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An analytical plate bending model is used in a novel measurement method that is being developed for feedback control of material properties in paper manufacturing. The model involves an efficient material model for paper yet allowing a selected level of material complexity. In this study, the effects of the simplifications are studied by comparing the results with a discretized model applied in finite element methods. The boundary conditions required for specifying the censoring location in the real system proves to be challenging and a specific combined load distribution might be necessary to apply the developed model.

Scientific research projects play a positive role in the development of scientific researches. Although researchers continue to study the paper output of projects, the correlation between projects and papers in research contents and topics is still unclear. Based on the data set NSFCD from China, the topics of papers are divided and the matching degree between projects and paper topics is defined to reveal the relationship between projects and papers. The results show that the research contents of projects and papers have some relevance, but the low values of matching degree indicate that the correlation between them is not evident. This work proposes a quantitative method for evaluating projects with paper topics and suggests that the quality of projects’ outputs needs to be strengthened. The papers published by project participants should be related to project contents.

This paper presents a techno-economic analysis of an absorption-based CO2capture process using Monoethanolamine (MEA) as the solvent for pulp and paper mill limekiln. The flue gas specifications were obtained from published limekiln data of a theoretical pulp and paper mill. The process was simulated in Aspen Plus and linked to CAPCOST using a python script for the cost calculations. The CO2capture cost estimates were compared to the only CO2capture costs data available in the literature for limekiln flue gas. Comparing the cost breakdown between the published data and this study, the capital cost difference was found to be highest for the stripper and the compression and dehydration sections. Further, the capture cost sensitivity analysis, evaluating the impacts of key parameters, including flue gas CO2mol%, MEA, electricity, and steam, showed that the capture costs varied from $70 to $82 per tonne of CO2captured.

Jan Willems introduced the system-theoretic notion of dissipativity in his foundational two-part paper which appeared in the Archive of Rational Mechanics and Analysis in 1972. Even earlier, in a likewise pivotal 1971 IEEE Transactions on Automatic Control paper, he investigated infinite-horizon least-squares optimal control and the algebraic Riccati equation from a dissipativity point of view. This note revisits infinite-horizon optimal control leveraging strict dissipativity. We discuss the interplay between dissipativity and stability properties in continuous-time infinite-horizon problems without assuming linear dynamics or quadratic cost functions. Finally, we compare our recent results from Faulwasser and Kellett (2021) to the original findings of Willems (1971).

Image enhancement is the most fundamental steps used to manipulate an image so that the result is more acceptable than the original image, with a try for preserving every detail, in the field of digital image processing. With the high demand of computer visual technology, digital image processing concepts have been rapidly used in many real world applications for information gathering, such as: industrial productions, medical images, video monitoring, intelligent transportations, etc. However, image captured under low light has some issues such that it may contains noise, undergoes color distortion and also may undergoes with some information loss. Again industry like medical and satellite we can't afford single information to be miss, so for that purpose low light image enhancement is a must needed concept for current emerging world. Here in this paper, we analyze different methods of image enhancement and their result, mainly this paper focus on different fusion-based methods, other methods such as retinex based methods, fuzzification etc.

Today, we have many options for hardware and software technology solutions for the migration of the manufacturing sector to an industry 4.0. However, there are many cases in which the adoption of new technologies has been slowed mainly due to uneven growth in the industry, little standardization, poor systems’ integration, lack of knowledge, and the lack of providers who understand the real needs of companies. The purpose of this work is to present a methodology for selecting the proper technology that was developed after characterizing and quantifying the industry's requirements. Our methodology presents criteria for evaluating and selecting technology providers that focus on four dimensions: technical criteria, purchase criteria, additional services for the operation of the products, and operating costs. This methodology represents a practical way to qualify technology providers, assigning them a quantitative score so that the decision-making process is much faster, more convincing, and compelling, especially when adopting new technology. The methodology was built and used into a paper mill for selecting sensor suppliers to implement a condition monitoring system that captures vibration signals at different points of paper presses. The evaluation was successful, resulting in a winning provider being selected and the IoT sensor-based system implemented.

Aiming at the problems with low efficiency and low standardization in the establishment of complex product Research & Development(R&D) process, a combination and optimization method for R&D activities is proposed in this paper. Firstly, process module is established by combining related R&D activities using process network diagram and description text. Then, a method is established for similarity representation between process module and design requirements, on which the module reorganization is based. And optimal combination scheme of modules is obtained by an adaptive genetic algorithm. Finally, the effectiveness of proposed method is verified through the example of a product R&D process.

