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For a simplified elastic drive train with nonlinear coupling torque, this paper proposes an efficient model of the coupled system model in time-discrete form. It is obtained from piecewise-exact discretization of the continuous-time system model and allows precise, efficient feed-forward control in real time. The high model accuracy is validated in a hardware-in-the-loop setting on a laboratory test rig for elastomer couplings. The model-based control law allows safe operation of the test rig across the system’s resonance range.

In this paper, we describe the development of a simulation model. The model deals with the role of an assembly worker in the virtual environment. He performs sequences of common tasks in manufacturing processes like reaching, grasping, walking, lifting etc. We compare time reports by the analytical MTM method and reports automatically generated by the Siemens JACK 8.4 tool. The methodology gives an insight into influential parameters, dimensions and mass of object, which have an impact on the time reports. Results of our research show that the parameters differ in importance in the MTM and JACK analyses. For one combination of influential parameters, we perform a practical experiment and compare the obtained time analysis with previous results.

Based on symbolic and numeric manipulations, a model simplification technique is proposed in this paper for the linear fractional representation (LFR) and for the differential algebraic representation introduced by Trofino and Dezuo (2013). This representation is needed for computational Lyapunov stability analysis of uncertain rational nonlinear systems. The structure of the parameterized rational Lyapunov function is generated from the linear fractional representation (LFR) of the system model. The developed method is briefly compared to the n-D order reduction technique known from the literature. The proposed model transformations does not affect the structure of Lyapunov function candidate, preserves the well-posedness of the LFR and guarantees that the resulting uncertainty block is at most the same dimensional as the initial one. The applicability of the proposed method is illustrated on two examples.

The paper deals with the problem of defining the optimal topology for a distributed fault detection architecture. A partition-based distributed fault detection method is considered based on the model of the system. The system is decomposed into subsystems and each subsystem is monitored by one local diagnoser. Non-overlapping decompositions are considered. A novel cost function is proposed to measure the detectability properties of a distributed fault detection method, depending on the topology of the detection framework. Different objective functions are taken into account and compared in order to analyze the influence of the decomposition on fault detection performance. Preliminary numerical results show that the minimization of the coupling between subsystems could not be always the best choice for the fault detection performance, and that the proposed cost function minimization allows the reduction of the detection time with the considered fault detection method.

In this paper, the use of supervised classifiers for leak location in water distribution networks (WDN) is discussed. A comparative study is presented in the context of a benchmark network under the same leak and sensor placement scenarios. The comparison considers four classification tools widely used in the pattern recognition framework: Nearest Neighbor, Bayes Classifier, Artificial Neural Networks and Support Vector Machines. The classifiers’ selection is made by considering their different working principles and application advantages. Training and testing sets are formed by the residuals generated by using the EPANET hydraulic simulator. The robustness of the methods is compared with respect to the leak location performance under model parameter uncertainty, demand uncertainty, leak size uncertainty and sensor noise. The SVM performs similar or better than the other classifiers when all uncertainties are present.

The fault diagnosis of wind turbine systems has been proven to be a challenging task and motivates the research activities carried out through this work. Therefore, this paper deals with the fault diagnosis of wind turbines, and it proposes viable solutions to the problem of earlier fault detection and isolation. The design of the fault indicator involves a data-driven approach, as it represents an effective tool for coping with a poor analytical knowledge of the system dynamics, together with noise and disturbances. In particular, the data-driven proposed solution relies on neural networks that are used to describe the strongly nonlinear relationships between measurement and faults. The chosen network architecture belongs to the nonlinear autoregressive with exogenous input topology, as it can represent a dynamic evolution of the system along time. The developed fault diagnosis scheme is tested by means of a high-fidelity benchmark model, that simulates the normal and the faulty behaviour of a wind turbine. The achieved performances are compared with those of other control strategies, coming from the related literature. Moreover, a Monte Carlo analysis validates the robustness of the proposed solutions against the typical parameter uncertainties and disturbances.

In this paper, we present the design of a non-classical observer that constructs estimates of the state of continuous nonlinear systems at remote locations. The system is connected to the remote location through a communication channel which can only transmit limited amounts of data per unit of time. The observer is designed with the objective that it is as data-efficient as possible whilst possessing a certain robustness towards communication losses without any feedback in the communication channel. We provide bounds for the sufficient data-rate to implement the observer. We apply the theory to the problem of observing the Lorenz system over a channel with minimal data-rates.

