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Inertial stabilized platforms are extensively used to keep the light of sight free from the effects of vehicle motion or vibration. In order to reduce the assembly difficulty and cost as well as achieve an accurate pointing in the presence of disturbances, a finite-time output feedback control strategy is proposed. On the basis of the dynamic model with both matched and unmatched uncertainties, two high-order sliding mode observers are designed to estimate the newly defined virtual state and the lumped uncertainty, respectively. Combing with the nonsingular terminal sliding mode technique, an output feedback control based on the estimates and the output is developed. The proposed method can achieve not only stability and robustness against uncertainties but also the finite-time convergence to the desired angular rate without control chattering. Finally, the comparative simulations are performed to show the effectiveness of the proposed method.

In this paper a robotic boat setup for research and education in control is presented. It was designed for students to develop their practical experience and validate theoretical results at the course “Control Methods for Robotic Applications”, which is being taught at the Department of Control Systems and Informatics of ITMO University. Four use cases of this setup are suggested in the paper. They are aimed to design controllers of various structures.

Providing an efficient production plan that integrates the procurement and distribution plans into a unified framework is significant to achieving competitive advantage. In this paper, we consider a supply chain master planning model consisting of multiple suppliers, one manufacturer and multiple distribution centers. We propose a new multi-objective linear programming model in a wood and paper industry for integrating procurement, production and distribution planning, considering various objectives simultaneously as well as the imprecise nature of some critical parameters such as market demands, cost coefficients and capacity levels. Then, we use a fuzzy ranking method to solve this fuzzy multi-objective supply chain master planning model. Afterwards we compare the results of this approach with the results of the deterministic one.

In a series of papers by Annunziato and Borzì, Model Predictive Control of the Fokker-Planck equation has been established as a numerically feasible way for controlling stochastic processes via their probability density functions. Numerical simulations suggest that the resulting controller yields an asymptotically stable closed loop system for optimization horizons looking only one time step into the future. In this paper we provide a formal proof of this fact for the Fokker-Planck equation corresponding to the controlled Ornstein-Uhlenbeck process using an L2cost and control functions that are constant in space. The key step of the proof consists in the verification of an exponential controllability property with respect to the stage cost. Numerical simulations are provided to illustrate our results.

This paper is an attempt to present the methodologically correct approach of the anisotropy-based theory for discrete-time systems with multiplicative noise. A formulation of sufficient conditions of boundedness of the anisotropic norm is the main goal of this paper. As it is shown, this problem can be reduced to the convex optimization problem. A simple numerical example to illustrate the solution procedure is given.

This paper deals with the development and identification of a simple mass balance model for plant growth. Further to previous studies, this paper deals with modeling of growth of Arabidopsis thaliana, for which a detailed experimental design has been performed with the objective to address some of the limitations of the previously developed plant growth models, largely in terms of data availability, while accounting for the practical experimental constraints.

The analysis of time-varying delay systems has attracted many researchers over the last two decades. One of the major contribution within this field is the recent reciprocally convex combination lemma. The relevance of this lemma arises from the derivation of stability conditions using Jensen’s inequality. The main interest of this lemma is the reduction of the number of decision variables while keeping the same level of conservatism. In this paper, we provide an alternative vision of this inequality through a new proof issued from the recent development on integral inequalities. The benefit of this proof relies on the derivation of an exact expression of the conservatism. A discussion is finally proposed at the end of the paper to point out the possible extensions of this approach.

The costs involved in building and launching standard big satellites are very high. Due to the miniaturization of electronic and mechanical devices, smaller and cheaper satellites, also known as microsatellites, are becoming more capable, and thus, more important in space exploration. In other words, microsatellites can unite simplicity with capacity to carry more sophisticated payloads. This paper details practical aspects of microsatellites ranging from integrating process, vibration tests and vacuum chamber tests to its operation.