This paper provides a conceptual analysis of the role of business continuity management (BCM) as a key enabler of supply chain resilience (SCRE). Through an analysis of the extant literature, the authors show how BCM can facilitate and enhance key constructs that are directly linked to SCRE. The results show that the existing BCM lifecycle – already widely used in the industry – has the potential to play a decisive role in boosting agility, flexibility, velocity, visibility, and collaboration, as well as response and recovery capabilities.

Nowadays, technologies and strategies to bring processes toward the Industry 4.0 paradigm go beyond simple paper proposals and focus on their implementation. This article presents a case study of University-Business collaboration focused on technology transfer as part of a viable solution to real problems in the company. The objective of the collaborative project was the transfer of knowledge and use of technologies by the research group and, together with the knowledge based on the experience of the company's maintenance department, to solve a real problem in the industry. Therefore, we set out with the industry the objective of implementing a system for early detection of failures in press bearings to ensure that maintenance personnel performs maintenance actions on time. We combined agile methodologies and design thinking for the implementation because agile methodologies allow controlling the development progressively, and design thinking focuses on the user's needs. The implementation process was executed in six phases: empathize, ideate, define, test, prototype, and implement. The project was successfully implemented in six months and met industry requirements. Combining agile methodologies and design thinking was fundamental for the implementation, as it was possible to get feedback from industry personnel and to have materialized progress in each meeting.

Machining distortion caused by residual stresses is one of the major challenges in the production of thin-walled monolithic parts, which are widely used in the aerospace industry. The relevant influencing factors include the selected process parameters, the machining sequence as well as the used fixture system. This paper presents an adaptive fixture system for reducing machining distortion caused by residual stresses in milling. To validate the advantage of this system, machining distortion experiments are conducted on monolithic parts made of Ti-6A1-4V using a conventional and the adaptive fixture system.

Intrinsically, many real control problems are mathematically challenging. Thus, we often relax the original problem to obtain a tractable problem. In this paper, we consider a computational technique to solving a nonlinear stochastic optimal control problem in a direct manner. Our computational approach employs the idea of the recently developed technique, called deep unfolding. Deep unfolding is a deep learning method that is model-based, used to accelerate iterative algorithms. For the finite-horizon optimal control problem, we regard each state transition of the discrete-time dynamical system with control inputs and disturbances as an iteration step. Then, we unfold those state transitions into the layers to construct a deep neural network so that each of those layers contains a trainable parameter of the control input. This produces a computational graph. Once the computational graph is fixed, the control inputs are computed by training the deep neural network. This open-loop method is then integrated into model predictive control to close the loop. The advantages of our computational technique are not only the ability of handling nonlinear state transitions and non-Gaussian disturbances, but also its simplicity. The feasibility and benefit of the proposed technique are demonstrated by numerical experiments using a continuous stirred tank reactor model, which is highly nonlinear.

In this paper, adaptive radial basis function neural network based sliding mode control of a fast acting superconducting magnetic energy storage (SMES) is reported. With the converter interface SMES is installed and connected with the wind-diesel micro grid to carry the required power exchange to improve the system frequency. With sliding surface design and neural network using a radial basis function, a sliding mode controller action is used to control the converter, and achieve the desired operation of SMES. Computer simulations are performed and presented to show the superiority of the proposed methodology with the system subjected to load and wind power variations.

Stabilization problem of inverted pendulum system is one of the challenging issues to the researchers. To stabilize this unstable inverted pendulum system, linear quadratic regulator (LQR) controller has been designed along with the Kalman filter. Kalman filter estimates the state variables of the system and LQR controller takes these estimated state variables as inputs. The performance of this proposed controller has been tested for both stabilization and tracking problem of inverted pendulum system. Both the stabilization and tracking responses of the closed-loop system satisfies the required performance specifications. The responses of the closed loop system with Kalman filter and without Kalman filter also have been compared. Kalman filter based closed-loop system eliminates the effects of process and sensor noises that are present in the outputs of the system. The performance of this proposed controller is also compared with LMI based robust controller that had been earlier designed for the same system.

