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In this work, we face the problem of estimating the relative position and orientation of a camera and an object, when they are both equipped with inertial measurement units (IMUs), and the object exhibits a set of n landmark points with known coordinates (the so-called Pose estimation or PnP Problem). We present two algorithms that, fusing the information provided by the camera and the IMUs, solve the PnP problem with good accuracy. These algorithms only use the measurements given by IMUs’ inclinometers, as the magnetometers usually give inaccurate estimates of the Earth magnetic vector. The effectiveness of the proposed methods is assessed by numerical simulations and experimental tests. The results of the tests are compared with the most recent methods proposed in the literature. [ABSTRACT FROM AUTHOR]

This paper presents two novel results concerning the control allocation problem (CAP) for marine crafts with 2 azimuth thrusters. The first result is a novel closed form solution for the CAP which ensures Lipschitz continuity of the azimuth angle. The second result is a convex optimization reformulation of the CAP for the case of limited rate of changes for the actuators orientation which includes a novel regularization term that avoids anomalous behaviors due to underactuation. The effectiveness of the two methods are showcased through numerical simulations. • Novel closed-form solution enhances marine craft control with 2 azimuth thrusters. • Convex reformulation manages actuator rate of change, preventing anomalous behavior. • Simulations validate closed-form solution and convex optimization [ABSTRACT FROM AUTHOR]

In this paper, we present novel multi-variable Newton-based extremum seeking systems, based on Lie bracket approximation methods. More precisely, we consider cost functions with an unknown mathematical description, but whose value can be measured on-line. We propose extremum seeking systems that approximate the Newton-based optimization law, by combining the on-line measurement of the cost with time-periodic excitation signals. The inversion of the Hessian matrix is avoided by introducing a first order dynamical system, whose output approximates the Newton step. This provides practical robustness with respect to ill-conditioned Hessian matrices. Semi-global stability properties of the proposed schemes are demonstrated both for static cost functions and for cost functions associated with a general non-linear dynamical system. The effectiveness of the approach is shown in simulations. [ABSTRACT FROM AUTHOR]

Abstract In this paper we propose an approximated method for the design of feedforward actions based on model stable inversion. In the proposed framework the solution of the model stable inversion is approximated in the least squares sense by a suitable combination of any arbitrary set of basis functions. The paper makes use of operator theory to derive a closed-form formula to compute such approximation and to provide boundedness properties for the computed solution. Simulations are provided to show the effectiveness of the proposed method with different families of basis functions. [ABSTRACT FROM AUTHOR]

In this paper, we address a general eigenstructure assignment problem where the objective is to distribute the closed-loop modes over the components of the system outputs in such a way that if a certain mode appears in a given output, it is unobservable from any of the other output components. By combining classical geometric control results with the theory of combinatorics, we provide necessary and sufficient conditions for the solvability of this problem, herein referred to as state-to-output decoupling, under very mild assumptions. We propose solvability conditions expressed in terms of the dimensions of suitably defined controlled invariant subspaces of the system. In this way, the solvability of the problem can be evaluated a priori, in the sense that it is given in terms of the problem/system data. The proposed approach is constructive, so that when a controller that solves the problem indeed exists, it can be computed by using the machinery developed in this paper. [ABSTRACT FROM AUTHOR]

The explicit reference governor is a constrained control scheme that was originally introduced for generic nonlinear systems. This paper presents two explicit reference governor strategies that are specifically tailored for the constrained control of linear time-invariant systems subject to linear constraints. Both strategies are based on the idea of maintaining the system states within an invariant set which is entirely contained in the constraints. This invariant set can be constructed by exploiting either the Lyapunov inequality or modal decomposition. To improve the performance, we show that the two strategies can be combined by choosing at each time instant the least restrictive set. Numerical simulations illustrate that the proposed scheme achieves performances that are comparable to optimisation-based reference governors. [ABSTRACT FROM AUTHOR]