Modern globalization leads companies into a changing environment with a highly uncertain future development of key drivers of change. Especially, global production networks are affected by uncertainty and dynamic changes. Reactiveness becomes of crucial importance, as the adaptation to environmental conditions is the key to maintain competitive advantages. This article presents an approach for flexible migration planning in global production networks. The focus is on the formulation of a Markovian Decision Process (MDP) that enables the identification of optimal reactions to stochastic changes of key drivers of change. The formulation includes the description of a multi-level modelling approach for global production networks. Furthermore the valuation model of the reward function of the MDP is discussed in detail. Finally, the paper provides a brief description of exemplary optimization results solving the MDP by backward induction.

In this paper a series of practical tasks on control for manipulators is presented. This series represents a laboratory part of the course "Control Methods for Robotic Applications" included in the curriculum of the master’s degree program at the Department of Control Systems and Informatics of ITMO University. The exercises are aimed to attract and motivate students, improve their practical competency and simplify their understanding of theoretical background. The tasks are focused on such problems as path and trajectory planning, automatic code generation and design of a tracking system for moving target.

This paper concerns the global cooperative output regulation problem for a class of nonlinear multi-agent systems with unknown control directions and unknown exosystem. The communication among agents makes it difficult to implement the Nussbaum gain technique to handle the non-identical unknown control directions. Moreover, the unknown parameter in the exosystem, which cannot be easily handled by the estimators in existing results, makes our problem more intricate. To overcome these challenges, we employ a novel internal model candidate to develop a two-layer control scheme for an adaptive distributed controller design. Theoretic stability analysis is presented and a numerical example is given to show the effectiveness of the proposed controller.

Maritime Unmanned Vehicle (MUV) as Unmanned Surface Vehicle is introduced as a novel traffic device for continuous maritime traffic information collection and complementing existing methods for Electronic Patrol System (EPS). MUV cruise path planning is studied regarding maximum sailing time, which is aiming at completing MUVs cruise task on a certain number of the areas with relatively high risk needed surveillance. With detailed description of MUV cruise path planning problem, it is Abstracted as travel salesman problem solved by ant colony algorithm in this paper. The approach used to select cruise objects is proposed, especially the cloud model applied to choose cruise navigation segments. The factors including total cruise distance and standard deviation of cruise time, and K-means clustering method are used to evaluate and handle the scenario with MUV navigation constraint. On the basis of above operations, the special scenario is finally converted into several ones without constraint. At last, the cases of Fuzhou Maritime Safety Administration areas are demonstrated. The results indicate that the method proposed for MUV cruise path planning is feasible and effective.

This paper deals with the analysis of the non isothermal tubular reactor model. It is shown in particular and in line with similar studies on biochemical reactor models that multiple equilibrium profiles can be exhibited if the diffusion coefficients are large enough. The consequence on the stability of these equilibrium profiles is that these also exhibit the alternance of stable and unstable equilibrium profiles.

The problem of community detection(or clustering)in graphs plays an important role in analysis of complex large-scale networks and big data structures, arising in natural, behavioral and engineering sciences. Examples of such networks include, but are not limited to, World Wide Web (WWW) and Internet, social networks, ecological networks and food webs, cellular and molecular ensembles. A community (or a module) in a graph is a subset of its nodes, whose members are "densely" connected to each other yet have relatively few connections with nodes outside this subset. A number of algorithms to subdivide the nodes of large-scale graphs into communities have recently been proposed; many of them hunt for the graph’s partitions of maximal modularity.One of the most efficient graph clustering algorithms of this type is the Multi-Level Aggregation (or "Louvain") method. In this paper, a randomized counterpart of this algorithm is proposed, which provides a comparable "quality" of graph’s clustering, being however much faster on huge graphs. We demonstrate the efficiency of our algorithm, comparing its performance on several "benchmark" large-scale graphs with existing methods.