In this paper, we present a method aiming at pattern prediction in networks of diffusively coupled nonlinear systems. Interconnecting several globally asymptotical stable systems into a network via diffusion can result in diffusion-driven instability phenomena, which may lead to pattern formation in coupled systems. Some of the patterns may co-exist which implies the multi-stability of the network. Multi-stability makes the application of common analysis methods, such as the direct Lyapunov method, highly involved. We develop a numerically efficient method in order to analyze the oscillatory behavior occurring in such networks. We show that the oscillations appear via a Hopf bifurcation and therefore display sinusoidal-like behavior in the neighborhood of the bifurcation point. This allows to use the describing function method in order to replace a nonlinearity by its linear approximation and then to analyze the system of linear equations by means of the multivariable harmonic balance method. The method cannot be directly applied to a network consisting of systems of any structure and here we present the multivariable harmonic balance method for networks with a general system’s structure and dynamics.

This paper considers the problem of surgery planning with elective and urgent patients. The proposed solutions follows three steps: 1) scheduling of elective patients:given the ordered waiting lists of elective patients, schedule them in the available Operation Rooms(ORs); 2) scheduling of urgent patients:one day in advance, schedule the urgent patients in the remaining time after the step 1; and 3) sequencing of the surgeries:the elective and urgent patients scheduled in each OR are sequenced. In the first step, the elective patients are scheduled for a target Elective Surgery Time(EST) in the ORs, trying to respect the order of the patients on the waiting list. In the second one, the urgent patients are scheduled in the remaining time ensuring that an urgent patient does not wait more than 48 hours. Finally, in the third step, the surgeries assigned to each OR (elective and urgent) are sequenced in such a way that the maximum time that an emergency patient should wait is minimized. Using real data from the Orthopedic Department of the "Lozano Blesa" hospital in Zaragoza, the scheduling obtained by the three steps approach are analyzed by simulations. The influence of different EST, i.e., different reservation policies of the ORs are analyzed in terms of performance and quality of the surgical service.

This paper introduces multi-robotic system ATEROS which can be applied in the area of the chemical, biological, radiological and nuclear (CBRN) protection. Potential dangers are identified by robots working in the autonomous mode. One particular problem is addressed - how to present those dangers to a human operator of a robot working in the telepresence mode. The technique of the augmented reality is used as a solution. It is assumed that the autonomous robot provides georeferenced information about the danger. The remotely controlled robot Orpheus-X4 is equipped with a precise GNSS receiver working in Real Time Kinematic (RTK) mode which provides accurate self-localization and with a camera. Knowing both the position and the orientation of the camera as well as its parameters, it is possible to calculate position of the danger within a picture provided by the camera. The augmented reality was successfully implemented to the Orpheus-X4 for a zero tilt. It was applied in a scenario of the gamma radiation sources localization but can be expanded to other cases even outside the CBRN domain.

This paper presents a moving horizon estimator (MHE) for estimating pose (attitude and position) of a dynamic system where pose measurements are available in the form of unit dual quaternions. A unit dual quaternion is an 8 parameter nonsingular representation of pose and has previously been used for pose estimation with Kalman filters (KF). We formulate a cost function in terms of the quaternion product and propose a MHE that includes the Nlatest measurements in the estimation. In addition, we suggest a measurement relation based on the Cayley transform of the noise, where the noise has a Gaussian distribution about the x-y-zand roll-pitch-yawparameters of the pose. The MHE is compared against the dual quaternion multiplicative extended KF (DQ-MEKF) and the twistor-based unscented KF (T-UKF) through 100 Monte Carlo simulations, where the simulated data is generated according to the defined system dynamics. It is found that the MHE gives more accurate pose estimation results.

The paper is devoted to construction of solutions in dynamic bimatrix games. In the model, the payoffs are presented by discounted integrals on the infinite time horizon. The dynamics of the game is subject to the system of the A.N. Kolmogorov type differential equations. The problem of construction of equilibrium trajectories is analyzed in the framework of the minimax approach proposed by N.N. Krasovskii and A.I. Subbotin in the differential games theory. The concept of dynamic Nash equilibrium developed by A.F. Kleimenov is applied to design the structure of the game solution. For obtaining constructive control strategies of players, the maximum principle of L.S. Pontryagin is used in conjunction with the generalized method of characteristics for Hamilton-Jacobi equations. The impact of the discount index is indicated for equilibrium strategies of the game and demand functions in the dynamic bimatrix game are constructed.