Summary: In this paper, a distributed command governor (CG) strategy is introduced that, by the use of graph colorability theory, improves the scalability property and the performance of recently introduced distributed noncooperative sequential CG strategies. The latter are characterized by the fact that only 1 agent at a decision time is allowed to update its command, whereas all the others keep applying their previously computed commands. The scalability of these early CG distributed schemes and their performance are limited because the structure of the constraints is not taken into account in their implementation. Here, by exploiting the idea that agents that are not directly coupled by the constraints can simultaneously update their control actions, the agents in the network are grouped into particular subsets (turns). At each time instant, on the basis of a round‐robin policy, all agents belonging to a turn are allowed to update simultaneously their commands, whereas agents in other turns keep applying their previous commands. Then, a turn‐based distributed CG strategy is proposed and its main properties are analyzed. Graph colorability theory is used to determine the minimal number of turns and to distribute each agent in at least a turn. A novel graph colorability problem that allows one to maximize the frequency at which agents can update their commands is proposed and discussed. A final example is presented to illustrate the effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]

In this study, the authors address the problem of reaching a given percentage of an optimal cost, so as to guarantee a known reserve with respect to the optimum. This reserve aims to counteract fast changes in the operating conditions. The authors assume that the analytical expression of the cost function is not available. To tackle this problem, they propose a novel extremum seeking scheme, the sub‐optimal extremum seeking, that is able to keep a given margin with respect to the estimated optimal cost. Stability properties of the control scheme are proved and the effectiveness of the approach is validated in simulation. [ABSTRACT FROM AUTHOR]

Reference and command governors are add-on control schemes which enforce state and control constraints on pre-stabilized systems by modifying, whenever necessary, the reference. This paper surveys the extensive literature concerning the development of such schemes for linear and nonlinear systems. The treatment of unmeasured disturbances and parametric uncertainties is also detailed. Generalizations, including extended command governors, feedforward reference governors, reduced order reference governors, parameter governors, networked reference governors, and decentralized/distributed reference governors, are discussed. Practical applications of these techniques are presented and surveyed as well. A comprehensive list of references is included. Connections with related approaches, including model predictive control and input shaping, are discussed. Opportunities and directions for future research are highlighted. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of achieving monotonic tracking control for any initial condition (also referred to as global monotonic tracking control ). This property is shown to be equivalent to global non-overshooting as well as to global non-undershooting (i.e., non-overshooting and non-undershooting for any initial condition, respectively). The main objective of this paper is to prove that a stable system is globally monotonic if and only if all the rows of the output matrix are left eigenvectors of the space transition matrix. This property allows one to formulate the design of a controller which ensures global monotonic tracking as a convex optimization problem described by a set of Linear Matrix Inequalities (LMIs). [ABSTRACT FROM AUTHOR]

In this paper, the robust stability problem for uncertain linear continuous-time systems is faced making use of piecewise quadratic Lyapunov functions (PQLF). PQLF are obtained by partitioning the state space into polyhedral cones and by associating a quadratic form with each cone. The proposed formulation allows us to recover, as particular cases of PQLFs, not only the class of quadratic functions but also the class of polyhedral functions. In this way, we manage to show the universality of the class of PQLF for the robust stability problem. The main contribution of the paper is the formulation of a low-computational cost procedure for the stability analysis of uncertain linear systems. Several numerical examples are included in the paper, where the proposed approach is tested on some benchmark cases taken from the literature. Comparisons with existing methods show that the proposed method performs better under several aspects. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In this paper, we address the clock synchronization problem for wireless sensor networks. In particular, we consider a wireless sensor network where nodes are equipped with a local clock and communicate in order to achieve a common sense of time. The proposed approach consists of two asynchronous consensus algorithms, the first of which synchronizes the clocks frequency and the second of which synchronizes the clocks offset. This work advances the state of the art by providing robustness against bounded communication delays. A theoretical characterization of the algorithm properties is provided. Simulations and experimental results are presented to corroborate the theoretical findings and show the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Next-generation Multiprocessor, or Multicore, Systems-on-Chip offer very high computing performance at the expense of a very high power density unevenly distributed on the chip. The hot spots thus generated represent a significant source of performance and reliability degradation, as well as power consumption increase. In recent years, run-time thermal control strategies have been developed to deal with this issue by acting on some "computational knobs" (e.g., clock frequencies and supply voltages). In this context, schemes based on Model Predictive Control (MPC) are particularly suitable due to their capability to deal with constraints explicitly. In this paper, we first discuss relevant properties for the design of predictive controllers for thermal systems. Starting from the Partial Differential Equation (PDE) describing heat diffusion in a solid, we prove meaningful feasibility properties that can be leveraged for constraint reduction. We then present a procedure to derive approximated but effective modular thermal models intended to build an efficient distributed MPC. Finally, a two-layer control solution is proposed to maximize performance while preserving feasibility despite model approximations. The effectiveness of this approach is validated through extensive and realistic numerical simulations. • A distributed controller is proposed for the optimal thermal capping of MPSoCs. • Control feasibility conditions are studied exploiting a PDE thermal model. • A hysteresis controller is designed for decentralized ultimate thermal capping. • A distributed MPC strategy is employed for performance optimization. • The resulting two-layer solution is validated for relevant simulation benchmarks. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes a novel class of Command Governor (CG) strategies for input and state-related constrained discrete-time LTI systems subject to bounded disturbances in the absence of explicit state or output measurements. While in traditional CG schemes the set-point manipulation is undertaken on the basis of either the actual measure of the state or its suitable estimation, it is shown here that the CG design problem can be solved, with limited performance degradation and with similar properties, also in the case that such an explicit measure is not available. This approach, which will be referred to as the Feed-Forward CG (FF-CG) approach, may be a convenient alternative CG solution in all situations whereby the cost of measuring the state may be a severe limitation, e.g. in distributed or decentralized applications. In order to evaluate the method proposed here, numerical simulations on a physical example have been undertaken and comparisons with the standard state-based CG solution reported. [Copyright &y& Elsevier]

