy virtual outputs to be used for control purposes. The scheme is fully described and all of its properties investigated and proved. Finally, a simulation example is reported in details to show the effectiveness of the scheme.

This paper presents an urban smart lighting system capable to autonomously control the street lamp lighting level by exploiting data related to vehicles (bus, car, motorcycle and bike) and/or pedestrians traffic in a specific area. The system is able to set the lighting level on the basis of required needs and allows one to reduce energy costs. To this end, it makes use of local controllers, motion sensors, video-camera and electronic devices for video processing. In this way the inputs coming from the sensors is processed in order to optimally dim the lighting by applying an 1–10 V control input voltage to the fixture or driver. The control can be performed either on a decentralized fashion on each single street lamp or on a group of them. Experimental results show that with respect to a traditional street lamp system, the presented architecture allows energy saving up to 65%.

Community-based microgrid (C- $\mu $ Grid) systems are gaining increasing importance nowadays because of the lack of $\mu $ Grid public investment and management policies. Technoeconomic analysis shows that C- $\mu $ Grid based on a cluster of microgenerators could be an effective solution when individual systems are not feasible. In this paper, the controlling capability of the central controller of the C- $\mu $ Grid is improved through an economic model predictive control (EMPC) approach operating at the pricing level that can fulfill the goal of the operational control of the cluster. With a central controller, it is capable of satisfying the demand at prosumer (active energy producer and simultaneous consumer) sides and, at the same time, optimizing the various $\mu $ Grid contrasting constraints. Emphasis here has been given to the operational constraints related to the battery lifetime, so that the maintenance and replacement costs would be reduced. A comparative analysis has been carried out between the performance of two systems, one based on an IF-THEN-ELSE heuristic supervision logic (S-LOGIC) and the other based on the proposed EMPC strategy. The analysis has been undertaken in a location in Dublin, Ireland, on the basis of available measured data. Simulation shows the effectiveness of implementing the EMPC approach to optimally manage the system.

The reachability properties of networked leader-follower configurations are analyzed for linear time-invariant systems subject to time-varying delay in the command and measure-ment channels. The novelty of the proposed approach relies upon the capability to combine sequences of inner approximations of one-step controllable sets with polyhedral piecewise state-space partitions.

Regulation problems for networked constrained polytopic Linear Parameter Varying (LPV) systems are considered. It is assumed that the communication network is subject to latency effects which result in induced time-varying time-delays on both the control and measurement channels. A novel LPV customization of a recent Receding-Horizon control (RHC) scheme developed for polytopic uncertain linear systems is here proposed. It exploits the LPV scheduling parameter availability for pre-computing nested families of one-step ahead controllable ellipsoidal sets, which are significantly less conservative than their robust counterparts. Finally, simulation results concerning the constrained regulation of a unicycle-type mobile robot are reported to show the effectiveness and the benefits of the proposed control scheme.

This paper concerns the implementation of an efficient underwater acoustic network suitable for long lasting environmental monitoring in fish farming. Several hardware and software solutions have been designed and implemented to extend the network lifetime and to make the system autonomous and suitable for such an application scenario. The proposed system is composed of different components. The SUNSET Software Defined Communication Stack (SDCS) is used to provide networking capabilities to underwater nodes communicating acoustically through AppliCon SeaModem modems. The Hydrolab Series 5 probes are used to monitor the water quality. Lifetime of underwater nodes is extended through the use of a novel device that allows to harvest energy from underwater water currents via suitable propellers. In addition, novel sleep and wake up mechanisms have been designed and implemented into the underwater nodes to minimize the energy consumption of the system during the idle periods. The performance of the proposed system has been extensively evaluated in field by monitoring the water quality in three fish farming cages located in the Mediterranean Sea, Italy. The system has been connected to the Internet infrastructure allowing the users to easy interact with the underwater system in real-time. Our results confirm that the proposed system is suitable for long term monitoring providing a reliable and robust data collection scheme with an extended network life time.

The chapter concerns the solutions of lane detection (LD) and lane tracking (LT) problems that are relevant in the implementation of lane departure warning systems (LDWSs), a kind of advanced driver assistance systems (ADASs) finalized to warn the driver that an imminent and possibly unintentional lane departure is taking place. The proposed solutions are based on simple image processing algorithms that work on the frames of a video stream of the oncoming road sections taken by a camera mounted on the front windshield of the vehicle. LD algorithms are finalized to identify the stripes demarcating the lane within a single frame, whereas LT algorithms try to track the demarcating stripes in subsequent frames of the stream. Final simulations on the software simulator CarSim 8 are also provided at the end of the chapter under realistic driving scenarios.