This paper develops sufficient conditions for the constrained robust stabilization of continuous-time polytopic linear systems with unknown but bounded perturbations. The attractive ellipsoid method (AEM) is employed to determine a robustly controllable invariant set,known as attractive ellipsoid,such that the state trajectories of the system asymptotically converge to a small neighborhood of the origin despite the presence of non-vanishing perturbations. To solve the stabilization problem, we employ the Finsler’s lemma and derive new linear matrix inequality (LMI) conditions for robust state-feedback control design, ensuring convergence of state trajectories of the system to a minimal size ellipsoidal set. We also consider the state and control constrained problem and derive extended LMI conditions. Under certain conditions, the obtained LMIs guarantee that the attractive ellipsoid is nested inside the bigger ellipsoids imposed by the control and state constraints. Finally, we extend our AEM approach to the gain-scheduled state-feedback control problem, where the scheduling parameters governing the time-variant system are unknown in advance but can be measured in real-time. Two examples demonstrate the feasibility of the proposed AEM and its improvements over previous works.

This paper presents sufficient conditions for the robust stabilization of discrete-time polytopic systems subject to control constraints and unknown but bounded perturbations. The attractive ellipsoid method(AEM) is extended and applied to cope with this problem. To tackle the stabilization problem, new linear matrix inequality (LMI) conditions for robust state-feedback control are developed. These conditions ensure the convergence of state trajectories of the system to a minimal size ellipsoidal set despite the presence of non-vanishing disturbances. The developed LMI conditions for the AEM are extended to deal with the problem of gain-scheduled state-feedback control, where the scheduling parameters governing the time-variant dynamical system are unknown in advance but can be measured in real-time. A feature of the obtained conditions is that the state-space matrices and Lyapunov matrix are separated. The desired robust control laws are obtained by convex optimization. Numerical simulations are given to illustrate the feasibility of the proposed AEM for robust disturbance rejection.

The paper discusses the existence of a linear manifold in a vicinity of a steady state for stabilization of the Hamiltonian systems arising in optimal control problems for economic growth models. It is shown that such stable manifold exists for almost all possible values of model parameters guaranteeing the existence of a steady state. Research is based on the qualitative analysis of the Hamiltonian dynamics, which plays a key role for investigating the asymptotic behavior of optimal trajectories. A procedure is proposed for stabilization of the Hamiltonian system, whose trajectories converge to equilibrium and approximate the optimal solution with the quadratic accuracy at a vicinity of the steady state. Basing on properties of the Hamiltonian matrices, the classification of steady states is provided and the sensitivity analysis for identification of their character is implemented with respect to model parameters. The proposed approach is applied to the model dealing with dynamic optimization of the resource productivity.

In this paper, we consider controlling a spacecraft swarm in a circular low Earth orbit (LEO) via controlling its density distribution. We build on our previous research on decentralized density feedback control, which generates smooth velocity fields as a function of time and position. Performing kernel density estimation, each spacecraft compares desired density profiles with local density estimates, and invokes heat equation based feedback controller to generate the velocity commands to be tracked. Governed by Clohessy-Wiltshire equations, each spacecraft uses the velocity commands to calculate the next vehicle state and two consecutive ∆Vcommands to transition between states described in the local-vertical local-horizontal (LVLH) frame. The continuity of the velocity field facilitates smooth and organized behavior for spacecraft and as the numerical results illustrate, the swarm density distribution converges to the desired density distributions.

This paper proposes a PID (proportional-integral-derivative) control design method to solve the regulation problem for a class of coupled multi-agent nonlinear uncertain stochastic systems, where each agent only has access to its own regulation error without communicating with others. A three-dimensional manifold will be constructed based on the information about the Lipschitz constants of both the unknown nonlinear drift and diffusion terms, such that the three parameters of each agent’s PID controller can be chosen arbitrarily from this common manifold. It will be shown that such an uncoupled PID design method can globally stabilize the whole nonlinear uncertain stochastic multi-agent system with the regulation error of each agent approaching to zero asymptotically.