The world is facing many and different, difficult, and challenging problems. Ways are explored, to make the world a safer and better place for all humans using Ethical AI and wisdom. Certain questions as to what intelligent machines can do and what cannot do are reviewed. The importance of global cultural diversity and its relation to global coherence is analyzed. Basic definitions of artificial intelligence (AI) are given. The relation of AI and ethics are provided in some details. Technical problems associated with its design and conceptualization, the cognitive revolution, brought about by the development of AI also gives rise to social and economic problems that directly impact humanity. It is vital and urgent to examine AI from a moral point of view. A new industrial revolution is proposed, the Wise Anthropocentric Revolution: INDUSTRY 6.0. We examine the relationship between wisdom and morality within the basic framework of neo-Aristotelian Virtue Ethics

This paper deals with the problem of adaptive output regulation for multivariable nonlinear systems by presenting a learning-based adaptive internal model-based design strategy. The approach builds on the recently proposed adaptive internal model design techniques based on the theory of nonlinear Luenberger observers, and the adaptation side is approached as a probabilistic regression problem. Unlike the previous approaches in the field, here only coarse assumptions about the friend structure are required, making the proposed approach suitable for applications where the exosystem is highly uncertain. The paper presents performance bounds on the attained regulation error and numerical simulations showing how the proposed method outperforms previous approaches.

For mitigation of the spread of human infectious diseases, this paper proposes nonlinear PI control laws of vaccination, isolation, and contact regulation. Using the SIQR model, the control laws are designed to achieve not only internal stability on an arbitrarily large domain in the state space, but also input-to-state stability with respect to inflow perturbations of immigrants and newborns. The control laws get rid of the precise knowledge of the nominal rates required by feedback control. This paper pursues incorporation of integral action in control to render a coarse estimate a real equilibrium. It is demonstrated how to accomplish it in the presence of severe range limitations of control inputs.

We consider the fractional optimal control problem with state constraints. The fractional calculus of derivatives and integrals can be viewed as generalizations of their classical ones to any arbitrary real order. In our problem setup, the dynamic constraint is captured by the Caputo fractional differential equation with order α ∈ (0, 1), and the objective functional is formulated by the left Riemann-Liouville fractional integral with order β ≥ 1. In addition, there are terminal and running state constraints; while the former is described by initial and final states within a convex set, the latter is given by an explicit instantaneous inequality state constraint. We obtain the maximum principle, the first-order necessary optimality condition, for the problem of this paper. Due to the inherent complex nature of the fractional control problem, the presence of the terminal and running state constraints, and the generalized standing assumptions, the maximum principle of this paper is new in the optimal control problem context, and its proof requires to develop new variational and duality analysis using fractional calculus and functional analysis, together with the Ekeland variational principle and the spike variation.

This paper deals with stochastic model predictive control (SMPC) based on polynomial chaos expansion (PCE) for linear systems with time-invariant stochastic parametric uncertainties and time-varying stochastic additive disturbances subject to chance constraints on states and inputs. Exploiting terminal ingredients in the SMPC problem and a hybrid update strategy, a recursively feasible optimization problem is formulated. Moreover, stability of the system of PCE coefficients can be shown. Furthermore, in the paper the performance and computational complexity of SMPC based on PCEs is compared to tube-based SMPC and robust model predictive control (RMPC) is analyzed and benefits are demonstrated in simulation.

This paper examines the control of abdominal perfusion during medical interventions. The goal is to avoid safety risks such as excessive retained fluid volume, tissue damage/trauma, and intra-abdominal hypertension. The paper presents a perfusion system with peristaltic pumps dictating fluid inflow/outflow rates, and uses model-based control to avoid multiple safety risks simultaneously. Specifically, the paper: (i) introduces a discharge efficiency metric reflecting the risk of tissue trauma and/or outflow cavitation; (ii) identifies a dynamic model of discharge efficiency from animal test data; and, (iii) develops a safe perfusion control algorithm based on this model. The algorithm receives three user inputs: a desired inflow rate, a desired perfused volume, and a safety bound on discharge efficiency. The algorithm minimizes the deviation of the commanded inflow/outflow rates from a nonlinear model reference controller, subject to a barrier constraint on discharge efficiency. This furnishes a switching control structure providing convergence to the desired perfusion settings in the absence of outflow occlusion, and operating safely when outflow is occluded, as shown in simulation-based validation studies.