Abstract: In this paper we study a class of systems governed by piecewise continuous control laws, subject to measurement noise and such that more than one state configuration could be a desired point for the system to be stabilized. Such a problem is common in many practical situations, including, for example, vehicle attitude control, robotic manipulator positioning, deployment of multistable structures, etc. Our aim is to characterize, both for continuous and discrete-time models, control properties and switching policies ensuring that any possible state trajectory of the closed-loop system is robustly asymptotically convergent to one of the desired “target points”. First we will show that in the discrete-time case, conditions guaranteeing the asymptotic stability to a set of points can be derived by exploiting the intrinsic “holding” nature of the discrete-time framework. Such results can be extended to continuous time by means of standard sample-and-hold techniques. However, this kind of solution is revealed to be very conservative and then not realistically employable in many contexts. For such a reason further less restrictive policies will be introduced and investigated; these are mostly based on the concept of “strategy holding” in time (by providing a slower switching policy sampling time) and space (by using hysteresis regions). Those strategies will be characterized both in terms of global stabilizability and in terms of switching flexibility, i.e., the capability to react in a convenient way to sudden state changes. Driven by the above analysis an “opportunistic switching” strategy combining all the advantages of the strategies considered will be proposed and analyzed. Synthesis results are presented, providing a constructive procedure to design the proposed control laws. The paper is concluded with an application example based on the attitude control of a Ducted-Fan Aerial Vehicle, showing the effectiveness of the proposed approach in a practical application. [Copyright &y& Elsevier]

In this paper, the use of time‐varying piecewise quadratic functions is investigated to characterize the finite‐time stability of state‐dependent impulsive dynamical linear systems. Finite‐time stability defines the behavior of a dynamic system over a bounded time interval. More precisely, a system is said to be finite‐time stable if, given a set of initial conditions, its state vector does not exit a predefined domain for a certain finite interval of time. This paper presents new sufficient conditions for finite‐time stability based on time‐varying piecewise quadratic functions. These conditions can be reformulated as a set of Linear Matrix Inequalities that can be efficiently solved through convex optimization solvers. Different numerical analysis are included in order to prove that the presented conditions are able to improve the results presented so far in the literature. [ABSTRACT FROM AUTHOR]

Life tables are one of the most common tools to describe the biology of insect species and their response to environmental conditions. Although the benefits of life tables are beyond question, we raise some doubts about the completeness of the information reported in life tables. To substantiate these doubts, we consider a case study (Corcyra cephalonica) for which the raw dataset is available. The data suggest that the Gaussian approximation of the development times which is implied by the average and standard error usually reported in life tables does not describe reliably the actual distribution of the data which can be misleading and hide interesting biological aspects. Furthermore, it can be risky when life table data are used to build models to predict the demographic changes of the population. The present study highlights this aspect by comparing the impulse response generated by the raw data and by its Gaussian approximation based on the mean and the standard error. The conclusions of this paper highlight: i) the importance of adding more information to life tables and, ii) the role of raw data to ensure the completeness of this kind of studies. Given the importance of raw data, we also point out the need for further developments of a standard in the community for sharing and analysing data of life tables experiments. [ABSTRACT FROM AUTHOR]