This paper addresses the problem of reliable control of a vehicle active suspension system under actuator faults.The methodology based on event-triggering scheme is employed to design the controller and the proposed results are expressed in the form of linear matrix inequalities (LMIs). The motivation for introducing event-triggering control is to reduce the amount of data transmission in the system. The problem is contrived as a co-design problem to find both controller gain and event-triggering parameters. Finally, the efficacy of the proposed results is demonstrated for the system under fault through a simulation example. Also, a comparison is drawn between the sampled-data control and the proposed event-triggered control strategy.

In this paper, we evaluate the closed-loop performance of two switching strategies for a dual-hormone artificial pancreas (AP). The dual-hormone AP administers insulin and glucagon subcutaneously. Since insulin and glucagon have opposite effects, we want to avoid simultaneous injections of these two hormones. To handle non-simultaneous injections of insulin and glucagon, we compare model predictive control (MPC) algorithms using a hysteresis switch between insulin and glucagon controllers with a multiple-input single-output (MISO) formulation. Although the closed-loop performance of these two control strategies is similar, the hysteresis switch is preferable due to (i) its greater flexibility in control design and tuning and (ii) a more straightforward way to avoid simultaneous injections of insulin and glucagon.

The paper presents a microscopic highway simulation model, built as an environment for the development of different machine learning based autonomous vehicle controllers. The environment is based on the popular OpenAI Gym framework, hence it can be easily integrated into multiple projects. The traffic flow is operated by classic microscopic models, while the agent’s vehicle uses a rigid kinematic single-track model, with either continuous or discrete action spaces. The environment also provides a simple high-level sensor model, where the state of the agent and its surroundings are part of the observation. To aid the learning process, multiple reward functions are also provided.

The paper presents a reinforcement learning based solution for the control design problem of a gearbox actuator. The system is operated by an electro-pneumatic, three-state, floating piston cylinder. Besides the primary goals of positioning the piston, the nonlinear system’s quality objectives are to minimize switching time and overshoot. The control strategy based on the measurable parameters of the system is realized by a dense feedforward neural network. With the utilization of the policy based reinforcement learning architecture, the learning agent develops the optimal strategy for fast and smooth switching, under different and changing conditions.

Remaining useful life (RUL) prediction is a key part of battery health management to ensure vehicle safety and economy. This paper makes a comparative study of four representative RUL prediction methods, including fitting-based method, particle filter (PF) based-method, Box-cox transformation (BCT)-based method and support vector machine (SVM)-based method. A battery degradation experiment is performed to support the comparative study. After the comparative study of on-line battery RUL prediction, BCT shows the best comprehensive performance, while the other three methods have their own application characteristics respectively.

Due to the security threats faced in the connected world, the consideration of security requirements during system design and modeling has become a necessity. Unfortunately, the identification of new requirements that may arise throughout additional phases of a system’s life-cycle (e.g. operation) must also be considered due to the ever-changing threat landscape. These new requirements may derive in system adaptations or modifications that ensure continuous system security. The identification of these new requirements and the implementation of their derived changes must be performed in a timely manner in order to avoid time windows where the system is vulnerable to security attacks. Unfortunately, the timely implementation of security-related changes is a challenge when dealing with automation systems as it may affect their availability and functionality. This position paper presents an approach that allows semiautomatic identification of system vulnerabilities in order to facilitate the derivation of new requirements that allow to ensure the security of a system. This identification is carried out throughout multiple phases of a system’s life-cycle.

Power flows exceeding the thermal capacity on power system lines and severe rise on bus voltages are a potential problem in the future distribution grids where development of photovoltaic (PV) installations is expected. This problem could cause damage on components such as transformers and distribution lines provoking triggering the system protections that would eventually lead to system failures. The economic and environmental implications related to reinforcing of the existing network, make this option not the immediate solution for implementation. Installation of battery energy storage systems (BESS) and control of active power curtailment (APC) would be another option to phase the upcoming problems. In this paper a forecast for the year 2035 in a real residential area in Switzerland was investigated, where large amount of PV panels are expected to be installed. This project investigates different solutions based on BESS and APC to deal with the inevitable increase of power flow in the network. The solutions presented here, prevent the overload in a distribution system transformer and avoid excess power flows on the lines. For a given set of demand and PV production curves, simulations in Matlab were performed and computations of optimal power flows were calculated. The solutions explored on this work consider economical aspects such as discounted cash flow method that finds the optimal size of BESS installed. Results also show that combining BESS with curtailment is the most viable solution.