A novel distributed coordination strategy is presented for networked, locally regulated and possibly dynamically coupled, interconnected systems. Such systems are assumed to be connected via data links and subject to pointwise-in-time global constraints on some relevant variables of them, to be enforced as a coordination goal along the overall system evolutions. Such a coordination-by-constraint paradigm is accomplished by resorting to a novel distributed multi-agent Command Governor (CG) approach where each agent is in charge to locally modify, whenever necessary and on the basis of a reduced amount of data exchanged with the other agents, the prescribed set-points to the regulated subsystems so that the coordination constraints are always satisfied. The strategy is described and its main properties analyzed, especially for what it concerns the stability, feasibility and Pareto optimality of the solution. A liveness analysis concerning the possible presence of undesirable Nash equilibria and/or deadlock situations is presented and a discussion on the scalability of the solution with the problem dimension is reported as well. The constrained coordination of a network of interconnected water tanks is presented as a final example in order to show the effectiveness of the proposed strategy. © 1963-2012 IEEE.

Summary An application of a recently proposed fault detection and isolation (FDI) design methodology for linear parameter varying (LPV) systems is presented which concerns the robust detection of stator windings faults of an electrical induction motor regulated by a speed controller. On the basis of a detailed nonlinear mathematical model of the motor, it is shown how, based on a judicious convex interpolation of a family of linearized models, a quasi-linear parameter varying (quasi-LPV) approximation capable to catch most of the nonlinearities of the model can be achieved and can directly be used for synthesizing LPV-FDI observers. The design methodology consists in solving a multi-objective convex linear matrix inequalities optimization problem where disturbance rejection and fault sensitivity are traded-off in suitable frequency regions. The resulting diagnostic observer is gain-scheduled and uses a set of motor variables, assumed measurable online, as a scheduling vector. The effectiveness of the LPV-FDI framework is illustrated in a final numerical example. Copyright © 2014 John Wiley & Sons, Ltd.

SUMMARY A distributed supervisory strategy for addressing load/frequency set-point reconfiguration problems in networked multi-area microgrid is presented. The aim is at finding a distributed coordination strategy able to reconfigure, whenever necessary in response to unexpected load changes and/or faults, the nominal set-point on frequency and generated power to the generators of each area so that viable evolutions arise for the overall power system during transients and a new post-fault sustainable equilibrium is reached. In order to demonstrate the effectiveness of the strategy, an example on a four-area power system is presented. Copyright © 2014 John Wiley & Sons, Ltd.

SUMMARY In this paper, we propose a fault detection and isolation filter design method for internal combustion spark ignition engines. Starting from a detailed nonlinear mean-value mathematical description of the engine, a novel linear parameter varying (LPV) model approximation is derived on the basis of a judicious convex interpolation of a family of linearized models. A filter structure consisting of a bank of LPV observers is considered, each of them in charge of detecting a particular class of faults and exhibiting low sensitivity to all other faults and exogenous inputs. The resulting diagnostic filter is parameter-dependent in that a set of measurable engine variables is used online to suitably modify the filter gain so as to better take care of system nonlinearities. The quality of the LPV model approximation of the engine and the diagnostic capabilities of the fault detection and isolation architecture are demonstrated by a series of extensive numerical simulations. Copyright © 2013 John Wiley & Sons, Ltd.

This paper presents a fault-tolerant sensor reconciliation scheme for systems equipped with a redundant number of possibly faulty “physical” sensors. The reconciliator is in charge to discover on-line, at each time instant, the possibly faulty physical sensors and exclude their measures from the generation of the “virtual” sensors, which, on the contrary, are supposed to be always healthy and suitably usable for control purposes without requiring the reconfiguration of the nominal control law. Amongst many, the solution proposed here is based on the use of a Linear Parameter Varying Unknown Input Observers (LPV-UIO) coupled with an “ad-hoc” parameter estimator used to identify on-line the current sensor reconciliation matrix. The latter is therefore used to hide the faulty measures from the pool of physical outputs in the generation of the virtual outputs. For simplicity, the sensor faults here considered are limited to variation of sensors' gain and offset values. The scheme is fully described and all of its properties investigated and proved. Finally, a simulation example is reported in details to show the effectiveness of the scheme.

Novel distributed supervisory strategies for multi-agent linear systems connected via data networks and subject to coordination constraints are presented in this paper. Such a coordination-by-constraint paradigm is based on the online management of the prescribed set points and it is characterized by a set of spatially distributed dynamic systems, connected via communication channels, with possibly dynamical coupling amongst them which need to be supervised and coordinated in order to accomplish their overall objective. Two distributed strategies will be fully described and analysed. First, a “sequential” distributed strategy will be presented where only one agent per decision time is allowed to manipulate its own reference signal. Such a strategy will be instrumental to introduce a more effective “parallel” distributed strategy, in which all agents are allowed, under certain conditions, to modify their own reference signals simultaneously. Finally, some cases of study will be presented to show the effectiveness of the proposed methods.