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In this work, the fabrication of Rochelle salt based piezoelectric structures is illustrated. Structures composed of paper and Rochelle salt are easily manufactured using simple processes. Both manufacturing and the material itself are environmental friendly. Additionally Rochelle salt is biocompatible. In the paradigm of a cleaner piezoelectric technology, the fabrication of active sensing or actuating devices is developed. Thus processing method, material and piezoelectric properties have been studied: (1) pure crystals are used as acoustic actuator, (2) properties of paper impregnated with Rochelle salt are detailed, (3) charge generation is demonstrated on the impregnated material. Actuating and sensing devices are reported in order to highlight the potential of this green piezoelectric material. [ABSTRACT FROM AUTHOR]

Abstract: Good cross-directional control of sheet variables in a paper machine depends on a number of factors. One such factor is the correct alignment of actuators with their corresponding responses. The alignment changes with time either locally or across the whole paper sheet due to a variety of reasons. In this paper, we provide an online mapping technique based on estimating the cross-correlation between actuator movement and the measurements. This procedure involves two steps–in the first step (called alignment monitoring) we determine if the current mapping is wrong at any CD position, and in the second step (called re-mapping), the alignment of actuators is corrected. The main advantage of this method is in its ability to re-align under closed loop conditions with minimal operational disruption. [Copyright &y& Elsevier]

Abstract: The studies reported here examined differences in user behaviour when presented with a low-fidelity paper prototype compared to fully operational product, and evaluated the effectiveness of different types of enhanced labelling of controls. In the first study with a paper prototype, 30 users of high-pressure washers were asked to choose the settings of the temperature control for different cleaning objects, comparing standard with information-enriched control labelling. In the second study, 34 users operated a real high-pressure washer with different forms of control labelling. The results of both studies provided evidence for some benefits of an information-enriched control labelling over traditional temperature-centred controls labelling. Furthermore, a comparative analysis of the data of the two studies suggested that low-fidelity paper prototypes may have to be used with caution. Therefore, designers need to be aware that the behavioural effects induced by different design modifications may be overestimated when using paper prototypes. The implications of the findings are discussed within the framework of an enlarged concept of fidelity. [Copyright &y& Elsevier]

Abstract: Hyphenated techniques like Py-GC/MS (pyrolysis-gas chromatography/mass spectrometry) result often in complex chromatograms containing a high information density. Extracting meaningful information is difficult and time consuming. Especially with automatic sampling, data evaluation becomes a major bottleneck. An effective approach to automate the characterization of chromatograms is presented. The database approach consists basically of three steps: measuring reference substances to generate a database, measuring unknown or difficult samples and using the database for the identification of these samples. The compiled database contains a collection of mass spectra of pyrolysis products with data such as retention index and additional information from which reference substances the pyrolysis products may derive. The developed database approach has been tested on designed mixtures of compounds forming “white pitch”. “White pitch” is a typical type of deposit found in paper production processes deriving from chemicals used in coating processes. [Copyright &y& Elsevier]

One of the main tenets of Gricean pragmatics is the distinction between what is said and what is implicated; that is, between truth-conditional and non-truth-conditional meaning. The main contribution of post-Gricean pragmatics was a new definition of what is said as a pragmatic and truth-conditional meaning (Carston, 2002; Recanati, 2010; Moeschler, 2018a). However, implicated meaning (mainly conversational implicature) is still defined as non-truth-conditional because it is cancellable without contradiction. The main consequence is that a speaker can deny having intended her implicature, which results in the hearer having no guarantee of the truthfulness of his inference. This paper explores the relationship between truth and pragmatic implicated meaning and posits that the assessment of a truth value of an inference is a crucial step in the comprehension process. To demonstrate this hypothesis, the paper develops a relevance-based approach to inference, which is coupled with Kahneman's dual cognitive system. I argue that the triggering of an inference is an intuitive and automatic process controlled by System 1, whereas its assessment as a true or false assumption is controlled by System 2. In other words, System 2 is responsible for what is defined in relevance theory as epistemic vigilance. • Explaining the dissociation of the inferential process from the assignment of a truth value to the inferred conclusion. • Assigning the inferential task to Kahneman's System 1, and the task of determining truth value to his System 2. • Fake news is assigned the truth value 'true' because it is compatible with the common ground. • The detection of fake news requires the intervention of System 2, which compares the proposition with the common ground. • Debunking fake news is only possible if an alternative common ground is accessible to the audience. [ABSTRACT FROM AUTHOR]

Denial of services (DoS) attacks exist in wind integrated power system. DoS attacks can cause network-induced delay and packages loss in information transmission. Meanwhile, considering the parameter perturbation of controller and system model uncertainty in wind integrated power system, these may cause the system dynamic performances degradation or even instability. Based on the above considerations, the joint non-fragile automatic generation robust control of wind integrated power system under DoS attacks is studied in this paper. In order to ensure the expected system performance and more effectively utilize the limited network communication resources under DoS attacks, a novel dynamic multi-event driven mechanism based joint non-fragile H ∞ automatic generation control method is proposed. By constructing a suitable Lyapunov-Krasovskii functional and utilizing the Shur complement lemma to handle nonlinear matrix inequality, the sufficient conditions are derived to guarantee the asymptotic stability of wind integrated power system under DoS attacks. Furthermore, the performance of the proposed non-fragile regulator is demonstrated through a four-area wind integrated power system to show the feasibility and applicability. The analysis result indicates that the proposed scheme provides stronger robustness, higher wind energy utilization efficiency and more efficient communication mechanism. [Display omitted] • A multi-event driven mechanism (MEDM) based joint non-fragile control scheme is derived. • The wind-integrated power system model based on MEDM is established. • The MEDM is integrated into the joint non-fragile controller design under DoS attacks. • The MEDM based joint non-fragile H ∞ automatic generation control strategy is proposed. • It provides stronger robustness and higher wind energy utilization efficiency. [ABSTRACT FROM AUTHOR]