State estimation is essential for enabling battery management systems (BMSs) to monitor, control, and optimize battery performance. The first step toward this is the ability to predict a battery's behavior given its input current and present state. In the early stages of new battery chemistry development, prior to commercialization, lab-fabricated battery cells might be used for characterization and BMS development. Such custom-fabricated batteries are often more prone to anomalies in their cycling behavior, including loss of connectivity and instantaneous internal shorts. The use of battery cycling data containing such anomalies can negatively affect modeling accuracy and system predictability. This paper uses the K-means clustering method to detect outlier patterns in battery cycling, thereby enabling the extraction of sanitized cycling data. A feedforward neural network is then trained to predict battery voltage one step ahead, given the input current and prior voltage history. The paper demonstrates this machine learning approach for cycling data from laboratory-fabricated lithium-sulfur (Li-S) cells. This demonstration highlights both the accuracy of the proposed voltage prediction algorithm and the degree to which the proposed outlier detection algorithm helps improve this accuracy.

This paper develops a scalable model of the dynamics of gas exchange during peritoneal oxygenation, motivated by the potential of such oxygenation to provide life support for patients with severe respiratory failure. The literature presents peritoneal oxygenation experiments for both large and small animals, including adult swine, rabbits, rats, and piglets. Results of these experiments suggest a potential discrepancy, with the benefits of peritoneal oxygenation possibly being stronger for smaller animals. We hypothesize that this size dependence is at least partially attributable to the effect of animal size on the ratio of peritoneal diffusion surface area to animal volume. The paper develops a scalable multi-compartment model of gas transport dynamics during peritoneal oxygenation. Simulating this model provides important insights regarding the potential impact of animal size on the viability of peritoneal oxygenation.

Jumping robots are interesting devices that offer several advantages in terms of navigation about cluttered environments. However, they present unique challenges with respect to their design and control. In this paper, we propose a design that uses four planar thrusters/propellers on a jumping robot. Such a system provides better maneuverability due to agility provided by the propellers to guide the motion. From energy consumption perspective, we use the gravity for free fall and a spring mechanism to execute the jumping motion without loss of much energy on impact. The primary contribution of this paper is developing a novel navigation and control method for such jumping robots to go to the desired goal. In this paper, we present a projectile motion planning approach for the control of the proposed system. We propose proportional (P) and proportional-derivative (PD) controllers that compute the launch velocity required for the jumping drone after impact with the ground to follow a projectile motion in each jump to reach the goal position. The jumping drone bounces after impact with the ground, and the drone is then actuated for a certain time till it attains the required launch velocity after which it is made to move freely under the influence of gravity. Such a system shows significant reduction of energy consumption (by 81.35%) as compared to a normal drone navigating to the same goal location. Simulation results validate the effectiveness of the proposed approach.

The paper explores the use of knee stiffness as a parameter in the design of wearable knee assistive devices for augmenting human postural balance. The knee moment-angle relationship is utilized to estimate the quasi-stiffness of the knee. The measurement methods are carefully chosen to be non-invasive without rigid joint attachment to allow observation of unimpeded quiet stance. The relationship between identified biomechanical parameters and computed stiffness estimates is analyzed, and the resulting estimates are employed in the controller design of a stiffness-based knee assistive device. The paper also investigates the biomechanical response of the human body to the modulation of applied stiffness in the presence of varied visual stimuli. This research is a crucial first step toward designing knee-based assistive devices to enhance human postural balance in destabilizing environments.

Autonomous vehicle navigation and control require both planning and successful following of a desired path through an environment. This is often performed by a hierarchical framework that includes both a path planning algorithm and a path following controller. However, these two levels often operate with different modeling assumptions, constraints, and cost functions, leading to several challenges when integrated. To demonstrate the challenges that arise specifically when mapped obstacles represent regions that can be traversed but with varying traversal costs, this paper builds an intuitive control hierarchy consisting of an A* path planner, a Kalman filter to estimate traversal costs, and a model predictive controller. While each of these elements have been deeply studied individually, this paper identifies challenges that can arise from their coupled behavior and categories existing literature that addresses some of these challenges. Simulation results for a ground vehicle traversing regions of varying cost illustrate both the benefits and potential pitfalls of this hierarchical integration, motivating its further study for vehicle autonomy.

The aim of this paper is to explore potential of feedback control design based on a switching composition of two controllers. The paper considers two stochastic optimal controllers for the motion of an unmanned aerial vehicle (UAV) in the horizontal and vertical planes. We show that the two controllers, one for reaching a point in the horizontal plane and the other for reaching and keeping a desired altitude in the vertical plane, can be computed using Cartesian coordinates. To reach a desired waypoint in 3D, both controllers are necessary while the vehicle has to reach simultaneously the horizontal and vertical navigation goals. For this reason, we compute the expected time of each controller toward its goal in 2D. Then, we propose a switching rule that guarantees the simultaneous arrival of each controller to its 2D goal, which is equivalent to the vehicle reaching the 3D waypoint. Finally, we explore a possibility for improvements of the switching rule using reinforcement learning and an actor-critic neural network. The paper results are illustrated by numerical simulations.