The anisotropic norm of a linear discrete time invariant system is a measure of system output sensitivity to stationary Gaussian input disturbances with mean anisotropy bounded by some nonnegative parameter. The mean anisotropy characterizes the predictability (or coloredness) degree of stochastic signal. The anisotropic norm of a system is an induced norm, limiting cases of which are H2- and H∞-norms as a→ 0 and a→ ∞, respectively. In Vladimirov et al. (1996) a method for numerical computation of the anisotropic norm was proposed. This method involves cross-coupled Riccati and Lyapunov equations and associated special type equation. Another method for anisotropic norm computation via LMI-based approach was proposed in Tchaikovsky & Kurdyukov (2006). This paper develops a new method for getting the lower bound for anisotropic norm by linear algebra standard methods.

In controlling uncertain processes, it is decisive to utilize information provided by measurements in order to estimate parameters and states. Nonlinear Model Predictive Control (NMPC) is a popular method to implement feedback control and deal with uncertainties. Conventional NMPC or nominal control, however, sometimes does not provide enough information for system estimation, leading to unsatisfactory performance. Dual control attempts to strike a balance between the two goals of enhancing system estimation and optimizing the nominal objective function. In this paper, we analyze the performance of these strategies through the interplay between the performance control task and the information gain task in connection with Optimal Experimental Design. Examples illustrate the conflict and agreement between the two tasks and explain why in some cases nominal control performs well. It is also observed that measurement noise provides excitation helping to improve the quality of estimates.

The paper analyzes the relationship between the traffic flow and the look-ahead control. The purpose of the analysis is to map the impact of the unique speed profiles used by the look-ahead controlled vehicles on the traffic flow consisting of conventional vehicles. The analysis is carried out by considering different traffic volumes, penetration rates of the look-ahead vehicles and the energy-scaling parameter of the look-ahead optimization algorithm. The analysis is evaluated both in the Matlab simulation environment and in the microscopic traffic simulation environment VISSIM using COM interface.

In AC installations the Arc Fault Detection Devices are well established. However emerging high power (high current) DC application fields represent several challenges wit respect to arc faults and their detection. This paper presents arc fault signals in time domain and in frequency domain, discusses challenges associated with arc fault detection in the DC application domain and application of the UL1699B standard testing procedure for arc fault detection development in high current applications.

This paper applies the recently developed framework for integral control on nonlinear spaces to two non-standard cases. First, we show that perfect target stabilization in presence of actuation bias holds also if this bias is state dependent. This might not be surprising, but in practice it provides an easy way to robustly cancel nonlinear dynamics of the uncontrolled plant. We specifically illustrate this for robust stabilization of a pendulum at arbitrary angle. Second, as previous work has been restricted to systems with as many control inputs as configuration dimensions, we here provide results for integral control of a non-holonomic system. More precisely, we design robust steering control of a rigid body under velocity bias.

This paper has proposed the time-series based maximal overlap discrete wavelet transform (MODWT) technique for detection and localization of different types of power quality (PQ) disturbances. Ten types of different PQ events of the voltage signal such as sag, swell, interruption, harmonic, spike, notch etc. are analyzed with the aforementioned wavelet transform (WT). Each of the signal is decomposed up to fourth level with the MODWT. The co-efficients of MODWT decomposition are further used for feature extraction which are the input to the classifiers like Support Vector Machine (SVM) and Decision Tree(DT). For the detection of the disturbances, the signals are decomposed up to four finer levels whereas for the classification, decomposition is carried out up to seventh finer levels.

The problem of grabbing a non-tumbling satellite, addressed in this paper, is split into two subproblems, the first of determining a minimum distance collision-free path from the starting stowed pose of the space manipulator to the grabbing pose, and the second of determining a minimum time motion along this path, subject to speed and torque constraints of the manipulator. The first problem is solved using the well known A∗algorithm, after posing path planning as a graph search problem. Different types of heuristic functions are used for the graph search, and their influence in finding the solution is discussed. The non-smooth path generated using A∗algorithm is smoothened, and then minimum time motion subject to constraints is determined by posing it as a nonlinear programming problem and solved using an SQP algorithm. Solutions obtained for three different target satellite poses are discussed.