Mechanical systems subject to constraints play a essential role in the field of control engineering, profoundly influencing the design and performance of control strategies. Consequently, there is a compelling need to explore diverse control methods to effectively tackle the complex task of stabilizing nonlinear systems while ensuring the constraints are not violated. In this context, this paper proposes a design procedure for position-constrained controllers in robot manipulators. The solution relies on the construction of a diffeomorphism (a differentiable coordinate transformation) that maps all the trajectories of the constrained dynamics into an unconstrained one. The controller design is carried out in the unconstrained dynamics without dealing directly with the constraints. The proposed family of controllers employ an explicit control law which circumvents the need for additional time-consuming computation for feasibility and/or optimization. Moreover, the proposed controller is parametrized by a class of diffeomorphisms which can be selected by the designer. Exponential stability in constrained and unconstrained position states is achieved, in the certain case. For the uncertain case, the controller is augmented through sliding modes guaranteeing finite-time convergence towards the manifold and keeping the exponential convergence within the manifold dynamics. The approach is validated through experiments in an actual 2 DOF lightweight robot manipulator. • A diffeomorphism-based control law for MIMO constrained nonlinear systems. • The explicit controller ensures (local) exponential convergence of the all states. • A robustification of the controller to deal with uncertainties via sliding modes. [ABSTRACT FROM AUTHOR]

Behavioral interventions (such as those developed to increase physical activity, achieve smoking cessation, or weight loss) can be represented as dynamic process systems incorporating a multitude of factors, ranging from cognitive (internal) to environmental (external) influences. This facilitates the application of system identification and control engineering methods to address questions such as: what drives individuals to improve health behaviors (such as engaging in physical activity)? In this paper, the goal is to efficiently estimate personalized, dynamic models which in turn will lead to control systems that can optimize this behavior. This problem is examined in system identification applied to the Just Walk study that aimed to increase walking behavior in sedentary adults. The paper presents a Discrete Simultaneous Perturbation Stochastic Approximation (DSPSA)-based modeling of the Goal Attainment construct estimated using AutoRegressive with eXogenous inputs (ARX) models. Feature selection of participants and ARX order selection is achieved through the DSPSA algorithm, which efficiently handles computationally expensive calculations. DSPSA can search over large sets of features as well as regressor structures in an informed, principled manner to model behavioral data within reasonable computational time. DSPSA estimation highlights the large individual variability in motivating factors among participants in Just Walk , thus emphasizing the importance of a personalized approach for optimized behavioral interventions. [Display omitted] This paper demonstrates a comprehensive data-driven methodology for modeling physical activity through the synergism of behavior change theory, process modeling, and control. • Increasing physical activity in individuals can be described as a process system. • Behavioral science, process modeling, and system identification can be applied. • Benefits are obtained from novel experiment design and model validation procedures. • Stochastic search via DSPSA enables determining the best features in limited time. • Estimated models can contribute to personalized "just in time" adaptive interventions. [ABSTRACT FROM AUTHOR]

With the increasing complexity of the power grid, the precision of the thermal power units' execution of automatic generation control (AGC) commands is gradually increasing the impact on the online regulation of the power grid. The deviation between the actual output of thermal power units and the AGC command of the grid will not only affect the consumption of new energy output, but also endanger the safe operation of the grid. This paper introduces "deep learning" technology to solve the problem. Firstly, an AGC command execution effect identification and confidence evaluation algorithm (ACEEI-CEA) is proposed. The algorithm builds a neural network model to accurately predict the unit output and the confidence evaluation of the prediction results. Next, a high-dimensional input preprocessing strategy based on variational autoencoder (VAE) is proposed to reduce the dimensionality of the model input attributes, improving the convergence and accuracy of the model. Finally, an AGC optimal command fast inversion solution method (AOCFISM) is designed. This method transforms the unit output deviation problem into an objective optimisation problem. And improve the efficiency of solving the optimal AGC command value by constraining the unit command value. The calculation results show that the error of the prediction results of the model proposed in this paper is 5% lower than that of the traditional neural network. The difference between the output value of the optimal AGC command and the expected output value obtained is less than 0.5 MV, which can support AGC online decision-making.© 2017 Elsevier Inc. All rights reserved. • An AGC command execution effect identification and confidence evaluation algorithm (ACEEI-CEA) is proposed. • Results show the ACEEI-CEA performs well in command prediction compared to the state-of-the-art methods. • ACEEI-CEA achieves confidence assessment of predicted values. • The pre-processing strategy based on variational autoencoder shortens the training time of the model. • The AGC optimal command fast inversion solution method (AOCFISM) can obtain the AGC optimal command value. [ABSTRACT FROM AUTHOR]

This paper aims to provide a review of the conceptual design and theoretical framework of the main control schemes proposed in the literature for unmanned underwater vehicles (UUVs). Additionally, the objective of the paper is not only to present an overview of the recent control architectures validated on UUVs but also to give detailed experimental-based comparative studies of the proposed control schemes. To this end, the main control schemes, including proportional–integral–derivative (PID) based, sliding mode control (SMC) based, adaptive based, observation-based, model predictive control (MPC) based, combined control techniques, are revisited in order to consolidate the principal efforts made in the last two decades by the automatic control community in the field. Besides implementing some key tracking control schemes from the classification mentioned above on Leonard UUV , several real-time experimental scenarios are tested, under different operating conditions, to evaluate and compare the efficiency of the selected tracking control schemes. Furthermore, we point out potential investigation gaps and future research trends at the end of this survey. [ABSTRACT FROM AUTHOR]

Drones or unmanned aerial vehicles (UAVs) are increasingly deployed for disaster relief, from aiding in drug or supply delivery to search and rescue victims. For a large-scale mission, the more drones are used, the more difficult they are to control and manage in a timely manner. Automated control is one of the central techniques used in knowledge-based drone management systems to enhance the quality and efficiency of the disaster relief mission. Although many drone management systems have been developed, automatic control of these systems remains a difficult problem. Many approaches have "human in the loop" that trades the degree of autonomy with the efficiency and perhaps accuracy. Our goal is not to fully automate the system but to reduce user interactions where intelligible (e.g., selecting appropriate drones/actions based on prioritized needs) by means of significant advances in control mechanisms. This paper presents a knowledge-based framework for intelligent drone management by explicitly representing control knowledge for use in selecting appropriate task-specific actions. The paper describes how control decisions are derived using domain knowledge relevant to drones and disaster relief. By employing a blackboard control architecture, our framework provides adaptability for dynamic control behaviors and flexibility to handle unanticipated situations during the drone mission activities. The paper illustrates the use of the framework on specific scenarios. [ABSTRACT FROM AUTHOR]

