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Masonry is a building material that has been used in the last 10.000 years and remains competitive today for the building industry. The compressive strength of masonry is used in modern design not only for gravitational and lateral loading, but also for quality control of materials and execution. Given the large variations of geometry of units and joint thickness, materials and building practices, it is not feasible to test all possible combinations. Many researchers tried to provide relations to estimate the compressive strength of masonry from the constit-uents, which remains a challenge. Similarly, modern design codes provide lower bound solutions, which have been demonstrated to be weakly correlated to observed test results in many cases. The present paper adopts soft-computing techniques to address this problem and a dataset with 401 specimens is considered. The obtained results allow to identify the most relevant parameters affecting masonry compressive strength, areas in which more experimental research is needed and expressions providing better estimates when compared to formulas existing in codes or literature., MU - Malayer University(undefined)

How can different organizations in the same building each maintain security of their internal wifi communications? Part of the answer to this is for organizations on opposite sides of a single wall or floor to use distinct radio channels. How many channels may be needed to meet this requirement in a typical modern office building? Mathematically this channel assignment problem is a vertex colouring problem with the organizations as the vertices, joined if somewhere they are adjacent either wall-to-wall or floor-to-ceiling. The study group showed that there is no easy answer to this question, but that bounds can be obtained in particular cases. For instance if each company only occupies one floor of the building, 8 channels suffice. Public graph-colouring algorithms are quick and successful in finding minimal colourings for reasonably complex building layouts.

This paper deals with the distributed fast and finite-time consensus problem of multiagent systems with bounded disturbances in a leader-follower-based frame work. A novel integral sliding mode-based robust finite-time event-triggered control strategy has been proposed. A fast reaching law is also utilized to improve the speed of convergence. The triggering condition is derived based on the defined novel measurement error, suited for systems with model uncertainties/disturbances. The expression for the lower bound for the interexecution time has been derived to ensure that the zeno behavior is avoided. The proposed nonlinear consensus protocol is such that the desired relative state deviation between the agents can be achieved with a directed graph topology. The theoretical results are validated through real-time experimentations done using Pioneer P3-DX as well as FireBird VI robots.

This paper addresses a formation problem for a network of autonomous agents with second-order dynamics and bounded disturbances. Coordination is achieved by having the agents asynchronously upload (download) data to (from) a shared repository, rather than directly exchanging data with other agents. Well-posedness of the closed-loop system is demonstrated by showing that there exists a lower bound for the time interval between two consecutive agent accesses to the repository. Numerical simulations corroborate the theoretical results.

This paper investigates the performance of cloud radio access networks (CRANs) for a downlink multiuser millimeter wave (mmWave) system, where randomly distributed remote radio heads ( ${\rm RRH}$ s) supported by a central baseband unit ( $\mathrm{BBU}$ ) communicate with multiple mobile equipment ( ${\rm ME}$ s). The fronthaul and access link transmissions are implemented with mmWave frequency bands. The ${\rm RRH}$ s and ${\rm ME}$ s are modeled as independent poisson point processes. We characterize the outage probability, average latency, and throughput of this system under essential factors, such as blockages, ${\rm RRH}$ density, and path loss. Two specific ${\rm ME}$ association scenarios are considered: best channel participation (BCP) and nearest neighbor participation (NNP). We derived for both scenarios, closed-form expressions of outage probability in the noise-limited case, and upper and lower bounds of outage probability in the interference-limited case. Our results show blockages and path loss to have a positive effect of decreasing outage probability. Larger antenna arrays are shown to compensate for communication degradation (outage performance and latency) with higher ${\rm RRH}$ deployment, which can be considered a tradeoff between intercluster interference and ${\rm RRH}$ density. Finally, we show that for the ${\rm ME}$ association process, BCP is the most viable for mmWave CRAN systems due to its outperforming NNP.