Any practical control system is affected by external signals. Some of them are “informative” (e.g. reference signals), and the others (disturbances, measurement noises etc.) are undesired as they can visibly deteriorate the system behavior. General approaches to disturbance rejection and reference tracking have been elaborated in the framework of geometric control theory, among them are disturbance decoupling control and internal model principles. These approaches, however, assume the state and control of the system to be unconstrained. To take such constraints into account, one usually has to consider optimization problems where the performance index penalizes in some way the system process. Optimization problems in presence of uncertain signals have been addressed in the context of robust control and stochastic control. Standard methods like usually provide only suboptimality of the process; the optimal value can be found for either “worst-case” signal (like in minimax H8-and L1-optimization approaches) or “on average”, assuming the external signals to be stochastic with known spectral density. In a series of his papers published in 1992-2012, Vladimir A. Yakubovich promoted the approach of universal controllers for linear-quadratic regulation (LQR) problems under uncertain signals. The term “universal” emphasizes that the controller renders the solution of the closed-loop system optimal for any external signal. Although this is not possible for arbitrary signals, in some special classes of signals the universal controllers not only exist but may even be chosen linear. The existence of linear optimal controllers has been proved for two important classes of uncertain signals, that is, polyharmonic signals with known spectrum and signals with fast decreasing spectral density. In this paper we extend the results of V.A. Yakubovich to more general classes of systems and quadratic performance indices, arising in problems of optimal oscillation damping, reference tracking and model matching.

This paper studies the properties of closed reacting systems described by mass-action kinetics. Following recent developments in potential-driven kinetic representations and stability analysis of (open) reacting systems, this paper seeks to develop a framework where both thermodynamically-consistent reaction fluxes and stoichiometry play a role in the identification of invariants and on stability analysis. Following some observations from the literature, the proposed approach seek to re-express mass-action kinetics in terms a potential-driven dynamical system. The proposed approach consists in the homotopy decomposition of the mass-action kinetic to compute such a potential-driven representation. An example is considered to illustrate the proposed approach.

This paper studies geometric properties of a class of irreversible dynamical systems, referred to in the literature as metriplectic systems. This class of systems, related to generalized (or dissipative) Hamiltonian systems, are generated by a conserved component and a dissipative component and appear, for example, in non-equilibrium thermodynamics. In non-equilibrium thermodynamics, the two potentials generating the dynamics are interpreted as generalized energy and generalized entropy, respectively. Stability and stabilization results for metriplectic systems have been presented in the literature, however, some aspects are still poorly understood, in particular the existence of dynamical invariants such as periodic orbits. In this note, we study the properties of metriplectic systems by considering a lift from the n-dimensional state space to a (2n+1)-dimensional contact space, following an approach introduced in recent years to study irreversible control systems. This lift leads to a deeper geometric characterization of metriplectic systems in the extended space. An example is provided to illustrate the approach proposed in this paper.

A robust tuning problem for a two-degree-of-freedom model predictive controller is explored for single-input, single-output uncertain paper-making processes. The objective is to achieve satisfactory closed-loop responses, as measured by overshoots, settling times and output oscillations with user-specified parametric uncertainties. As the output variation cannot be easily specified by the end users, two methods are proposed to connect a total variation specification to user-friendly indices, based on which two algorithms are designed to solve the tuning problem. An application to a process extracted from the pulp and paper industry is employed to verify the effectiveness of the proposed algorithms.

Sustainability is a popular topic in many research fields, but sustainability of mining in Finland is even more so after the economic, environmental and social damage caused by case Talvivaara. Here a measure for sustainability of exploration phase of mining is introduced alongside with the mathematics of the method. The method described is called Data Envelopment Analysis (DEA), which is a versatile tool used in many instances to measure eco-efficiency. Here the main emphasis is on combining economic, ecological and social variables with DEA to produce one index for every exploration site studied. This index, between zero (0) and one (1) is called sustainability score of the given exploration site. The paper uses artificial data to introduce the idea of sustainability measurement. However, as shown in the paper, the artificial data is close to the data already collected on the exploration sites. The study at hand is able to discern a useful index called sustainability score, taking into account six variables affecting the sustainability of exploration phase of mining. It should be noted that if more explorations sites are included into evaluation, the utility is two-fold: first, the sustainability scores become more versatile, and second, there can be more variables to be included into the analysis, adding to the detail at which different factors of sustainability can be taken into account.