• A real-time Autonomous Emergency Braking system (RTAEB) is proposed considering accuracy and timeliness. • The system helps avoid collisions and enlarged the final headway distance. • 1.5 s TTC and −7.5 m/s2 deceleration thresholds could achieve the best collision avoidance effect. • Sudden brake and small car-following distance are more inclined to trigger the RTAEB. • Drivers' collision avoidance capability under the current situation is very important. Introduction: To assist drivers in avoiding rear-end collisions, many early warning systems have been developed up to date. Autonomous braking technology is also used as the last defense to ensure driver's safety. Method: By taking the accuracy and timeliness of automatic system control into account, this paper proposes a rear-end Real-Time Autonomous Emergency Braking (RTAEB) system. The system inserts brake intervention based on drivers' real-time conflict identification and collision avoidance performance. A driving simulator-based experiment under different traffic conditions and deceleration scenarios were conducted to test the different thresholds to trigger intervention and the intervention outcomes. The system effectiveness is verified by four evaluation indexes, including collision avoidance rate, accuracy rate, sensitivity rate, and precision rate. Results: The results showed that the system could help avoid all collision events successfully and enlarge the final headway distance, and a TTC threshold of 1.5 s and a maximum deceleration threshold of −7.5 m/s2 could achieve the best collision avoidance effect. The paper demonstrates the situations that are more inclined to trigger the RTAEB (i.e., a sudden brake of the leading vehicle and a small car-following distance). Moreover, the study shows that driver characteristics (i.e., gender and profession) have no significant association with system trigger. Practical Applications: The study suggests that development of collision avoidance systems design should pay attention to both the real-time traffic situation and drivers' collision avoidance capability under the present situation. [ABSTRACT FROM AUTHOR]

This paper presents a critical review of tuning methodologies and controller structures applied to the Cholette bioreactor. This (bio)reactor has been studied extensively by the automatic control community from the point of view of controller structures and tuning methodologies, from controllers with a single structure to nonlinear controllers, and from the synthesis method to the frequency response. Therefore, new trends and opportunities of study have been detected with regard to their operating points, controller structures and tuning methodologies which could be addressed for this system. • The study system is the Cholette's bioreactor which can be represented by different models. • Critical review of control structures and tuning methodologies. • Areas of opportunity for new developments in automatic control of this study system. [ABSTRACT FROM AUTHOR]

Large adjusting camera is an important facility of modern power grid, its own operation must be stable enough, but manual can not control it comprehensively, so it needs to use incremental PID control algorithm to carry out automatic control. This paper will study the application of incremental PID control algorithm in large tuning camera, mainly introduces the basic concept of PID control algorithm, and compares the incremental PID control algorithm with the position PID control algorithm, demonstrates the advantages of the incremental algorithm, and then puts forward its application method, analyzes the application effect of the incremental algorithm. Through research, the incremental PID algorithm has advantages and has more outstanding application value in the control of large adjusting camera, which can better keep the large adjusting camera in stable operation state. [ABSTRACT FROM AUTHOR]

The analysis of convergence of extended state observers (ESOs) requires the total disturbance to be differentiable. However, this requirement is not satisfied in many control engineering practices. In this paper, we attempt to analyze the convergence of ESOs for nonlinear systems with non-differentiable uncertainties. A decomposition method is first presented to divide the non-differentiable total disturbance into a differentiable signal and a bounded but non-differentiable signal. Based on this decomposition, we give out the convergence of both nonlinear and linear ESOs (NLESO/LESO), low- and high-power ESOs (LPESO/HPESO), and fixed-time ESO (FxESO). We also derive the explicit formulas for the estimation errors of these ESOs. Simulations and experiments demonstrate the correctness of the analysis results. • The perturbation of ESO is established based on the proposed decomposition method. • The convergence of typical ESOs is analyzed when disturbance is non-differentiable. • Simulation and experimental testing of ESOs' convergence performance are carried out. [ABSTRACT FROM AUTHOR]

Hydraulic excavators, widely employed in harsh environments, have garnered increased attention in recent years from manufacturers and researchers for automatic operation, particularly through vision-based manipulator pose measurement. This paper utilized a mixed review method to explore the vision-based measurements as well as other applications. A quantitative analysis of computer vision (CV) applications on excavators was performed through a literature search, leading to the classification of these applications into three categories based on keywords. Subsequently, a comprehensive literature review identified four key requirements and challenges for vision-based measurement in the context of automatic control, followed by a discussion on the future prospects of marker-based and deep learning-based manipulator pose measurement for automatic control. This paper offers an in-depth examination of CV applications in excavators, emphasizing the pertinent challenges and forthcoming trends in vision-based manipulator pose measurement for automatic control. • Investigate the applications of computer vision on excavators • Review the literature in 3 aspects: excavator detection and tracking; action recognition; manipulator pose estimation. • Propose requirements and challenges of vision-based measurement approaches from the perspective of automatic control. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of integrating optimal pressure management and self-cleaning controls in dynamically adaptive water distribution networks. We review existing single-objective valve placement and control problems for minimizing average zone pressure (AZP) and maximizing self-cleaning capacity (SCC). Since AZP and SCC are conflicting objectives, we formulate a bi-objective design-for-control problem where locations and operational settings of pressure control and automatic flushing valves are jointly optimized. We approximate Pareto fronts using the weighted sum scalarization method, which uses a previously developed convex heuristic to solve the sequence of parametrized single-objective problems. The resulting Pareto fronts suggest that significant improvements in SCC can be achieved for minimal trade-offs in AZP performance. Moreover, we demonstrate that a hierarchical design strategy is capable of yielding good quality solutions to both objectives. This hierarchical design considers pressure control valves first placed for the primary AZP objective, followed by automatic flushing valves placed to augment SCC conditions. In addition, we investigate an adaptive control scheme for dynamically transitioning between AZP and SCC controls. We demonstrate these control challenges on case networks with both interconnected and branched topology. [ABSTRACT FROM AUTHOR]

This article focuses on extending, disseminating and interpreting the findings of an IEEE Control Systems Society working group looking at the role of control theory and engineering in solving some of the many current and future societal challenges. The findings are interpreted in a manner designed to give focus and direction to both future education and research work in the general control theory and engineering arena, interpreted in the broadest sense. The paper is intended to promote discussion in the community and also provide a useful starting point for colleagues wishing to re-imagine the design and delivery of control-related topics in our education systems, especially at the tertiary level and beyond. [ABSTRACT FROM AUTHOR]

High occupancy pattern mining has been recently studied as an improved method for frequent pattern mining. It considers the proportion of each pattern in the transactions where the pattern occurred. The results of high occupancy pattern mining can be employed for automated control systems in order to make decisions. Meanwhile, the features of the databases have changed, because information technology has advanced. In real-world databases, new transactions are inserted in real time. However, the state-of-the-art approach to high occupancy pattern mining cannot handle incremental databases. Moreover, the existing method also requires a large amount of memory space, because it adopted a BFS-based search in order to find patterns. In this paper, we propose an approach, which is called HOMI (High Occupancy pattern Mining on Incremental databases), that uses a DFS-based search in order to detect patterns, and it mines high occupancy patterns on incremental databases. The performance analysis for both real and synthetic datasets indicates that HOMI has better performance than the state-of-the-art approaches and related algorithms. • Occupancy Pattern Mining to Analyze Incremental Dynamic Data in Intelligent Systems. • An Efficient Algorithm to Mine High Occupancy Patterns Using an Improved List Structure. [ABSTRACT FROM AUTHOR]