The paper deals with the fault-tolerant reconfigurable control design of a four-wheel independently-actuated (4WIA) electric vehicle. The purpose of the proposed method is to ensure the velocity and path tracking of the 4WIA vehicle in the event of a fault or performance degradation in one or even several in-wheel electric motors. The novelty of the presented method is the high-level control reallocation method based on the Linear Parameter Varying (LPV) control framework, by which the torque vectoring and steering of the vehicle are modified in order to deal with the impact of faulty in-wheel motors. The operation of the designed fault-tolerant method is validated in CarSim simulation environment.

In this paper, we propose a fractional order adaptive backstepping control solution to the saturation problem of incommensurate fractional order system with uncertainty and non-linearity. A model transformation is introduced to make the system appropriate to backstepping scheme, and the virtual signals are generated to compensate saturation via the construction of fractional order auxiliary system. By indirect Lyapunov method, the backstepping procedure is applied to fractional order systems with input saturation for the first time, which achieves the goals of stabilization and tracking. Finally, the effectiveness of the proposed fractional order backstepping is demonstrated in several examples.

For constructing a span-lateral inhibition neural network (S-LINN) with optimal architecture and parameters for actual application, an improved self-organizing optimization approach is proposed in this paper to tune the architecture and parameters simultaneously. This self-organization pruning algorithm can prune insignificant hidden neurons automatically through building a modified significance index function to evaluate the significance of hidden neurons. A preprocessing training of the initial network with assumed redundant hidden neurons will be allowed in the tuning process. A subsequent learning after the self-organization pruning process is also implemented to optimize the parameters of pruned network. The proposed self-organizing approach has been tested on nonlinear dynamic system identification benchmark problem. Simulation results demonstrate that the proposed method has good exploration and exploitation capabilities in terms of searching the optimal structure and parameters for S-LINN.

Multi-Aircraft Conflict Resolution (MACR) is a Multi-Criteria Decision-Making (MCDM) problem, which involves multiple stakeholders (airline, air traffic controller, and aircraft) with competing and incommensurable objectives. This paper proposes a two-step MCDM scheme to the solution of MACR. In the first step, a second order cone program is adopted to generate a set of candidate resolution strategies with different minimum separations between trajectories. Each candidate strategy is then evaluated via three criteria modeling the interests of the stakeholders. In the second step, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approach is used to determine the best strategy that realizes an adequate tradeoff among the competing interests while coping with their incommensurability. Some numerical results are presented to show the efficacy of the proposed scheme. Interestingly, the minimum separations associated with the best resolution strategies according to either the interest of the airline or that of the aircraft both differ from the one adopted in the current air traffic control operation.

The optimal control of a Dubins car endowed with a circular or a line segment capture set is considered and the connection to their Homicidal Chauffeur differential game (HCDG) analogues is discussed. The capture set endowed Dubins cars are tasked to reach a stationary target point in minimum time. The solutions to the Dubins car optimal control problems (DCOCP) are similar to the solutions of the HCDG where the Evader, which is slower than the pursuing car, has simple motion. The similarities can be attributed to the fact that both the DCOCPs and the HCDG dynamics have two states and the Pursuer in the HCDG is modeled as a Dubins car. Because of these similarities, the solutions to the DCOCPs yield insights into the solutions of the attendant HCDGs where the Pursuer is endowed with a circular, or a line segment, capture set. This paper develops the solution to the optimal control problem of a Dubins car with a line segment capture set, presents the solution when the car is endowed with a circular capture set, and then draws comparisons to the respective HCDGs.

This paper deals with the problem of string stability of interconnected systems with double-integrator open loop dynamics (e.g. acceleration-controlled vehicles). We analyze an asymmetric bidirectional linear controller, where each vehicle is coupled solely to its immediate predecessor and to its immediate follower with different gains in these two directions. We show that in this setting, unlike with unidirectional or symmetric bidirectional controllers, string stability can be recovered when disturbances act only on a small (TV-independent) set of leading vehicles. This improves existing results from the literature with this assumption. We also indicate that string stability with respect to arbitrarily distributed disturbances cannot be achieved with this controller.