The paper focuses on the robust control design of a look-ahead cruise control system. The aim of the look-ahead control is to consider the topographic conditions and speed regulations of the oncoming road sections. The look-ahead cruise control leads to an optimization method, which generates a fuel and time optimal reference speed for the vehicle. It requires external topographic and road information, and measurements concerning the vehicle, such as speed and acceleration. Since the signals of the vehicle are considered in the generation of the reference speed, the generator has an impact on the stability of the cruise control system. In the paper a robust H∞cruise control design method is proposed, which considers the efect of the reference speed generation, and guarantees the stability and the speed tracking performance of the entire system. The efciency of the method is analyzed through a simulation example with real highway data.

Model predictive control is a modem method which was used in many different applications. One of the applications are electrical machines. Designing a controller for a synchronous drive has shown to be a challenging task. Many papers substitute one or more controllers in the cascade structure. Nevertheless, this approach calculates only the suboptimal trajectory, not the optimal trajectory. This paper presents a linearization approach of nonlinear model predictive controller and its preparation for implementation. Simulation is performed to verify presented ideas and its results are shown and discussed at the end of the paper.

Deriving certification bounds for optimization algorithms is an active research area in the control community. This is mainly impulsed by the use of on-line optimization algorithms in real-time MPC through limited computation power. However, the way such bounds are then used to derive a convergence certification for MPC frameworks is still not suficiently mature. This paper contributes in clarifying what are the unavoidable additional ingredients that need to be combined with any algorithm's certification bound in order to derive a relevant certification result for the MPC-based closed-loop performance. Moreover, the paper gives such a general certification result based on these ingredients for any pair of certified algorithm and provably stable ideal MPC formulation. The proposed framework is then instantiated to the particular case of linear MPC and a simple example is given to illustrate the introduced concepts.

Software enables impressive cyber-physical system applications, but also dominates the costs and perils of designing such systems. Is there a science of software and system design? This paper reflects on this question, and identifies opportunities for interactions with the fields of artificial intelligence and machine learning.

In this paper, weighted stochastic gradient (WSG) algorithms for ARX models are proposed by modifying the standard stochastic gradient identification algorithms. In the proposed algorithms, the correction term is a weighted term of the correction terms of the standard SG algorithm in the current and last recursive steps. In addition, a latest estimation based WSG (LE-WSG) algorithm is also established. The convergence performance of the proposed LE-WSG algorithm is then analyzed. It is shown by a numerical example that both the WSG and LE-WSG algorithms can possess faster convergence speed and higher convergence precision compared with the standard SG algorithms if the weighting factor is appropriately chosen.

Emotionally expressive eye movement is important for facial animation. However, relation between eye movement and emotions has not been well studied. This paper integrates various sources of information to create an expressive eye model capable of expressing different emotions through vivid eye movements. First, based on statistical data and experimental studies, we endow eye model with detailed motion parameters and functions, with which detailed eye movement under neutral emotion state can be displayed. Second, an eye tracking experiment is performed to find the impact of emotional change on eye movements, and then the analysis result of tracking data are used to model eye movements under different emotion conditions. Experiment demonstrates that our eye model can reflect emotional changes of virtual character through different eye movement patterns.

Adaptive Posicast Controller that is robust to delay-mismatch is introduced in this paper. Inspired from a recent result on guaranteed delay margins in adaptive control, the original adaptive laws of the above mentioned controller are modified using projection to compensate the uncertainty in the input delay. It is conjectured and shown in simulations that even though the assumed upper bound for the delay value is exceeded, Adaptive Posicast Controller with projection algorithm keeps all the system signals bounded.