The scope of this paper is the analysis of open-loop and L 2 / D controlled closed-loop behavior of the Cholette's bioreactor under different operating conditions. The open-loop behavior is studied by means of the steady-state multiplicity areas formed by substrate feed concentration and substrate concentration in the output using the nonlinear model of the bioreactor. The stability of each steady-state inside areas is determined by a polynomial representation. The nonlinear dynamic and steady-state multiplicity areas of the Cholette's bioreactor are shown to depend on the operating conditions and concentration required. The closed-loop behavior is studied by the tuning of a set of Pareto-optimal L 2 / D linear controllers at a stable operating point. Numerical simulations and phase portraits are used to evaluate the control system. This paper could be a guide for theoretical and practical applications in the fields of biotechnology and automatic control. • The Cholette's bioreactor was evaluated under different operating conditions. • The open-loop behavior is studied by means of the steady-state multiplicity areas. • The stability of each steady-state is determined by a polynomial representation. • A set of Pareto-optimal L 2 ∕ D linear controllers was tuned. • This paper could be a guide for theoretical and practical applications. [ABSTRACT FROM AUTHOR]

• A commuting-pattern-oriented representation of random driving conditions is developed. • Integrated stochastic optimization of powertrain design and control is engineered. • Machine-learning solvers for stochastic optimization are developed. • The stochastic optimization improves the expected energy economy by up to 12%. • Topology modification to EV powertrains improves energy economy by over 17%. The performance of an electric vehicle (EV) powertrain is greatly influenced by the design scheme, control strategy, and driving conditions. Owing to uncertainties in driving conditions and the mutual correlation between design scheme and control strategy, identifying the optimal topology and component parameters of EV powertrains for improving the energy efficiency of EVs remains a challenging task. Oriented to the commuting patterns of individuals, this paper proposes a stochastic optimization method for identifying the superior powertrain design that optimizes the expected energy economy of the EV powertrain and reduces the energy economy variability in random driving conditions. This paper also introduces a machine-learning solution to the complex mutual restrictions among design variables, control strategies, and driving conditions, which determines the feasible design space and powertrain performance. Suggested by massive validations, the stochastic optimization method reduces the expectation and deviation of the energy consumption of EV powertrains up to 12.0% and 5.6%, compared with deterministic optimization methods. Additionally, by comparative evaluation among stochastic optimal design results of different topologies, contributions of topology modifications to the energy efficiency of EV powertrains are clarified, and the superior powertrain topology is identified, which improves 17.1% expected energy economy compared with the current popular topology. [ABSTRACT FROM AUTHOR]

• A special three-dimensional attention mechanism module is constructed that comprehensively explores the high-dimensional features of nodules through the attention-guided method (PSV-AM) of plane texture, slice volume, and slice order; • A U-Net-like structure with a 3D cuboid attention module is proposed for the lung nodule detection task. Considering the heterogeneous structure of nodules comprehensively, the extraction features of nodule detection are realized; • A deep neural network based on transfer residual block is introduced as a prototype to achieve false-positive nodule reduction under the supervision of the attention mechanism module; • Comparisons are made with other state-of-the-art methods on the recognized LUNA16 dataset. After training, the PSV-AM detection model proposed in this paper achieves a training accuracy of 96.15%, sensitivity of 97.61% on the test set, and average false-positive detection rate (FPDR) value of 0.893 in the reducing branch. False-positive diagnoses often occur during pulmonary nodule detection, which directly affects the prognosis of patients. The development of intelligent computing and the wide application of deep learning technology have brought about the intelligent detection of lung nodules, which has greatly improved the detection efficiency and detection accuracy. This paper explores a convolutional neural network filled with a 3D cuboid attention module for lung nodule detection comprehensively encompassing nodules in the cross, coronal, and sagittal planes of CT sections. First, 3D cuboid attention module extracts image features in order from the plane, space, and volume directions of CT slices (PSV-AM). Second, the model adopts a structure named UPSV-Net of 9 modules to expose the lesion area as much as possible. The PSV-AM is embedded into the network to guide it in extracting the structural features of nodules. At the same time, the features extracted from the shallow and deep layers are associatively concatenated. Thirdly, on the basis of the convolution module shared from the detection branch, deepening the convolution path to form the true and false nodule classification network, which is committed to reducing the false positive rate. The model study was validated on the 2016 Lung Nodule Analysis challenge dataset. On the test bench, the detection accuracy is 96.15%, and the sensitivity is 97.6%. Subsequently, the sensitivity to the true and false nodules reached 97.61% and the average false-positive detection rate (FPDR) was 0.893, which was comparable to the diagnosis results of clinicians. [ABSTRACT FROM AUTHOR]

This paper proposes a nonlinear predictive generalized minimum variance (NPGMV) control scheme for automatic control of the managed pressure drilling (MPD) systems in the presence of disturbances. Since the exact model of the system is not usually available in practice, the hydraulic flow model of MPD is described by an autoregressive second-order Volterra model. The conventional least-squares method is applied to input–output data, thereby identifying the Volterra model. Bottom-hole pressure regulation and kick handling are achieved through the control scheme. To deal with a reservoir kick, the proposed method switches to flow control mode automatically, and prevents the reservoir fluid influx into the well surface. The proposed method also has the capability to keep the bottom-hole pressure above the reservoir pressure during a formidable scenario such as pipe connection. In addition, the robustness of the controller in the face of heave disturbance and uncertainty is investigated. To show the effectiveness of the proposed method, the comparison study of several scenarios with a switching PI controller is provided and demonstrates the NPGMV control outperforms other approaches with respect to steady-state performance. • A nonlinear predictive generalized minimum variance (NPGMV) control is proposed for managed pressure drilling (MPD) systems in the presence of disturbances. • Representing the hydraulic flow model of the MPD system by exploiting an autoregressive second-order Volterra series model. • To deal with a reservoir kick, a switching control solution is provided. • Robustness of the proposed control scheme is evaluated in face of heave disturbance and model uncertainty. • Using the conventional least-squares method to identify an inputoutput model without the need for having an exact model of the system. [ABSTRACT FROM AUTHOR]