Calcium ions (Ca2+) are versatile, yet critical components of molecular communication networks. Cells have developed complex mechanisms to maintain homeostasis of Ca2+concentration levels within cytoplasm. Both at the cellular and tissue levels, information is transferred through complex dynamical patterns of Ca2+signals. Since Ca2+is involved in the regulation of various biological processes, understanding its dynamics is a valuable source of information with potential applications in systems and synthetic biology. However, modeling such complicated dynamics is a challenge in systems biology. In this work, we present a data driven analysis of Ca2+dynamics based on Dynamic Mode Decomposition (DMD) to study the dynamics of Ca2+signaling in Drosophila melanogasterwing disc in late larval stages. This system is amenable to studying Ca2+signaling in a developing epithelium. Experimentally, it is observed that Ca2+dynamics show different dynamical patterns depending on genetic perturbations. Therefore, from a systems biology perspective, devising a reasonable model to capture various dynamical patterns can shed light into interplay between dynamics and various biological processes. The dynamics of Ca2+is analyzed using a first-order state space model approximated by DMD algorithm. DMD modes and eigenvalues of the derived model was investigated to identify coherent spatial and temporal patterns that reflect the known morphogenetic boundaries of the organ. This implies that Ca2+signaling dynamics encode the differentiation state of cells in the system.

In this study a free convective three-dimensional flow of an incompressible Jeffery’s fluid through a highly porous medium bounded by an infinite vertical porous plate subjected to a constant suction is modeled and analyzed theoretically. The permeability of the medium is assumed to be periodic while free stream velocity is assumed to be uniform. The assumption of either constant or time dependent permeability of a porous medium leads to two-dimensional flows, however, the flow becomes three-dimensional due to variable permeability of the porous medium. Cattaneo-Christov approach is used for the transfer of heat in fluid flow. Analytic solutions for velocity field, pressure, skin friction components and temperature distribution are obtained. The effects of physical parameters emerging in the mathematical model of the physical phenomenon on these physical quantities are discussed and visualized graphically. It is noted that main flow velocity component decreases due to enhancement in non-Newtonian parameter, however, pressure rises due to thickening of the fluid.

This publication presents the ongoing developments of a tightly-coupled navigation filter for maritime applications. The filter is based on quantities of an Inertial Measurement Unit (IMU) and is aided by signals of a Global Navigation Satellite System (GNSS). Furthermore, to improve robustness, the filter is aided by data of a Doppler Velocity Log (DVL), which determines vessel velocity in three dimensions relative to ground. This publication introduces the filter algorithms and the necessary extensions to include DVL information. Therefore, the received data are synchronized, the measurement noise is parameterized for use in the applied Extended Kalman Filter (EKF) and the improved filter-convergence behavior at system initialization is investigated and proofed. The new filter approach is evaluated and compared within extensive real-world measurement campaigns, which are carried out in the Harbor of Rostock, Germany. Results show an improved convergence behavior, a more robust localization in case of poor satellite reception due to a more accurate velocity correction, and slightly improved position estimation accuracy, depended on the GNSS observables accuracy within the navigation filter.

In order to enhance the ‘sustainability’ of offshore wind farms, thus skipping unplanned maintenance operations and costs, that can be important for offshore systems, the earlier management of faults represents the key point. Therefore, this work studies the development of an adaptive sustainable control scheme with application to a wind farm benchmark consisting of nine wind turbine systems. They are described via their nonlinear models, as well as the wind and wake effects among the wind turbines of the wind park. The fault tolerant control strategy uses the recursive estimation of the faults provided by nonlinear estimators designed via a nonlinear differential algebraic tool. This aspect of the study, together with the more straightforward solution based on a data-driven scheme, is the key issue when on-line applications are proposed for a viable implementation of the proposed solutions.

Properties of the value function are examined in an infinite horizon optimal control problem with an unlimited integrand index appearing in the quality functional with a discount factor. Optimal control problems of such type describe solutions in models of economic growth. Necessary and sufficient conditions are derived to ensure that the value function satisfies the infinitesimal stability properties. It is proved that value function coincides with the minimax solution of the Hamilton Jacobi equation. Description of the growth asymptotic behavior for the value function is provided for the logarithmic, power and exponential quality functionals. An example is given to illustrate construction of the value function in economic growth models.