This paper focuses on the constrained attitude control of a rigid body. We propose an Explicit Reference Governor (ERG) specifically designed to handle constraints on S O (3). Two types of constraints are considered: (i) input torque constraints, and (ii) geometric conic pointing constraints. The objective of this paper is to design a control law that is able to steer the attitude of a rigid body to the desired attitude reference while enforcing constraints at all times. To this end, the first step is to design a controller that makes the desired attitude asymptotically stable when constraints (i) and (ii) are neglected. Next, the proposed control scheme is augmented with a novel ERG scheme able to add constraint-handling capabilities to the overall architecture. All the proposed solutions are defined in terms of S O (3) and do not make use of any parameterization such as quaternions or Euler angles. Numerical simulations are carried out to show the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

This paper focuses on the control of a cooperative system composed of an Unmanned Aerial Vehicle (UAV) and an Unmanned Ground Vehicle (UGV) manipulating an object. The two units are subject to input saturations and collaborate to move the object to a desired pose characterized by its position and inclination. The dynamics are derived using the Euler–Lagrange method. A stabilizing control law is proposed where the UGV is tasked to deploy the object to a certain position whereas the UAV adjusts its inclination. In particular, a proportional–derivative control law is proposed for the UGV, and a cascade control is used for the UAV, where the inner loop controls the attitude of the UAV and the outer loop stabilizes the inclination of the object. The stability of the points of equilibrium is proven using small gain arguments. To ensure constraints satisfaction at all times, a reference governor unit is added to this stabilizing control law. Simulations and experimental results are provided to validate the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

Abstract This paper addresses the tracking problem of constant references for multiple-input multiple-output linear time-invariant systems. We provide necessary and sufficient conditions for the solvability of the problem under the minimal set of assumptions that guarantee the well-posedness of every control problem requiring stability. Our approach allows to solve tracking problems for systems which are possibly nonright-invertible, simply (not asymptotically) stabilizable, and possibly with invariant zeros at the origin. Our methodology is constructive and results in the design of a stabilizing feedback matrix and a feedforward signal which solve the problem. Graphical abstract [ABSTRACT FROM AUTHOR]

This paper focuses on the design of a stabilizing control law for an aerial vehicle physically connected to a ground station by means of a tether cable. By taking advantage of the tensile force acting along the taut cable, it is shown that the tethered UAV can maintain a non-zero attitude while hovering in a constant position. The control objective is to stabilize the desired configuration while simultaneously ensuring that the cable remains taut at all times. This leads to a nonlinear control problem subject to constraints. This paper provides a two-step solution. First, the system is stabilized using a cascade control scheme based on thrust vectoring. Then, constraint satisfaction is guaranteed using a novel Reference Governor scheme. [ABSTRACT FROM AUTHOR]

This paper provides sufficient conditions to assess the stability and Input-to-State stability of a class of second-order systems by only looking at the structure of the dynamic equations. These results are proven by using the Variable Gradient Method to build suitable Lyapunov functions. The paper includes a number of relevant examples that highlight the value of the contribution. [ABSTRACT FROM AUTHOR]