Broadband acoustic absorbers have extensive applications in noise control engineering and room acoustics. In recent years, acoustic metamaterials have provided an avenue for compact low-frequency and broadband absorbers but the complexity of their configurations is commonly increasing with the larger number of component resonators. In this paper, we propose an easy and effective design concept for broadband two-resonator acoustic absorbers by conceptually analyzing the absorption properties based on basic resonance qualities. Then, proof-of-concept structures validate the presented design concept with remarkable broadband absorption performances and simple structure configurations. One of the resonators has a low quality factor (Q) and the other resonator features a high quality factor. The low- Q resonator contributes to the broadband absorption performance while the high- Q resonator leads to high-efficiency absorption as well as a sharp absorption curve at low frequencies. We practically achieve the low- Q resonator by combining a perforated absorber with a porous material (foam) and a straight cavity. The high- Q resonator is realized with another perforated absorber with a foam and a curled cavity. The combination of the two resonators can achieve high-efficiency sound absorption in the range of 300–6400 Hz with a structural thickness of 100 mm. This work presents a simple and effective design approach using only a low- Q resonator and a high- Q resonator to achieve a thin and efficient broadband acoustic absorber, which would have broad application prospects in the field of low-frequency and broadband absorption. [Display omitted] • The two-resonator design concept facilitates simple broadband acoustic absorbers. • Modulation of quality factors simplifies design processes of coupled absorbers. • A thin two-resonator absorber achieves high-efficiency absorption in ultrabroad band. [ABSTRACT FROM AUTHOR]

Velocity control proves to be an effective and a more easily implementable actuation than boundary and distributed actuations for hyperbolic distributed parameter systems. However, the design of velocity control for these systems, following the late lumping approach, i.e., using the partial differential equations model, poses a challenging problem in control engineering. Noticeably, the velocity controller faces a control singularity issue, resulting in a loss of controllability that renders the controller impractical. In this paper, we demonstrate that the zeroing dynamics method is a viable alternative design approach for velocity control of hyperbolic distributed parameter systems following the late lumping approach. Thus, employing the partial differential equations model, a velocity state feedback forcing output tracking is developed based on the zeroing dynamic method. Furthermore, to address the control singularity problem, the zeroing gradient method is combined with the zeroing method to design a state feedback that achieves output tracking even when a singularity occurs. The tracking error convergence is demonstrated for both developed state feedbacks. The effectiveness of these design approaches is clearly demonstrated in the case of a steam-jacketed heat exchanger and a non-isothermal plug flow reactor. • The zeroing dynamics methods are applied for velocity control for hyperbolic systems. • A stability analysis is provided for the developed controllers. • The use of ZGD controller proves effective in overcoThe zeroing-gradient dynamics controller effectively handles control singularities. • The controller effectiveness is shown in simulations, demonstrated on two processes. [ABSTRACT FROM AUTHOR]

Abstract The mathematical model of heating substation, which is an important part of district heating system, is presented in this paper. The main components of district heating substation system, from the automatic control perspective, that are observed in this paper are heat exchanger and three-way valve. Presented mathematical model is developed so it could operate in real-time, since the crucial aspect of development, deployment and testing of new control techniques and optimization algorithms is their accurate validation on the real system. However, the accessibility to a real system is often not possible. Corresponding to this, an alternative is the existence of the correct mathematical model of a process, with the ability to operate in real-time. First, the presented mathematical model of district heating substation is decomposed to the simpler models of its main elements, in order to provide the analysis of the various parameters on the model performance through numerical simulation. Second, a real-time model is developed in LabVIEW software package and deployed on the embedded controller with a real-time processor as a three-layer application which enables data acquisition and communication with a programmable controller. [ABSTRACT FROM AUTHOR]

Highlights • An adaptive windows based algorithm to localize and classify PCG heart sounds. • Introduction of a peak based clustering technique to handle varying beat duration. • Effective extraction of cycle/period from clean patches of noisy signals. • Extension of cycle/period to complete signal. • A novel memory-based classifier to guard against varying heart rate. Abstract Automated analysis of heart sound recordings (phonocardiograms or PCG) for classification of heart beats in noisy environments is a challenging problem. The major challenges are introduced due to inter- and intra-beat variation, spectral overlap of heart and lung sounds and noise picked from environment. This paper presents a robust algorithm for heart beat localization and classification using variable length window and peak amplitude threshold variation. The technique presented in this paper first attempts to find peaks which correspond to the fundamental heart sound cycle (FHSC) i.e. S 1, S 2, S 1, ... or fundamental heart sound period (FHSP) i.e. S 1, S 1, .... If FHSC/FHSP is found, an attempt is made to extend it to the whole signal. Once extended, the signal is directly labeled using systole and diastole durations. During classification, history of labels is used to counter the heart rate variations. The proposed technique is evaluated on publicly available Pascal Heart Sound Challenge dataset and reports smaller average error for localization and has high sensitivity and accuracy for heart beat classification. [ABSTRACT FROM AUTHOR]

Abstract Programmable hydraulic control (PHC) technology utilized in construction machinery is superior to the conventional electronic-hydraulic control technology. This paper aims to briefly survey the recent results and upcoming trends in this field. First, major problems encountered using the widespread mobile hydraulic system, including insufficient compatibility, low energy efficiency and poor controllability, are demonstrated. The key element of PHC technology contains independent actuators, integrated sensors and intelligent software control, which transfers functionality from hardware to software. A survey of recent advancements for the three aspects is summarized in detail. Next, several industrial products applied in construction machinery are introduced. Finally, the paper focuses on the challenges facing the PHC technology, which prevented it from becoming widely spread in the market, and the countermeasures are discussed. Graphical abstract Unlabelled Image Highlights • Major problems of the widespread mobile hydraulic systems are demonstrated. • Advancements of the programmable hydraulic control technology are introduced. • The independent actuator, integrated sensor and intelligent control are contained. • The performance depends on the software rather than hardware with this technology. • Challenges using the technology and their countermeasures are presented. [ABSTRACT FROM AUTHOR]

Abstract This paper presents an overview of the design of the ITER blanket remote handling system (BRHS), an indispensable component for ITER operation because the high irradiation environment will not allow humans to perform in-vessel maintenance activities. Instead, a large power manipulator will be deployed in the equatorial plane of the vacuum vessel and travel in the toroidal direction along an articulated rail, which will enable the heavy components to be handled with high positioning accuracy. The BRHS also includes a rail deployment system and various tooling and control systems in addition to its main components. This paper summarizes the design status of the in-vessel manipulator and its support system in the run-up to manufacture. [ABSTRACT FROM AUTHOR]