This paper proposes a novel controller for trajectory tracking of nonholonomic vehicles of unicycle type. The vehicle is subject to velocity constraints consisting of angular velocity saturation and bounded linear velocity with a positive-minimum value. It is shown that the trajectory tracking problem can be solved with the proposed velocity controller. Meanwhile, the velocity constraints can be satisfied by tuning of the design parameters. Finally, simulation results of several examples illustrate the effectiveness of the proposed controller.

A generalized approach to design a proportional and derivative system faults fuzzy observer for continuous-time nonlinear systems described in the Takagi-Sugeno descriptor statespace form is presented in the paper. Considering measurable premise variables, the problem addressed is formulated as an enhanced concept to descriptor systems design, implying the asymptotic convergence both the state observer error as fault estimate error. Presented in the sense of the second Lyapunov method, an associated structure of linear matrix inequalities is outlined to possess the global observer asymptotic dynamic properties with compiling the local insiders by inference through the membership functions equal those of the system model. The proposed design conditions are verified by simulations in the numerical illustrative example.

Generalizing numerous results on absolute stability of nonlinear Lurie systems, Vladimir A. Yakubovich established a fundamental abstract criterion of absolute stability under uncertain nonlinearities. His Quadratic Criterion offers elegant conditions for the uniform output L2-stability in a wide class of uncertain nonlinearities, obeying anytime ("local") or integral quadratic constraints; these conditions are based on either quadratic Lyapunov functions whose existence is proved via the KYP lemma, or the method of integral quadratic constraints dating back to the works by V.M. Popov. In the present paper, we extend the Yakubovich quadratic criterion, replacing the output L2-stability by boundedness of some quadratic integral performance index, defined by a quadratic form F and referred to as F -stability. We demonstrate that our F -stability criteria enables one to obtain easily many stability criteria for the “critical” case, where the frequency-domain inequality is non-strict and usual L2-stability of the solution cannot be proved, e.g., we derive the circle and Popov's stability criteria in this degenerate case. All of these criteria are especially effective for scalar nonlinearities, nevertheless, we demonstrate that our approach is also applicable to a class of large scale nonlinear multi-agent networks, and derive the “networked” circle criterion for such networks as a consequence of our results.

The problem of assigning in part common left eigenvector assignment by the state feedback control of linear multi-input systems is considered in the paper. Based on linear matrix inequalities characterization, the exposition of the problem is formulated to handle a common left eigenvector for switched modes. Limited by preexisting degrees of freedom, which factually makes mutually contingent subsets of the closed-loop eigenvalue spectrum of each mode, the design conditions for in part common left eigenvector assignment of all modes are derived in the terms of linear matrix inequalities. The procedure provides a way available in linear switched control system design while the state-space control structures take standard forms. An example is included to assess the properties of the technique and its application.

A Galerkin-based family of numerical formulations is presented for solving nonlinear optimal control problems. This paper introduces a family of direct methods that calculate optimal trajectories by discretizing the system dynamics using Galerkin numerical techniques and approximate the cost function with Gaussian quadrature. In this numerical approach, the analysis is based on L2-norms. An important result in the theoretical foundation is that the feasibility and consistency theorems are proved for problems with piecewise continuous controls. Galerkin methods may be formulated in a number of ways that allow for eficiency and/or improved accuracy while solving a wide range of optimal control problems with a variety of state and control constraints. Numerical formulations using Legendre test functions are derived. One formulation allows for a weak enforcement of boundary conditions, which imposes end conditions only up to the accuracy of the numerical approximation itself. Finally, a numerical example is shown to demonstrate the exponential convergence of Galerkin optimal control.

In this paper two control problems for a surface robotic vessel are addressed. One is design of a dynamic positioning (DP) system. The other deals with advanced dynamic positioning (ADP) which is extended by an unknown disturbance rejection. Considered plant is represented as a MIMO system which comprised of three independent SISO channels. For the DP problem all SISO channels are controlled by virtual control inputs generated using a robust approach, so called, "consecutive compensator". Then after an inverse MIMO transformation real control signals are calculated for robotic actuators. For the APD problem we design the cancellation scheme for channels with input delays that may be caused by a command transmission via the radio channel or computational complexity of the image processing. To finalize the investigation proposed DP algorithm is applied to the robotic complex of vessel motion modeling.