This article presents a novel information-based mission planner for a drone tasked to monitor a spatially distributed dynamical phenomenon. For the sake of simplicity, the area to be monitored is discretized. The insight behind the proposed approach is that, due to the spatiotemporal dependencies of the observed phenomenon, one does not need to collect data on the entire area, which is one of the main limiting factors in unmanned aerial vehicle (UAV) applications due to their limited autonomy. In fact, unmeasured states can be estimated using an estimator, such as a Kalman filter. In this context, the planning problem becomes the one of generating a flight plan that maximizes the quality of the state estimation while satisfying the flight constraints (e.g., flight time). The first result of this article is the formulation of this problem as a special orienteering problem where the cost function is a measure of the quality of the estimation. This results in a mixed-integer semidefinite formulation, which can be optimally solved for small instances of the problem. For larger instances, a heuristic is proposed, which provides suboptimal results. Simulations numerically demonstrate the capabilities and efficiency of the proposed path-planning strategy. We believe that this approach has the potential to increase dramatically the area that a drone can monitor, thus increasing the number of applications where monitoring with drones can become economically convenient. Note to Practitioners—This article was motivated by the problem of performing large-scale field monitoring activities using unmanned aerial vehicle (UAV), which at the moment is very time-consuming and limits the definitive adoption of UAVs for this kind of activities. This problem is caused by the limited autonomy of commercial UAVs and the lack of systematic ways to plan missions so as to maximize the amount of information collected. This work starts from the observation that, in many applications, the phenomena that one wants to observe have dynamics and statistical properties. Accordingly, data that are not directly measured can be estimated with a characterizable observation error. In this article, we develop the theoretical foundations for an information-based path planning and define the problem of designing the optimal mission as an optimization problem based on the knowledge of the monitored phenomenon. The presented results have the potential to dramatically improve the effectiveness of drones for monitoring applications. [ABSTRACT FROM AUTHOR]

In this paper, a novel wireless sensor network synchronization protocol for event-driven measurement applications is proposed. The objective is twofold: 1) to provide high accuracy in the area where an event is detected and 2) to ensure a long network lifetime. The complexity of the problem arises from the fact that these two properties are usually in conflict. To increase the synchronization accuracy, nodes are required to exchange synchronization packets at higher rate, thus impacting the network lifetime. Vice versa to ensure a long network life time, the number of packets to be exchanged should be minimized thus impacting the synchronization accuracy. A tradeoff can be achieved by observing that the packet rate should be increased only for the portion of the network surrounding the detected events as only these nodes require a higher accuracy to collect data. The proposed algorithm represents a formalization of this idea. Numerical and experimental results are provided to validate the effectiveness of the proposed algorithm. [ABSTRACT FROM PUBLISHER]

This paper tackles the constrained control problem of unmanned aerial vehicles with planar multirotors. The proposed solution splits the constrained control problem into two separate tasks, i.e. stabilization and constraint enforcement. It is shown that the problems addressed by each individual layer is much simpler than the original combined problem. For the unconstrained control of UAVs we consider a control scheme based on a cascade structure. The Lyapunov function for the stabilized cascaded system is then derived. Using this Lyapunov function, we develop an Explicit Reference Governor for constraint enforcement. Numerical simulation shows the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

The P-difference between two sets A and B is the set of all points, C , such that the sum of B to any of the points in C is contained in A. Such a set difference plays an important role in robust model predictive control and set-theoretic control. In this paper, we show that an inner approximation of the P-difference between two sets described by collections of polynomial inequalities can be computed using Sums of Squares Programming. The effectiveness of the procedure is shown with some computational examples. [ABSTRACT FROM AUTHOR]

Robotics in construction is an emerging field that aims to automate various construction activities. Among the various innovative technologies for the construction sector, in this paper we focus on robotic solutions for the bricklaying task. In particular, we describe and explain in detail the implementation of a control framework for a recently introduced multi-robot bricklaying concept, specifically designed for laying activities with large and heavy blocks. The multi-robot system subject of this work is based on the collaboration of a robotic manipulator and a crane. The control architecture proposed to perform the construction task belongs to the Explicit Reference Governor (ERG) formalism. The ERG is a constrained control structure that enforces the constraints of the system and ensures that the robotic system operates correctly and safely. The efficiency of the proposed solution is confirmed by experimental validation on a custom-made crane and a KUKA LBR IIWA14R820 robotic arm. [Display omitted] • Implementation of a constrained control for a multi-robot bricklayer system for construction activities. • The proposed control scheme does not rely on any online optimization problems. • Detailed explanation of the full-scale prototype and implementation of the control algorithm. • Experimental validation on a custom-made overhead crane and a robotic arm. [ABSTRACT FROM AUTHOR]