Abstract This paper investigates the fixed-time attitude tracking control problem for rigid spacecraft based on rotation matrix. Two anti-unwinding control schemes are developed such that the desired attitude can be tracked with bounded convergence time regardless of the initial conditions. The first controller is established on basis of a novel sliding mode surface without considering the external disturbance. In addition, the explicit description of the settling time can be provided under this controller. By revising the sliding mode surface and utilizing an adaptive law, the fixed-time stability can still be achieved under the second controller even in the presence of external disturbance. Theoretical analysis and numerical simulations are presented to demonstrate the validity of the proposed controllers. Highlights • Two attitude tracking controllers are designed for spacecraft. • Rotation matrix is used to construct the controllers. • Controllers in this paper are fixed-time stable. • Adaptive law is applied to deal with the external disturbances. [ABSTRACT FROM AUTHOR]

Path planning and positioning are the key problems of the navigation for robot. Traditional global positioning and navigation systems, such as the Global Positioning System (GPS) and BeiDou Navigation Satellite System (BDS), can not exactly collect information nearby, which makes it difficult to complete accurate positioning and navigation indoor. SLAM algorithm allows the robot to sense the environment through its own sensors, build a map of the environment, and calibrate its position, so that it can move in an unknown environment. The SLAM algorithm based on laser has stable ranging performance in small static scenes, which is less affected by the light intensity. Therefore, in order to achieve precise positioning and navigation, the laser SLAM algorithm is applied to the sweeping robot indoor in this paper. The tests show that in the ROS operating system, the sweeping robot achieves precise positioning and plans the optimal path based on the laser SLAM algorithm, which better realizes the automatic control for the robot. [ABSTRACT FROM AUTHOR]

The tunable approximated explicit model predictive control (MPC) comes with the benefits of real-time tunability without the necessity of solving the optimization problem online. This paper provides a novel self-tunable control policy that does not require any interventions of the control engineer during operation in order to retune the controller subject to the changed working conditions. Based on the current operating conditions, the autonomous tuning parameter scales the control input using linear interpolation between the boundary optimal control actions. The adjustment of the tuning parameter depends on the current reference value, which makes this strategy suitable for reference tracking problems. Furthermore, a novel technique for scaling the tuning parameter is proposed. This extension provides to exploit different ranges of the tuning parameter assigned to specified operating conditions. The self-tunable explicit MPC was implemented on a laboratory heat exchanger with nonlinear and asymmetric behavior. The asymmetric behavior of the plant was compensated by tuning the controller's aggressiveness, as the negative or positive sign of reference change was considered in the tuning procedure. The designed self-tunable controller improved control performance by decreasing sum-of-squared control error, maximal overshoots/undershoots, and settling time compared to the conventional control strategy based on a single (non-tunable) controller. • Self-tunable control technique does not need any intervention during control. • Tuning based on operating conditions compensates for the nonlinear plant behavior. • Implementation of the proposed tuning method on a laboratory heat exchanger. • Control performance improved compared to utilizing explicit MPC with constant setup. [ABSTRACT FROM AUTHOR]

The notion of Internet of Things (IoT), as well as related topics such as Cyber-Physical Systems, Industrie 4.0 and Smart Manufacturing are currently attracting a lot of attention within the process and manufacturing industries. Clearly, IoT offers many potential applications for automation, ranging from engineering installation of new plants to production management and more intelligent maintenance schemes including novel sensor technologies. The focus of this paper is, however, on the control and operations. Most likely IoT leads to new system architectures where open standards play a significant role. Through better connectivity, information will be much more easily available, which could result in that previously isolated functions will become more closely integrated. Here modeling at the right level of fidelity will be absolutely key. It can be expected that the importance of optimization will increase and this paper discusses some aspects related to the opportunities, challenges and changes triggered by IoT. [ABSTRACT FROM AUTHOR]

Diabetes mellitus, commonly known as diabetes, is a disease that affects a vast majority of people globally. Diabetes cannot be cured; it can only be kept under control. In this paper, diabetes is diagnosed by the analysis of Heart Rate Variability (HRV) signals obtained from ECG signals. We employed deep learning networks of Convolutional neural network (CNN) and CNN-LSTM (LSTM = Long short term memory) combination to automatically detect the abnormality. Unlike the conventional analysis methods so far followed, deep learning techniques do not require any feature extraction. We initially performed classification splitting the database into separate training and testing data. The maximum accuracy obtained for test data is 90.9% using CNN-LSTM. Using 5 fold cross-validation, CNN gave an accuracy of 93.6% while CNN-LSTM combination gave the maximum accuracy of 95.1%. As per our best knowledge, this is the first paper in which deep learning techniques are employed in distinguishing diabetes and normal HRV. The accuracy obtained using cross-validation is the maximum value achieved so far for the the automated detection of diabetes using HRV. [ABSTRACT FROM AUTHOR]

In this paper, the potential improvement in performance of an NCS (Networked Control System) subject to variable delays controlled by means of an adaptive system is analyzed. An adaptive Smith Predictor has been used to compensate the effects of varying delays measured on the network. The NCS, closed over the Internet, has been implemented using a platform called NCS-CMUF, composed of local and remote stations. The plant used to carry out the experimental tests is consisted of an optical oven in which the luminosity loop is the main controlled variable whose time constant lies in the range of milliseconds. This paper highlights the importance of synchronization between the clocks of the local and remote stations to perform consistent measurements of network delays, in order to provide an active compensation of the effects of delays. The results show the effectiveness of adaptive Smith Predictor in coping with different delays and the performance improvements achieved with the proposed scheme. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel and accessible approach to time-series data validation: the Near-Real Time Autonomous Quality Control (NRAQC) system. The design, implementation, and impacts of this software are explored in detail within this paper. This software system, created in close conference with environmental scientists, leverages microservice design patterns employed for high volume web applications to develop a contemporary solution to the problem of data quality control with streaming sensor data. Through a comparative analysis between NRAQC and the GCE Toolbox, we argue that the web based deployment of QC software enhances accessibility to crucial tools required to make a robust and useful data product from raw measurements. Additionally, a key innovation of the NRAQC platform is its positive impact on modern data management practices and quality data dissemination. [ABSTRACT FROM AUTHOR]

This paper presents historical remarks on key projects and papers that led to the development of a theory of event diagnosis for discrete event systems modeled by finite-state automata or Petri nets in the 1990s. The goal in event diagnosis is to develop algorithmic procedures for deducing the occurrence of unobservable events, based on a formal model of the system and on-line observations of its behavior. It also presents historical remarks on the early works on the property of opacity, which occurred about ten years later. Opacity can be seen as a strong version of lack of diagnosability and it has been used to capture security and privacy requirements. Finally, diagnosability is connected with the property of observability that arises in supervisory control. This paper is part of set of papers that review the emergence of discrete event systems as an area of research in control engineering. [ABSTRACT FROM AUTHOR]