In this presentation we evaluate nonlinear control systems on a finite time interval. We also discuss application of the invariance features and weak invariant sets in the procedures of constructing solutions for approach problems. These problems are closely connected with evaluation of reachability sets and integral funnels of control systems. We revise several problems of approaching and one general scheme of constructing solutions for these problems. Scheme consist of two stages: at the first stage we construct approximate reachability set of the approach problem; at the second stage in one way or another we construct control on a given time interval that solves the approach problem.

A number of applications involving wave propagation are asymptotically stable, but the decay rate of the mode does not increase with increasing wave number. Applications include electrical transmission, acoustic waves and water waves. There are many different approximation methods for these systems that are fine for simulation. But the qualitative behaviour of the approximated eigenvalues can be quite different. Controller and estimator design with different approximations can yield different results. A number of partial differential equations (PDEs) are derived using Hamilton’s principle. This provides information about the energy of the system that can be a guideline in establishing well-posedness on an appropriate state space. Recent work on a port-Hamiltonian approach to approximation shows promise in obtaining finite-dimensional approximations that are useful for control and estimation. A key feature of the port-Hamiltonian approach is careful attention to how the energy in the system is modelled and approximated. How does this approach compare to more traditional approaches to scientific computation? In particular, what are the similarities and what are the differences? These questions will be examined in the talk, and in discussion, through several relatively simple examples.

In case of a flexible or underactuated system beside the controlled dynamics uncontrolled motion appear. This uncontrolled dynamics is called as the internal dynamics. Before the control design and the selection of the gain parameters the stability of the internal dynamics have to be ensured. The investigation of the internal dynamics requires to assign state variable for the internal dynamics which could be quite difficult in case of a complex system. In order to overcome this problem in this study a new simple approach will be proposed.

The Bernoulli filter (BF) in the interacting multiple model (IMM) framework is proposed for detecting and tracking a maneuvering target. The BF is implemented as a particle filter and embedded in the IMM structure. The communication between the IMM and the BF is achieved through a Gaussian layer. Particles are drawn from the mixture densities at each recursion and the model likelihoods are computed from the filter innovations. Simulations show that the proposed filter outperforms the single model variant and it can effectively choose the correct motion model and estimate the state of the tracked object.

The multiple task-priority inverse kinematics framework using the Moore-Penrose pseudoinverse has been shown to be asymptotically stable for the regulation problem with certain conditions on the tasks. In this article we present a theorem that extends this to the tracking problem by including an additional criterion that we term fully represented in the null-space. We show the effect of this on a simulation with a snake-like robot manipulator with 30 links for 3 compatible tasks, and an example of 2 tasks that are compatible as a regulation problem but incompatible as a tracking problem. As the tracking problem is more affected by the linearization assumption, we also include an example showing that the effect of linearization can be detrimental during tracking.

Wind turbine rotor imbalances may be created by a multitude of causes, e.g. pitch misalignment, blade erosion. In such situations, machines experience undesired vibrations, which cannot be compensated or mitigated by the controllers generally adopted by industry today. This happens because the control action may be collective or both collective and cyclic, but, in order to cope with imbalances, it is necessary to employ a fully individual control which allows a blade to move independently from the others.

The problem of the change point detection in a sequence of random variables is considered. The task arises in control of technological processes, particularly, in oil and gas production management. Some equipment parameters are to be controlled in order to detect a change of the equipment characteristics and, consequently, a breakdown of its technological regime. As a rule, the data observed are stochastic with the unknown distribution.

We develop a system for generating embedded diagnostics from an ODE model that can isolate faults given the memory and processing limitations of the embedded processor. This system trades off diagnosis isolation accuracy for inference time and/or memory in a principled manner. We use a Polynomial Regression approach for tuning the performance of an ensemble of low-fidelity ODE diagnosis models such that we achieve the target of embedded processing limits. We demonstrate our approach on a non-linear tank benchmark system.