This paper presents a general control framework for the constrained control of multibody systems actuated by vectorized thrusters. A cascade control scheme augmented with a constraint-enforcement unit is proposed to stabilize the system while ensuring constraint satisfaction at all times. The cascade controller consists of an inner loop and an outer loop that are interconnected by a suitable mapping. The inner loop is tasked to control the attitude of the vectorized thrusters. The outer loop is designed to steer the task configuration of the system to a desired pose, while providing to the inner loop the desired attitude through a mapping. To prove the stability of the interconnected system, input-to-state stability (ISS) and small gain arguments are used. All stability properties are derived in the absence of constraints and are shown to be local. Hence, the control scheme is augmented with a Reference Governor (RG) to enforce constraints at all times. Simulations on an unmanned tiltrotor docked to a stationary platform for a refueling operation are carried out to demonstrate the effectiveness of the proposed control framework. [ABSTRACT FROM AUTHOR]

This paper is based on the observation that, during Covid-19 epidemic, the choice of which individuals should be tested has an important impact on the effectiveness of selective confinement measures. This decision problem is closely related to the problem of optimal sensor selection, which is a very active research subject in control engineering. The goal of this paper is to propose a policy to smartly select the individuals to be tested. The main idea is to model the epidemics as a stochastic dynamic system and to select the individual to be tested accordingly to some optimality criteria, e.g. to minimize the probability of undetected asymptomatic cases. Every day, the probability of infection of the different individuals is updated making use of the stochastic model of the phenomenon and of the information collected in the previous days. Simulations for a closed community of 10'000 individuals show that the proposed technique, coupled with a selective confinement policy, can reduce the spread of the disease while limiting the number of individuals confined if compared to the simple contact tracing of positive and to an off-line test selection strategy based on the number of contacts. [ABSTRACT FROM AUTHOR]

Solar thermal power technologies require storage systems to mitigate the natural variability of solar irradiation. Packed bed thermal storage systems (PBTES) offer a cost-effective solution using air as heat transfer fluid and rocks as a storage medium. Compared to its alternatives, however, PBTES presents a limited flexibility of operation due to the conventional unidirectional flow, which involves the progressive reduction of the outlet temperature during discharge and thus lowers the thermodynamic efficiency of the power cycle. The present study summarizes the progress on the design and optimal operation of a novel multi-extraction PBTES, a project that aims at mitigating its typically poor operational flexibility for solar power applications. To this end, a one-dimensional model with a high spatial resolution of a PBTES was developed, which includes four intermediate outlet points along the axial direction to investigate the benefits of optimal extraction operation. In order to reduce the computational burden, a coarser model of the storage system is used in combination with non-linear model predictive control (NLMPC). Through the optimal manipulation of the extraction valves, the output temperature is maintained close to a prescribed temperature throughout the discharge. The control admits not only constant temperature targets, but also time-varying scheduled profiles. This work describes the limitation of such a design and control approach and sets the direction for the future, more detailed analyses needed to demonstrate its applicability. [ABSTRACT FROM AUTHOR]

• A complete theoretical analysis of a modified version of the Scalar Reference Gorvernor subjected to OR constraints. • An additional case study has been described to show the effectiveness of the proposed method in a variety of situations. • Experimental validation on a Li-ion graphite/LCO battery. In this paper, we propose a solution for the control of the state-of-charge of a lithium-ion (Li-ion) battery cell. The aim is to design a control scheme able to charge the battery in a fast way while ensuring that the safety constraints of the electrochemical model (ECheM) of the battery are not violated. The proposed solution is based on a novel computationally efficient formulation of the Reference Governor (RG) able to deal with the union of linear constraints and which has been designed on the basis of a reduced ECheM, namely the equivalent-hydraulic model (EHM). Using suitable safety margins, the satisfaction of the constraints on the EHM ensures the satisfaction of the constraints on the full-order ECheM. Simulation results obtained on an accurate simulator show that the proposed method, if compared with classical charge strategies, is able to considerably improve the charge performance while ensuring the satisfaction of the constraints. These findings are validated experimentally on a Li-ion graphite/LCO battery. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes a fast ellipsoidal Model Predictive Control (MPC) strategy to address feedback regulation problems for constrained polytopic Linear Parameter Varying (LPV) systems subject to bounded disturbances. In order to deal with the specific non-convex structure of the state prediction tubes arising in LPV contexts, a new convexification procedure is proposed and, based on the off-line computation of a sequence of inner ellipsoidal approximations of exact one-step controllable sets, a computationally low-demanding MPC algorithm is presented. Comparisons with state-of-the-art MPC control algorithms for LPV systems are reported in a final numerical example where several methods are contrasted in terms of achievable domains of attraction, control performance, numerical burdens and memory requirements. [Copyright &y& Elsevier]