In control engineering, control processes are regulated to prevent transient shocks to test rigs. As an important part of the control loop, the reference may be continuous and bounded to avoid the transient impulse generated by the controller. Therefore, a high-order bounded reference (HOBR) is proposed for the trajectory generation. This paper studies the smooth control process and constrained reference generation for large testing platforms. Utilizing the Hermite interpolation technique, a planning method for the HOBR is proposed to ensure a smooth and bounder generated trajectory, which derives an analytic function for the desired reference. In addition, an observer-based state feedback controller is presented and applied to enable the system output to converge asymptotically to the desired reference. The theoretical analysis shows that the HOBR satisfies continuous constraints and boundary constraints at the same time. Finally, the effectiveness of the proposed HOBR in two constraints is demonstrated by both simulations and experiments. • The proposed HOBR reference and its derivatives are continuous and bounded. • The proposed HOBR technique is robust to various periodical reference scenarios. • The proposed technique generates analytic solutions in polynomial time complexity. • The proposed controller utilizes the reference information to achieve convergence. [ABSTRACT FROM AUTHOR]

External fiber-reinforced polymer overlays have become the most popular method of building structures' strengthening on a global scale. Since no widely used and reliable methods for diagnosing the connection of overlays with the strengthened surface have been developed so far, developing such a method with the entire system, enabling easy and reliable use, has become a vital need of the construction sector. The paper presents an original infrared-based diagnostic module placed on a drone, enabling docking under the structure and conducting precise, uninterrupted diagnostics. The article describes the solutions and configuration of the automatic drone control system and the diagnostic module in all phases of its operation. The embedded measurement and control system has a multi-level structure distributed between the onboard automation system and the ground control station. Laboratory and field tests have shown that the road viaduct solution accurately reproduces joint damage, enabling the assessment of the structure's safety. • A thermography-based device for diagnosing the quality of the bond of FRP strengthening with a concrete surface was constructed. • The diagnostic module was placed on a flying platform in order to perform tests on hard-to-reach structures. • The proposed unmanned multi-rotor platform is equipped with a system that allows docking under the structure for testing and precise graphical data collection. • By creating a unique docking platform, automated processes for flight, approach to structure, docking, measurement and departure have been developed. • Laboratory and field tests show that the road viaduct solution accurately maps bond damage, enabling the estimation of load capacity of strengthened structural elements. [ABSTRACT FROM AUTHOR]

Achieving precise attitude control in satellites equipped with two reaction wheels remains a significant challenge. During the last decade, model predictive control has emerged as a promising technique for satellite attitude control. However, this paper takes a step by introducing the novel concept of linear time-varying fractional-order model predictive control specifically tailored for satellites with dual reaction wheels, enabling them to achieve desired orientations more effectively. This study begins by elucidating the nonlinear dynamics and kinematic equations governing the satellite's behavior. To facilitate control design, the satellite linear time-varying model is derived by linearizing the nonlinear equations around the operating point. Leveraging this linearized representation, a linear time-varying fractional-order model predictive control method is designed, leveraging the benefits of fractional-order cost functions. Finally, the simulation results show the remarkable performance of the linear time-varying fractional-order model predictive control approach, particularly when confronted with input constraints. Notably, the proposed method outperforms the traditional linear time-varying model predictive control method by achieving an 80% improvement in tracking speed, while simultaneously reducing the applied torque by 67%. Additionally, the proposed approach decreases the mean absolute error of the satellite attitude angles by over 80% compared to the conventional linear time-varying model predictive control method. This research not only addresses the pressing challenge of attitude control in satellites with dual reaction wheels but also presents a novel and highly effective approach that surpasses existing methods in terms of performance and efficiency. As such, it holds immense potential for the field of satellite attitude control, making it an indispensable read for engineers and researchers working in the domain of aerospace engineering and control systems. [ABSTRACT FROM AUTHOR]

• A model for wind power system with sensor and actuator faults is established. • The distributed resilient multi-event triggering scheme (DRMETS) is derived. • The DRMETS is integrated into the fault-tolerant cooperative controller design. • The DRMETS based fault-tolerant cooperative control strategy is proposed. • It provides stronger reliability and higher wind energy utilization efficiency. In wind energy conversion power system, some sensor and actuator fall into abnormal condition which may lead to lower wind energy utilization rate and poor system performance or even system collapse. Meanwhile, with the increasing complexity of wind energy conversion power system, more device nodes compete for limited network communication resources, there are time-delays and packets dropout in data transmission, and these may reduce the efficiency of wind energy conversion. According to above consideration, the multi-event triggering mechanism based distributed resilient fault-tolerant cooperative control scheme for wind energy conversion power system is put forward. A novel model of the wind energy conversion power system is established when the sensor and actuator faults simultaneously. According to Lyapunov stability theory, the sufficient condition for system exponential mean-square stable is obtained. Different from previous fault-tolerant control method, the proposed control strategy considered the wind energy conversion power system with data transmission management under simultaneous stochastic sensor and actuator faults. The simulation demonstrates that the method proposed in this paper is effective and the multi-event triggering mechanism based distributed resilient fault-tolerant controller provides stronger reliability and higher wind energy utilization efficiency. [ABSTRACT FROM AUTHOR]

This paper introduces a systematic approach to design and tune the airflow velocity control system for use during fire situations in road tunnels. The proposed approach is focused on road tunnels with a complex structure; long tunnels with connected ramps (entrances and exits), where the controller design can be challenging and time consuming. Such tunnels usually have many sections where a fire can be localized, and this makes the control task difficult. Our approach is based on a simplified one-dimensional simulation model of a tunnel, which includes all the important factors influencing the airflow dynamics of a tunnel. The proportional–integral (PI) controllers are tuned based on the Skogestad Internal Model Control (SIMC) method, which requires a simple model for the process dynamics. The case study is the airflow velocity control in the Blanka tunnel complex in Prague, Czech Republic, which is the largest city tunnel in Central Europe. The results of the paper show how to improve the control algorithm in real operation and how to use the proposed systematic approach for future tunnels. [ABSTRACT FROM AUTHOR]