This paper examines the generation of visibility maps for the purposes of robotic path planning. The paper proposes a map construction technique that extends a version of the viewshed problem to calculate the rays to and from all discrete points on the map efficiently. The introduced form is parallelizable, making it suitable for fast GPU computation necessary for realtime calculation during path planning. New algorithms are introduced to generate worst- and best-case visibility maps by bounding precision uncertainties in the discretized map. Visibility theory is then applied to quantify the detectability of each viewpoint in the space based on atmospheric visibility models. Path planning is executed on the generated cost map illustrating its usability for visibility path planning.

Ina recent paper Ortega et al. (2021a) the authors proposed the first solution to the problem of designing a globally exponentially stable(GES) flux observer for the interior permanent magnet synchronous motor. However, the establishment of the stability proof relies on the assumption that the adaptation gain is sufficiently small—a condition that may degrade the quality of transient behavior. In this paper we propose a new GES flux observer that overcomes this limitation ensuring a high performance behavior. The design relies on the use of a novel theoretical tool—the generation of a “virtual” invariant manifold—that allows the use of the more advanced Kreisselmeier's regression extension estimator, instead of a simple gradient descent one. We illustrate its superior transient behavior via simulations.

The manufacturing process of semiconductor devices is one of the most complex processes in manufacturing industry. The devices fabrication is performed through hundreds of sequential process steps with different recipes. The level of complexity is also increasing due to the high demands in terms of feature size and number of devices. Maintaining high yield and good quality production are the main objectives of these industries. These objectives can be achieved by adopting efficient maintenance strategies. In this context, a suitable prognostic model is required in order to schedule the maintenance actions. Among the different prognostic approaches, data-driven ones received a lot of attention since they do not require any specific knowledge for modeling these complex processes. Although the advances in data-driven prognostic works, there is a real lack of survey papers that overview and discuss the existing approaches for this industry over the past 10 years. Therefore, this paper presents a systematic overview of data-driven prognostic for semiconductor manufacturing. It investigates the different used methods, the challenges of their application and the unexplored research areas.

An efficient q-Markov covariance equivalent realization (QMC) formulation is presented in this paper. This efficient solution matches all Markov and Covariance parameters of a finite-dimensional system using a finite amount of data. The frequency responses of that system is also matched by the efficient QMC solution. The existence condition of this efficient QMC solution and its parametrization is given in this paper. Then, a generalized algorithm for system identification using the efficient QMC is given. The example is set up by a cantilever beam. Results show that the efficient QMC fully captures Markov and covariance parameters and frequency responses of the cantilever beam.

This paper presents an elaborate mathematical model of dynamic glucose metabolism. Although it is known that the control mechanism of glucose metabolism is partly related to the brain, almost all existing papers ignore the brain mechanism in the dynamic glucose metabolism of diabetes. Then, we propose a refined mathematical model to integrate the brain-based regulatory mechanism with leptin into the conventional FDA approval model for all human beings to obtain an optimal combined treatment of not only insulin therapy but also leptin therapy. The effectiveness and limitations of the proposed combined therapy with insulin and leptin for not only type 1 diabetes mellitus but also type 2 diabetes mellitus are also evaluated through in silicoexperiments.

This paper introduces CoGoV: a new Matlab-based toolbox for the simulation of Command Governor supervision strategies applied to distributed motion planning problems for multi-vehicle systems. CoGoV is an open-source and object-oriented software and contains several classes for modeling unmanned vehicles, designing control strategies and solving optimization problems for achieving prescribed tasks in complex simulation scenarios. Because of its modular structure, it can be used to supervise many kinds of autonomous vehicles in marine, terrestrial and aerial domains. Throughout the paper, the benefits of the toolbox are presented by means of simulations involving the supervision and coordination of autonomous marine surface vehicles. The CoGoV toolbox is available at https://github.com/vinz-uts/CoGoV/

This paper deals with load frequency control (LFC) problem for power system. The control is conducted to retain power demand supply balance by controlling output of generators in power network. In order to realize LFC with low power generation cost while maintaining control performance, this paper proposes frequency control method using Moving Horizon Estimation (MHE) and Model Predictive Control (MPC). First, MHE is used to estimate the state of the system. Next, optimization calculation is performed using MPC to determine the command values for each generator. Finally, simulation of load frequency control is carried out to verify the effectiveness of the proposed method.