The Kalman-Yakubovich-Popov (KYP) Lemma, giving the frequency-domain criteria for solvability of LQR problems and feasibility of related LMIs and Lur'e-Riccati equations, is undoubtedly one of the cornerstone results in modern control theory. Numerous applications of the KYP lemma in nonlinear and robust control motivated its extension to systems with distributed parameters. Unlike the finite-dimensional case, where the KYP lemma admits elegant proofs by means of algebraic techniques and methods from convex analysis, a prerequisite for its infinite-dimensional extensions is feasibility of the corresponding LQR problem, resulting on assumptions of exponential or L2-stabilizability. This property is restrictive and not easily verifiable for general infinite-dimensional systems. Being quite natural in the problems of absolute stability, it seems unnecessary in other applications of the KYP lemma which require only solvability of the operator inequality, but not positivity of its solution, such as e.g. instability or dichotomy criteria. In the present paper we show, that in the “strict” or coercive case the KYP lemma retains its validity, replacing stabilizability assumption with some technical assumption which holds, for instance, whenever the linear system is exponentially dichotomic.

Temporal logics are widely used to express (desired) system properties in controller synthesis and verification. In linear temporal logics, the semantics of the formulae are defined on the execution trajectories of the system. Recently, there have been a lot of interest in using dense-time linear temporal logic, such as Signal Temporal Logic (STL) in characterizing system trajectories. In this paper, we present a new method to derive an STL formula that characterizes the motion of a robot arm. Our work generalizes earlier work in this area by (i) allowing the use of polyhedral predicates, and (ii) incorporating a priori knowledge about the predicates. The formula is defined by a set of parameters, whose values are determined by minimizing a cost function that balances the trade-off between the formula's match with the trajectories and the the similarity between its predicate and an a priori known predicate. We apply our algorithm on experimental trajectories generated using a PHANToM Omni robot.

The integration of process design and control (IPDC) has become increasingly important and received considerable academic and practitioner attention over the recent decades. Moreover, some works in the field have utilized advanced controllers into IPDC framework. Considering the fact that the hierarchical control structure which consists of local PID controllers and an advanced process control (APC) as the central controller has been used widely in industrial practice, in this paper, the two-level hierarchical control structure is taken into account within the IPDC framework for the first time. The IPDC problem using hierarchical control structure is formulated as a bi-level optimization problem, which separates the design decisions from the APC decisions and then keeps the problem size manageable. And the APC decisions are incorporated in a superstructure-based dynamic optimization formulation which considers both cost and controllability. A continuous stirred tank reactor is used to test the proposed IPDC methodology.

The objective of this paper is to address the problem of state estimation in an Anaerobic Digestion Reactor (ADR) with unknown inputs - typically some influent concentrations, and to compare two different state and input estimation schemes. The first one is the classical Extended Kalman Filter (EKF) based on an augmented system that considers a slowly varying input (approach that has been already applied to this system and reported in the literature), whereas the second one is a recently proposed Unknown Input Observer (UIO), formulated in the spirit of a Kalman Filter, for continuous estimation with discrete measurements. The two filters are evaluated in simulation, demonstrating the superiority of the UIO.

This paper presents a project conducted at Bombardier Aerospace aimed at shortening span time and reducing duplicated effort during design. The current design processes were modeled at a high level of detail and analyzed for opportunities for improvement. After investigation of the critical paths and duplicated tasks, suggestions were proposed to add resources to critical tasks and to eliminate duplicated tasks by having only one department do them for all departments. Simulation of the suggested scenarios showed that span time could be reduced by more than 20% with almost no change in effort.