Industrial automation and control systems are increasingly deployed using wireless networks in master-slave, star-type configurations that employ a slotted timeline schedule. In this paper, the scheduling of (re)transmissions to meet real-time constraints in the presence of non-uniform interference in such networks is considered. As packet losses often occur in correlated bursts, it is often useful to insert gaps before attempting retransmissions. In this paper, a quantum Earliest Deadline First (EDF) scheduling framework entitled ‘Eligible EDF’ is suggested for assigning (re)transmissions to available timeline slots by the master node. A simple but effective server strategy is introduced to reclaim unused channel utilization and replenish failed slave transmissions, a strategy which prevents cascading failures and naturally introduces retransmission gaps. Analysis and examples illustrate the effectiveness of the proposed method. Specifically, the proposed framework gives a timely throughput of 99.81% of the timely throughput that is optimally achievable using a clairvoyant scheduler. [ABSTRACT FROM AUTHOR]

In this paper, a novel nonlinear control strategy along with its simulation for a quadrotor helicopter is proposed. The normal 6-DOF dynamic model of the quadrotor based on the Newton–Euler formula as well as the model with external uncertain disturbances are established. Considering the underactuated and strongly coupled characteristics of quadrotor helicopter, we design a nonlinear control method by using integral backstepping combined with the sliding mode control (integral BS-SMC) to stabilize the quadrotor attitude and to accomplish the task of trajectory tracking. The designed controllers based on the hierarchical control scheme can be divided into rotational controller and translational controller and their stability are validated by the Lyapunov stability theorem. By means of the proposed controllers, the chattering phenomenon and discontinuousness of control inputs faced by traditional sliding mode control (SMC) can be avoided. The feasibility of the control approach presented in this paper is verified by the simulations under different scenarios. The results show that the nonlinear control method not only has a better tracking performance than others but also has a higher robustness when unknown disturbances occur. [ABSTRACT FROM AUTHOR]

Finite time controllers robust to inertia matrix uncertainty for satellite attitude stabilization control and attitude tracking control are developed in this paper. A three-stage finite sliding mode is constructed to improve system convergence rate. The singularity issue is solved by using the property of Euler Axis when it's paralleled to angular velocity. Based on this finite time sliding mode, finite time controller is developed to ensure the system state could reach the sliding mode in finite time. System inertia matrix uncertainty and disturbance torque is considered in this paper. The control torque constraint is also considered to ensure the norm of control torque does not exceed system upper bound. Finite time stability of the controller is proved and the controller performance is demonstrated by simulation results. [ABSTRACT FROM AUTHOR]

In this paper, some data-based control design options that can be used to accommodate for the presence of uncertainties in continuous-state engineering systems are recalled and discussed. Focus is made on reinforcement learning, stochastic model predictive control and certification via randomized optimization. Some thoughts are also shared regarding the positioning of the control community in a data and AI-dominated period for which some suggestions and risks are highlighted. [ABSTRACT FROM AUTHOR]

This paper deals with the usage of a toolkit Control Design & Simulation in development software LabVIEW for simulation of a two-position controller. This automatic control can be used to regulate regulation processes with high rise time such as temperature processes. Many of these processes can be categorized as second order systems. Main topic of this paper is the description of on-off controllers, their abilities and shortcomings and the way of use of such controllers to regulate processes of a second order. The publication then describes the development software LabVIEW, mainly useful toolbox Control Design & Simulation. This module was then used to design, simulate and optimize two-position controller. [ABSTRACT FROM AUTHOR]

This paper discusses concepts and tools for joint human and cyber-physical-systems analysis and control in the view of increasing the whole system resilience. More precisely, it details new challenges for human reliability based on dissonance control of Cyber-Physical&Human Systems (CPHS) to improve the system's resilience. The proposed framework relates to three main topics: the stability analysis in terms of dissonances, the dissonance identification, and the dissonance control. Dissonance oriented stability analysis in this sense consists in determining any conflicting situations resulting from the human behaviors interacting with Cyber-Physical Systems (CPS). Frames of reference support the assessment of stable or unstable gaps among stability shaping factors and the identification of dissonances. Dissonance control consists in reinforcing the frames of reference by applying reinforcement modes. It aims then at accepting or rejecting the identified dissonances by using supports such as expert judgment, feedback of experience, simulation, learning or cooperation. An example in road transportation illustrates the interest of the proposed framework by studying possible dissonances between car drivers and CPS. As automation spreads out in society by generating close interactions with humans, the ideas of the paper will support the design of new analysis and control tools jointly made by researchers from social and control sciences to study the resilience of the whole CPHS in terms of dissonances. [ABSTRACT FROM AUTHOR]

This paper provides a review of the analysis and modeling of human spatial planning, perception, and learning based on the dynamics of agent-environment interactions. The approach stems from an analysis and modeling framework that was previously conceived using interaction patterns emerging from system-wide interactions as the basic unit of analysis. The paper first discusses the rationals for using patterns in agent-environment interactions as units of organization of behavior, and as functional units of the modeling framework. These concepts are then illustrated through two applications using experimental data from a first-person flight simulator that implements agile obstacle navigation tasks. The first application focuses on the analysis of the formation and evolution of interaction patterns over successive trials, and the use of these patterns as basic elements of the task environment representation, enabling the evaluation of the learning process and assessment of the operator performance. The second application focuses on the analysis of interaction patterns as functional units supporting the modeling of the underlying perceptual guidance and control mechanisms. These examples demonstrate the relevance of dynamics in agent-environment interactions for studying a wide range of functions across the human control hierarchy. [ABSTRACT FROM AUTHOR]

Petrochemical plants require the addition and removal of energy to and from the process and the movement of material to, from, and within the process piping and vessels. These fundamental mass and energy transfer requirements are typically achieved through the use of process utilities, which include electricity, steam, fuel gas, cooling water and compressed air. Utilities are responsible for a significant portion of the operating cost of a plant. Therefore, reduction in the consumption of utilities is a common process optimisation area. The situation is different when it comes to the generation and transportation of these utilities, which are often overlooked with regard to optimisation. In this paper, the potential benefits of utility optimisation are illustrated with particular focus on the generation and transportation areas. The main objectives are reductions in electrical energy consumption and cost and are illustrated for a dual circuit cooling water system. This system is non-linear and also hybrid in the sense that it contains both continuous and discrete input variables, which significantly complicates the design and implementation of control and optimisation solutions. This paper illustrates how the cost and energy consumption of a hybrid system can be reduced through the implementation of hybrid non-linear model predictive control (HNMPC) and economic HNMPC (EHNMPC). The results are compared to that of a base case and an Advanced Regulatory Control (ARC) case, showing that significant additional benefit may be achieved through the implementation of these advanced control and optimisation techniques. The paper further illustrates that additional capital is not necessarily required for the implementation of these techniques. [ABSTRACT FROM AUTHOR]