This paper introduces a novel general model based on Ordinary Differential Equations (ODEs) which is able to describe the population dynamics of a large class of insect pests. The proposed model is a physiologically-inspired generalization of a number of ad hoc models presented through the years in the literature. Its main feature is that it allows the systematic generation of a population model for a species by simply defining its key features, namely the sex ratio and the development, fertility, and mortality rates. The first part of the paper provides a detailed description of the model and shows that most ODE-based models existing in literature can be obtained as a special case of the proposed model. The second part of the paper shows an application of the model to the spotted wing drosophila Drosophila suzukii , which is a highly relevant pest in agriculture. The biological features of this species, i.e., the sex ratio and the various rates, were retrieved from the existing scientific literature. The obtained model was validated using data from a three-year survey conducted in two experimental fields. Results show that the model describes faithfully the experimental populations, although the simulations were performed completely in open-loop and without any adaptation of the parameters extracted from the existing literature. • Formulation of a novel physiologically-based model describing ectotherms. • A system of ordinary differential equations describes the identifiable life stages. • The model can describe the life cycle of most insect species. • Four models existing in literature can be obtained as a special case of the novel model. • Drosophila suzukii is considered as case study to validate the model with three-year field data. [ABSTRACT FROM AUTHOR]

The goal of this paper is to present a systematic method to compute reference dependent positively invariant sets for systems subject to constraints. To this end, we first characterize these sets as level sets of reference dependent Lyapunov functions. Based on this characterization and using Sum of Squares theory, we provide a polynomial certificate for the existence of such sets. Subsequently, through some algebraic manipulations, we express this certificate in terms of a Semi-Definite Programming problem which maximizes the size of the resulting reference dependent invariant sets. We then present some results implementing the proposed method to an example and propose some variants that may help in reducing possible numerical issues. Finally, the proposed approach is employed in the Model Predictive Control for Tracking scheme to compute the terminal set, and in the Explicit Reference Governor framework to compute the so-called Dynamic Safety Margin. The effectiveness of the proposed method in each of the schemes is demonstrated through simulation studies. [ABSTRACT FROM AUTHOR]

This paper studies application of different constrained control concepts for the control of Depth of Hypnosis (DOH) in closed-loop anesthesia to guarantee patient safety, while ensuring acceptable tracking performance. The core idea is to formulate Overdosing (OD) prevention and Blood Pressure Decrease (BPD) prevention as operational constraints, and then use a constrained control scheme to enforce the constraints satisfaction at all times. In this paper, three methods are studied: (1) Explicit Reference Governor (ERG), (2) Safety Preserving Control (SPC), and (3) Model Predictive Control (MPC). The performance of the methods is assessed with respect to a simulated surgical procedure for 44 patients whose models have been identified using clinical data. In particular, three realistic clinical scenarios are studied in this paper: (1) normal mode, (2) stimulation, and (3) low clearance. The results demonstrate cons and pros of each method in closed-loop anesthesia according to clinically relevant assessments. [ABSTRACT FROM AUTHOR]

In this work, we propose and demonstrate two low-computational MPC-based strategies to charge low-capacity commercial Li-ion batteries in the shortest time possible, while limiting the occurrence of degradation mechanisms. To do so, we use a low-complexity electrochemical model (the Equivalent Hydraulic Model, EHM). To reduce the computational burden of the resulting non-convex optimization problem, two approximations of the admissible region are considered: a time-invariant convexification, and a time-varying convex embedding. These approximations are experimentally tested on commercial LCO batteries (Turnigy 160mAh), under controlled ambient temperatures (10, 20, and 30 ∘C) and a non-controlled scenario. They are compared with the Constant Current-Constant Voltage (CCCV) protocol. Experimental results show that the proposed schemes achieve relevant reductions of the charging time with respect to the CCCV in thermal non-controlled and controlled environments. Results also highlight that the time-varying convex embedding does not significantly impact the computational time while further reducing the charging time. [ABSTRACT FROM AUTHOR]