Dynamical systems can be designed to exhibit a range of distinct behaviors, which all arise from the same set of continuous dynamics when the latter bifurcates, triggered by a switch in one of its scalar parameters. Building on recent advances that introduce motivation and value dynamics as an efficient way to design multi-behavioral systems, this paper lifts some of the existing restrictions on what kind of planar vector fields can be combined to produce bifurcations. This relaxation enriches the class of dynamical systems that such an approach applies, and gives rise to new behaviors. The paper identifies new analytical conditions under which this new set of planar vector fields can undergo Hopf bifurcations and result in a multi-behavioral system. Numerical simulations and experimental results confirm the theoretical predictions for the existence of the Hopf bifurcations and the applicability of the theory in real systems.

This paper considers a single-trajectory system identification problem for linear systems under general nonlinear and/or time-varying policies with i.i.d. random excitation noises. The problem is motivated by safe learning-based control for constrained linear systems, where the safe policies during the learning process are usually nonlinear and time-varying for satisfying the state and input constraints. In this paper, we provide a non-asymptotic error bound for least square estimation when the data trajectory is generated by any nonlinear and/or time-varying policies as long as the generated state and action trajectories are bounded. This significantly generalizes the existing non-asymptotic guarantees for linear system identification, which usually consider i.i.d. random inputs or linear policies. Interestingly, our error bound is consistent with that for linear policies with respect to the dependence on the trajectory length, system dimensions, and excitation levels. Lastly, we demonstrate the applications of our results by safe learning with robust model predictive control and provide numerical analysis.

This paper presents a methodology for implementing a vector field-based guidance algorithm for path following in three-dimensional space. The study focuses on converting the path given as a parametric function of a three-dimensional space curve into an implicit form. The paper explores how to compute vector fields in the space surrounding the reference curve by conducting implicitization on the parametric curve. The efficiency of computing the vector field using the implicit form curve is compared to the efficiency of computing the vector field using generalized cylinders. The results indicate that the coefficient area for the basis vectors becomes wider when using the approximate orthogonal surface compared to generalized cylinders.

In this paper, we deal with quantum information decoding. Quantum codes are a known technique for correcting errors of quantum information caused by noise or operational errors and one of the most popular types of codes is the so-called stabilizer code. Various implementation methods have been proposed to encode information into a stabilizer code, but there have been few studies on decoding to extract the original information from the code words. In this paper, we propose a decoding method for stabilizer codes using dissipation dynamics and show that the original information can be extracted from the code words.

The railway network is equipped with track switches that enable trains to move from one track to another. Existing track switch actuation is performed in open loop. Closed-loop control and redundant actuation can be applied to provide an opportunity to enhance the reliability of the railway track switch system. This paper looks at the control design development applied to a laboratory scale demonstrator of a redundant actuation concept. Open-loop control is first applied to this High Redundancy Actuation (HRA) track switch lab prototyping. According to the open-loop experimental input and output, a simplified linear model of the system is identified and estimated. A model-based Proportional-Integral-Plus (PIP) controller is then developed and tested, including nominal control and fault tolerant control. The results show that the proposed control method can meet the actuation requirements of the track switch system with high redundancy actuation. Another innovation of this paper is direct model-based control design, and this small HRA prototyping provides valuable insight into the future full-scale system.

This paper considers the problem of approximating the infinite-horizon value function of the discrete-time switched LQR problem. In particular, we propose a new value iteration method to generate a sequence of monotonically decreasing functions that converges exponentially to the value function. This method facilitates us to use coarse approximations resulting from faster but less accurate algorithms for further value iteration, and thus, our method is capable of achieving a better approximation for a given computation time compared with the existing methods. Two numerical examples are presented in this paper to illustrate the effectiveness of our method.

It is well known that the dependability of equipment on a production site is strongly linked to the quality of maintenance operations. This paper focuses on repair shops dedicated to the overhaul of defective equipment from several sites, via circular economy strategies. The literature provides analytical and numerical models that allow the performance of a repair shop to be evaluated under certain conditions. However, the majority of studies do not highlight the impact of component degradation after each equipment overhaul. We propose to study the life cycle of components throughout a repair process. Each component begins a cycle with a discrete failure rate that increases with the number of operations undergone. At the end of their life, components undergo an operation that allows them to have a second life. The novelty of this paper lies in the evaluation of the impact of the component life cycle on the performance of a repair shop. We propose a closed-loop model with which we were able to carry out a case study in the railway sector. The results show that considering several levels of component degradation in the repair shop has an impact on maintenance costs.