This paper deals with the problem of modelling a chemical reactor for the Direct Reduction of Iron ore (DRI). Such a process is being increasingly promoted as a more viable alternative to the classic Blast Furnace for the production of iron from raw minerals. Due to the inherent complexity of the process and the reactor itself, its effective monitoring and control requires advanced mathematical models containing distributed-parameter components. While classical approaches such as Finite Element or Finite Differences are still reasonable options, for accuracy and computational efficiency reasons, an algebraic approach is proposed. A full multi-physical, albeit one-dimensional model is addressed and its accuracy is analysed.

The paper deals with the problem of defining the optimal topology for a distributed fault detection architecture for non-linear large-scale systems. A stochastic modelbased framework for diagnosis is formulated. The system structural graph is decomposed into subsystems and each subsystem is monitored by one local diagnoser. It is shown that overlapping of subsystems allows to improve the detectability properties of the monitoring architecture. Based on this theoretical result, an optimal decomposition design method is proposed, able to define the minimum number of detection units needed to guarantee the detectability of certain faults while minimizing the communication costs subject to some computation cost constraints. An algorithmic procedure is presented to solve the proposed optimal decomposition problem. Preliminary simulation results show the potential of the proposed approach.

In this paper, a novel coordinated control method is proposed to handle the negative voltage impacts on weak sub-transmission systems, which are caused by wind power fluctuations. Firstly, wind variations are divided into large and small fluctuations. Then, according to the different control characteristics of on-load tap changers (OLTCs) and static compensators (STATCOMs), the OLTCs are used to minimize the impacts caused by large fluctuations, while the STATCOMs are used to eliminate the impacts introduced by small fluctuations. The model predictive control (MPC) and a consensus based distributed control techniques are adopted to design the control schedules of OLTCs and STATCOMs, respectively. It is shown that by the coordination of OLTCs and STATCOMs, voltage violations of points of interconnection (POIs) are eliminated, and voltage fluctuations at unregulated load buses are mitigated. The effectiveness of our proposed control scheme is presented through a case study on the modified IEEE 14-bus test system with a real wind power profile. According to the simulation results, it turns out that the proposed approach can reduce the operation number of OLTCs significantly, and decrease the required capacity of STATCOMs dramatically by comparison with the existing control scheme.

The paper proposes a method for the LPV-based (Linear Parameter-Varying) control design of the variable-geometry suspension system. The tilting actuation of the front wheel improves the lateral dynamics of the vehicle and assists for the driver in avoiding critical situations. The novelty of the method is the consideration of the nonlinearities in the tyre characteristics. The tyre forces are approximated by polynomials, which are used in the linearizing of the vehicle model. A parameter-varying vehicle model is derived, on which a robust LPV control is designed. The proposed method improves the performances of vehicle dynamics, and handles the critical maneuvers caused by e.g. a skidding car and high velocities. The proposed method is illustrated through CarSim simulation examples.

In Human-Machine (HM) systems, human operators and machines cooperate to achieve a system goal. Accordingly, the operators' task load should be dynamically adjusted in accordance with their physiological and psychological status, so-called operator functional state (OFS). In this sense, the HM system behavior becomes hybrid. Petri net and its extensions have been widely used in hybrid system modeling and control. In this paper, we use a fuzzy inference Petri net to model the HM system and then realize adaptive task allocation between human operator and machine. The model can predict the “performance” of the operator, based on which the workload can be reallocated (i.e., increased or reduced somehow) between the operator and machine in an adaptive fashion the tasks. The simulation results demonstrate that the approach of fuzzy inference Petri nets proposed is an effective way in modeling and control of HM hybrid systems.

In this paper we present a convenient technique for transferring small amounts of data to resource-constrained embedded devices. The concept is based on utilizing a mobile phone with a dedicated application to transfer data to one or more embedded devices by using solely the phone’s display and visible light as a medium. The presented solution requires very low computational power and its hardware cost is insignificant, thus can be utilized in very low power and resource-constrained devices. The described subsystem can be used to provide means of very convenient and relatively secure transfer of initial configuration for wireless sensor network motes, Internet of Things (IoT) entities, or smart home appliances.