Hammerstein systems are composed by a static nonlinearity followed by a linear dynamic system. The proposed method for identifying Hammerstein systems consists of a formulation within the Least Squares Support Vector Machines (LS-SVM) framework where the Impulse Response of the system is incorporated as a constraint. A fundamental aspect of this work is that the structure of the Hammerstein system allows to obtain an impulse response that approximates the linear block while LS-SVM models the nonlinearity. When the resulting model is trained, the regularization capabilities of LS-SVM are applied to the whole model. One of the main advantages of this method comes from the fact that while it incorporates information about the structure of the system, the solution of the model still follows from a simple linear system of equations. The performance of the proposed methodology is shown through two simulation examples and for different hyper-parameter tuning techniques.

A two relay controller is robustified by a switched integral sliding mode controller in order to generate robust self-oscillations for pendulum systems. The proposed strategy assures theoretically exact robustification of the self-oscillations preserving the amplitude and frequency of the oscillations even in the presence of matched uncertainties/perturbations. The efficiency of the proposed robustifying approach is illustrated on an example of a Furuta pendulum.

A water distribution network (WDN) is a dynamic system in which the demand is a nonstationary process. On the other hand, most of the successful data-driven fault detection and isolation (FDI) methods have been developed by assuming static models and stationary processes. A demand model for the WDN is formed by a periodic signal plus a stochastic variable. This model allows the transformation of data such that a stationary and extended space of data can be obtained as it is demonstrated here. This proposition is the principal contribution of this work. To illustrate the advantages of the proposal, three well-known data-driven FDI algorithms are applied for the leak detection of the Hanoi distribution network: principal component analysis (PCA), independent component analysis (ICA), and support vector data description (SVDD). The leaks were emulated with different outflow magnitudes in all nodes. As a performance index, the fault detection rate is used, and the results indicate an improvement in the general index of approximately 75% when data are periodically transformed according to the proposal.

The problem of curse-of-dimensionality in finite state and action Markov decision processes is considered using iterative clustering of closed-loop data and repeated discretization of the state space process model. The performance of the control design approach is demonstrated using a multi-input van der Vusse continuous stirred tank reactor control benchmark. It is demonstrated that the finite state description provides a simple implementation of Bayesian state estimation using cell filters, and dynamic programming gives means to conduct optimization of closed-loop performance of a nonlinear stochastic multidimensional chemical plant.

The objective of this contribution is to improve recent stability results for a system coupling ordinary differential equations to a vectorial transport partial differential equation by proposing a new structure of Lyapunov functional. Following the same process of most of the investigations in literature, that are based on an a priori selection of Lyapunov functionals and use the usual integral inequalities (Jensen, Wirtinger, Bessel...), we will present an efficient method to estimate the exponential decay rate of this coupled system leading to a tractable test expressed in terms of linear matrix inequalities. These LMI conditions stem from the new design of a candidate Lyapunov functional, but also the inherent properties of the Legendre polynomials, that are used to build a projection of the infinite dimensional part of the state of the system. Based on these polynomials and using the appropriate Bessel-Legendre inequality, we will prove an exponential stability result and in the end, we will show the efficiency of our approach on academic example.

Multi-sensor object detection and tracking on a highway scene with radar measurements is presented. The estimation algorithm is the random finite set based Bernoulli filter, working in the Bayesian framework. The recursion for calculating the Bayes estimation is implemented as a particle filter. A method is presented for calculating the likelihoods, suitable for particle filtering performed with moving sensors, assuming additive Gaussian measurement noise. In our approach, for calculating the posterior estimate of the object state, the measurement likelihoods are computed in the state space, instead of the measurement space, by mapping each measurement to the global coordinate system. The map consists of a nonlinear and an affine part. While the affine transformation trivially preserves the Gaussian nature, the nonlinear is well-proven to be approximated as affine too. This approach allows the particles to be drawn directly from the state space, hence the evaluation of the measurement model is not needed, which saves computational power.

The supply/demand balance problem plays a pivotal role in the electricity grid, especially when an increasing proportion of power is generated from renewable resources. Enormous supply/demand models have been put forward in order to handle the balance problem in the smart grid, in the presence of high renewable penetration. This has brought an awareness that the flexibilities in the demand can be exploited to alleviate the burden on the supply. In view of this, we apply and study differentiated energy services, which distinguish demands in terms of their available flexibilities. As a starting point, we concentrate on two problems regarding adequacy. On the one hand, we find both numerical and analytical ways to check the adequacy of a supply. On the other hand, we characterize the adequacy gap in the case of an inadequate supply.