This paper deals with the preview gust alleviation control system of an aircraft, which mitigates dangerous vertical acceleration due to wind gust using the forward wind velocity measured by the LIDAR (LIght Detection And Ranging). Although the vertical acceleration can be alleviated by preview control, the preview feedforward part tends to be sensitive to measurement errors. In order to maintain consistently high acceleration alleviation performance, the preview feedforward needs to be robust to errors. This paper presents a design method for preview feedforward gains that can reduce the effect of measurement errors. Nonlinear simulation results of a small jet aircraft are provided to show that the designed preview control gain can both alleviate vertical acceleration and be robust against random and bias errors.

Modern industrial systems are complex and large-scale, constituting multiple components and interconnections. These components interact through material and energy interfaces. Energy can be considered as a universal quality that allows system characterisation across different physical domains (thermal-fluid, mechanical, electrical and chemical). Therefore, an energy representation of a system can be used as a basis for fault detection and diagnosis. In order to cope with representing complex interconnections of systems, graphs can assist in presenting the structure and attributes of the system components. This paper reviews FDI applications using energy and graph-theoretical methods and then focuses on a unique approach that combines the strengths of both approaches. It furthermore specifically exploits the attributed graph as mathematical formalism for characterising an industrial process in terms of energy. The graph formalism allows capturing energy characteristics while retaining the structural information, i.e. linking the energy attribute to a physical location in the process. This approach is named the energy graph-based visualisation approach. The contribution of this paper lies in the portrayal of the full research endeavour for the purposes of comparison and future reference. Two applications are presented to illustrate the usefulness of the approach namely a gas-to-liquids process and a practical heated two-tank system.

This paper focuses on fault detection for oscillatory failures in hydraulic actuators of aircraft. These oscillatory failures, if not rectified in time, can cause severe loads on the airframe and eventually lead to structural damage. In this paper, a novel oscillatory failure case (OFC) detection algorithm is proposed which uses a nonlinear observer based on the mathematical model of the actuator in order to generate residual following a Linear Predictive Coding analysis of the residual to detect oscillatory behavior. Finally, OFC decisions are made after the quantification of the residual in frequency domain. To illustrate the effectiveness of the proposed algorithm, results are presented using a high-fidelity industrial benchmark simulation. Furthermore, a comparative study is provided against an existing technique.

The population of most developed countries is aging; thus, the median age of the global workforce continues to rise. Human aging often results in a decline in physical and cognitive abilities, which may adversely affect the performance of labor-intensive manufacturing systems. Older workers embody profound experience and refined skills, which are success factors for manufacturing companies. Therefore, it is important for manufacturing companies to ensure that older workers remain active and productive. Identifying the potential of an aging workforce, employing technical assistance systems to meet their needs, customizing work flow processes, imparting proper training, and utilizing their experience and skills may provide a competitive advantage for the company. This paper reviews the relevant literature to understand how aging influences workers’ learning in the manufacturing and service industries and identifies management concepts and technologies suitable to support an active aging workforce. We report preliminary insights and discuss selected papers on how aging influences learning-by-doing, life-long learning, training, and experiential knowledge retention. Finally, we propose some future research directions.

The paper discusses attacks on networked control loops by increased delay, and shows how existing round trip jitter may disguise such attacks. The attackers objective need not be de-stabilization, the paper argues that making settling time requirements fail can be sufficient. To defend against such attacks, the paper proposes the use of joint recursive prediction error identification of the round trip delay and the networked closed loop dynamics. The proposed identification algorithm allows general defense, since it is designed for delayed nonlinear dynamics in state space form. Simulations show that the method is able to detect a delay attack on a printed circuit board component mounting servo loop, long before the attack reaches full effect.

This paper proposes a data-driven cyberattack detection method in smart grids based on reservoir computing (RC). It has been discovered that standard recurrent neural networks such as long short-term memory (LSTM) and gated recurrent unit (GRU) achieve high classification performance on the attack and contingency scenario. However, those models require large computational time for the learning process, and hence their re-training during daily operation of the grid is impractical. To overcome this challenge, this paper adopts echo state network (ESN) as a specific architecture of RC, which is known to be a fast learning framework. Numerical experiments with standard datasets show that the proposed method greatly reduces the computational time with low performance degradation.

