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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.

In production, poor ergonomic environments not only lead to increased workloads and health hazards for employees but also tend to reduce efficiency and quality. Recently, the human-cyber-physical (HCP) system has been proposed and widely studies to meet human capabilities and limitations. However, most existing frameworks are still not adaptive enough to integrate humans into a smart production environment due to a lack of real-time individual human factors digitalization. This research proposes an HCP workstation model for comprehensive assembly resources digitalization and autonomous interaction by CPS enabling technologies. Based on this, an adaptive control system has been developed for proactive ergonomic risk mitigation. Computer vision is deployed for real-time individual ergonomic evaluation and a prototype has been set up for functional validation.

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.

During the last few years, servitization arises as a channel for manufacturers to reanimate their business and expand further profit space. Based on an in-depth systematic literature review of 57 papers in the empirical fields, we seek answers for the following question: how servitization strategies influence final value creation through different trajectories? Guided by a proposed framework, we recognize antecedent of servitization process, factors mediating value creation performance and moderator undermining servitized output. Knowledge on servitization is synthesized to provide an enlightening influence path of value creation. It is found that majority of empirical research tends to reveal mediating factor and there is a growing tendency that researchers shift their insights from individual to network, from static to a dynamic and from external to internal.

In order to quick response to emergencies such as air crash and natural disaster, satellites on orbit are desirable to fast achieve imaging sequence for real-time tasks. To select targets in hot area for formation flying satellites, two types of “select-by-grade” algorithms are presented to obtain an appropriate imaging sequence for each satellite. First, ground targets are divided into different groups with different grades in terms of their significances. By making analysis on constraints of target locations and satellite performances, the autonomous task planning is treated as a stepwise optimization problem where the objective function is to maximize the overall imaging gain for all satellites. Second, to solve the optimization problem, the aforementioned different grades ground targets are assigned to different satellites by pre-defined distribution schemes. Finally, a simulation example is provided to illustrate the effectiveness of the proposed algorithms.

This paper describes the dynamic modelling of a vertical take-off and landing (VTOL) aircraft and shows the flatness of the proposed model. Flat systems have the property that the inputs and the states can be written as functions of a set of the system outputs (called flat outputs) and the derivatives of these flat outputs. The flatness property allows to compute an inverse dynamic model of the given system. This can be used in path planning, nonlinear control and fault detection and isolation. The convertible aircraft presented in this paper uses redundant actuators. The advantage of this design is twofold. Firstly, it is possible to configure actuators to optimize both stationary and fast horizontal flight. Secondly, in case of one actuator failure, it provides sufficient flexibility to reconfigure the actuators in order to land safely. An important contribution of this paper is the demonstration of the flatness property for the proposed dynamical model of the convertible aircraft.

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.

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.

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.

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.

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.

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.

This paper addresses the stability analysis of linear systems subject to a time-varying delay. The contribution of this paper is twofolds. First, we aim at presenting a new matrix inequality, which can be seen as an improved version of the reciprocally convex combination, which provides a more accurate delay-dependent lower bound. When gathering this new inequality with the Wirtinger-based integral inequality, efficient stability conditions expressed in terms of LMI are designed and show a clear reduction of the conservatism with a reasonable associated computational cost. The second original contribution of this paper consists in noting that stability conditions issued from the Wirtinger-based integral inequality depends in an affine manner on the bounds of the delay function and also on its derivative. This allows to refine the definition of allowable delay set and to relax usual convex on the delay function. As a result of this new characterization, the LMI conditions allows obtaining stability regions for slow time-varying delay systems which are very closed to the constant delay case.

This paper presents the design of a fractional order combined model reference adaptive control scheme. This approach is based on the estimates of the plant unknown parameters and the adjusted controller parameters, which are obtained from adaptive laws using the identification error, the control error and the so called closed loop estimation errors. Fractional order indirect algebraic model reference adaptive control and fractional order indirect dynamic model reference adaptive control schemes are also proposed in this paper, which are used for comparison purposes with the combined approach. Boundedness of all the signals in the control schemes is analytically proved, as well as the convergence to zero of the mean values of the control, identification and closed loop estimation errors. Simulation results are presented to show the effectiveness of the proposed control strategies.

In this paper we describe a decentralized protocol that is applied for scheduling and management of energy sharing in heterogeneous systems. Particular system we have in mind comprises of agents with different mobility, energy harvesting and energy sharing capabilities herein called aPads, aFish and aMussels. aPads are surface vehicles that harvest energy and are able to transmit it to both aFishes and aMussels. aFish are underwater vehicles that can transmit energy to aMussels or to other aFish, and receive energy from aPads. aMussels, that can either be attached to the sea-ground or float on the surface, can only receive energy, either from aFish or aPads. For such a system to be able to function for an extended period of time, a decentralized energy management protocol should be designed, whose purpose is to schedule and coordinate charging of agents over time. These decisions depend on the current battery levels of different agents, as well as their positions, and should be done in a decentralized manner. In this paper we apply an energy sharing protocol based on TAEMS framework, originally applied to planning and coordination in aerial-ground robotics systems for search and rescue missions. We refine the obtained plans using the coalition-based metaheuristic for vehicle routing problems. We show execution schedules achieved using the proposed protocol and highlight its advantages and flaws when applied to our underwater scenario.

To extend inverted decoupling control method to the non-square multivariable time delay system, this paper presents a block inverted decoupling control approach with internal model structure. In this method, the non-square system is divided into two subsystems, and the decoupling structure is composed of two blocks. Each block in the designed structure is applied to each subsystem. With the proposed method, complex matrix computations are avoided, and the designed controller has low order. The stability of closed-loop system with the proposed method is analyzed in the paper. Numerical simulations are performed to illustrate the effectiveness of this methodology.

The paper proposes a reconfigurable control design of steering and torque vectoring using a variable-geometry suspension system. Torque vectoring control is based on the independent driving of the wheels. Simultaneously, the steering angle is generated by the variable-geometry suspension system by varying the camber angle of the front wheels. The efficiency of wheel tilting and torque vectoring, and their coordinated actuation are in the focus of the paper. In the analysis a reachable set computation method on the polynomial model of the vehicle based on the trajectory reversing method and the Sum-of-Squares (SOS) programming is proposed. Based on the analysis results a reconfigurable control is designed using the Linear Parameter Varying (LPV) method.

The paper presents the modelling of a mixed traffic flow in which look-ahead controlled vehicles with a speed control are driven together with conventional vehicles. Since the speed profile of the lookahead control may differ from that of the conventional vehicle, the structure of the traffic flow changes. The paper analyses the impact of vehicles applying look-ahead control strategy on the traffic flow. In a simulation-based analysis it is shown that the fundamental diagram of the traffic network changes. A new mixed-traffic model incorporates the nonlinearity of the traffic dynamics using a polynomial formula, in which the look-ahead cruise control is considered with a parameter-dependent form. Using these nonlinear forms the impact of the look-ahead vehicles on the stability of the traffic is examined through controlled invariant sets. In this analysis the sum-of-squares programming method is applied. The results are demonstrated through simulation examples using the VISSIM simulation environment.

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.

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.

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. In this paper we present a new individual pitch control algorithm able to simultaneously target the imbalance and reduce oscillating loads at frequencies multiple of the rotor revolution. The effectiveness of the proposed control is demonstrated in a high-fidelity simulation environment.

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 (N-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.

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. In the paper a non-parametric method for detection a change in the mean or in the variance of data is developed and some modifications are proposed. All the algorithms do not use the information concerning the distribution function of observations before and after the change point. The algorithms are applied to detect change points of the characteristics of a gas well.

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.