In this paper, a hybrid control strategy based on the super-twisting sliding mode approach and artificial neural network method has been proposed for collective blade pitch (CBP) control of floating wind turbines (FWT) above the rated wind speed. Besides the presence of uncertainties and external disturbances due to the complexity of the model of wind turbines, this paper uses the radial basis function (RBF) neural network to approximate model uncertainties and unmodeled dynamics, reducing the controller dependency on the exact model of the system. The implemented neural network adaptive law has been achieved based on the Lyapunov stability, and the convergence of the closed-loop system is guaranteed by adjusting the learning rate. As the floating wind turbine is a highly nonlinear system, the main objectives are limitation of platform pitch motion and related fatigues, blade fatigue load reduction, and essentially power regulation. Here, using the FAST simulator, the proposed controller has been tested by achieving the required dynamic and static performance. The simulation results illustrate the efficiency of the investigated strategy by comparing it with and without RBF neural network on the FWT.

This paper proposes an integral sliding mode (ISM) based unknown input observer (UIO) which is able to perform fault diagnosis (FD) in condition of lack of knowledge of the plant model. In particular, a two-layer neural network (NN) is employed to estimate online the drift term of the system dynamics needed to compute the so-called integral sliding manifold. The weights of such a NN are updated online using adaptation laws directly derived from theoretical analysis, carried out in this paper. Finally, the proposal has been assessed in simulation relying on a benchmark model of a DC motor.

The paper concerns a development of an approach for implementing fault-tolerant control (FTC) of automated guided vehicles (AGVs). The main challenge considered in the article is the way of taking into account in the scheduling algorithm the delays occurring in the system resulting from the use of transport means. Mostly, multiple AGVs are designed and implemented as a firm real-time system in which delays are treated as faults. The proposed framework allows to compensate the faults in the real time. In the paper a multiple AGVs that transport item from an output of a production system to a high bay warehouse is presented. The article presents the non-deterministic nature of a multi-AGV system and includes a mathematical description of the system using (max, +) algebra. Having a analytical description of AGVs system, a new FTC strategy is proposed. Moreover, in the paper an illustrative case study showing the performance of the developed approach is presented.

A layout has a big influence on the output of a plant or a facility. Because of that, many tools and methods linked to layout planning (LP) are already established in research and industry. The planning of a layout itself is an iterative workflow where changes within a project must be done continuously depending on the planning phase. On the other hand, the layout is often the basis for further activities in a project, e.g. commissioning. In consideration of these challenges, this paper presents an approach for LP based on so-called Production Module Containers (PMC). They gather all information needed for the LP in a defined standard format and have therefore the potential not only of making the whole planning process more easier, but also to enable an interoperable process between heterogeneous tools. This paper shows the basic concept of these PMC and implements a prototypical software to demonstrate the potential of this module-based approach for the process of LP.

The rail-road train operational companies globally, and particularly in Americas, have been facing a challenge of growing shortage of skilled human operators crew due to ongoing retirement wave of experienced aged operators combined with growing needs of crew for special slow-speed applications. This challenge can be addressed through development of advanced automation and control solution like Remote Control Locomotive (RCL) to help reduce the crew-size, improve train low-speed operational performance and productivity. This paper covers our R&D work towards a new mainline RCL product of Wabtec – one of the leading diesel-electric locomotives supplier from USA. This mainline RCL speed-controller solution is novel due to use of – i) model-cum-heuristics based intelligent open-loop startup logic & sequence control with smart exception-handling, and ii) the post-startup closed-loop adaptive speed control using novel self-adaptive formulation. This paper describes the RCL architecture, state-transition and sequence control scheme of intelligent open-loop start-up control, and the adaptive PID (Proportional-Integral-Derivative) speed-control algorithm. The performance of this novel RCL speed-controller is thoroughly evaluated and tuned using matlab-simulink framework using detailed in-house main-line locomotive-train simulator having real-life rail-track data and operational specifications. This novel RCL speed-control solution has been deployed on Wabtec's RCL Locotrol® controller product and field-validated on Wabtec's inhouse test-track at Erie, and some real-life tracks of mainline train operation customers including coalmine & other railroad applications. The performance of this novel RCL speed-controller is found satisfactory in terms of train and some exception handling, RCL speed tracking and regulation control behavior in the presence of realistic disturbances of grade-changes, train size and load changes across light & heavy trains. This solution is finally inducted and sold successfully as a new mainline RCL product offering by Wabtec.