The cable-stayed bridge with diamond-shaped pylons is one of the most popular bridges because of its obvious advantages such as aesthetical appearance and smaller foundation. However, the diamond-shaped pylons have both inward and outward inclinations, which may result in complicated seismic behavior when subjected to lateral earthquake excitations. To end this, the finite element model of a cable-stayed bridge with diamond concrete pylon is developed firstly. Four limit states and corresponding damage index are defined for each critical section. Finally, the lateral seismic fragility of the components and system of CSB was carried out. Based on the result of probabilistic estimation of lateral seismic responses, the order of the damage probability in all four damage states for each component of bridge is given. The fragility curves of bridge system on the lower bound and upper bound are studied. Moreover, the system fragility of the entire bridge is compared with that of each component.

This paper presents a physical frequency-diverse multimode lens-loaded cavity, designed and used for the purpose of the direction of arrival (DoA) estimation in millimetre-wave frequency bands for 5G and beyond. The multi-mode mechanism is realized using an electrically-large cavity, generating spatio-temporally incoherent radiation masks leveraging the frequency-diversity principle. It has been shown for the first time that by placing a spherical constant dielectric lens (constant-ϵr) in front of the radiating aperture of the cavity, the spatial incoherence of the radiation modes can be enhanced. The lens-loaded cavity requires only a single lens and output port, making the hardware development much simpler and cost-effective compared to conventional DoA estimators where multiple antennas and receivers are classically required. Using the lens-loaded architecture, an increase of up to 6 dB is achieved in the peak gain of the synthesized quasi-random sampling bases from the frequency-diverse cavity. Despite the fact that the practical frequency-diverse cavity uses a limited subset of quasi-orthogonal modes below the upper bound limit of the number of theoretical modes, it is shown that the proposed lens-loaded cavity is capable of accurate DoA estimation. This is achieved thanks to the sufficient orthogonality of the leveraged modes and to the presence of the spherical constant-ϵr lens which increases the signal-to-noise ratio (SNR) of the received signal. Experimental results are shown to verify the proposed approach.

The Worst Multiple Perturbation Load Approach (WMPLA) has been validated by full-scale tests to be an effective and credible determination method for the lower-bound buckling load of stiffened shells. In spite of its high prediction accuracy and robustness, the large computational time is a burden because this method is based on the large-scale optimization of detailed finite element (FE) models. Therefore, a high-fidelity approximate modeling strategy is proposed in this paper, to improve the efficiency of WMPLA and then promote its application for the preliminary design stage of stiffened shells. In the first step, an initial approximate model is established by means of the Representative Volume Element (RVE) equivalent method. In the second step, a modified equivalent model is generated after optimizing the effective stiffness coefficients of the initial equivalent model. In the optimization process, the objective is minimizing the relative error in buckling load between a detailed FE model and the modified approximate model. The constraint condition is controlled by the Modal Assurance Criterion (MAC) coefficients for buckling mode. In particular, the global and local deformation features are simultaneously considered in the MAC coefficients. After the establishment of the high-fidelity approximate model, it is then integrated into WMPLA. By optimization of the positions and amplitudes of the multiple perturbation loads, the minimum buckling load is obtained as the lower-bound value. By comparison against known methods, the lower-bound buckling load predicted by the proposed strategy is quite close to the test result, succeeding in providing a safe design load prediction. The proposed strategy reduces the computational time sharply by 94% compared to the WMPLA based on detailed FE models, indicating high efficiency. Additionally, an illustrative example is shown to highlight the importance of the prediction accuracy of buckling mode on the fidelity of approximate models for WMPLA. Finally, it is concluded that the proposed strategy is a potential and efficient approach to predict lower-bound buckling loads of stiffened shells.

Motivated by reliability/availability concerns in chemical plants, this paper proposes MINLP models to determine the optimal selection of parallel units considering the trade-off between availability and cost. Assuming an underlying serial structure for availability, we consider first a case where the system transitions between available and unavailable states, and second the case with an intermediate state at half capacity. Two non-convex MINLP models maximizing net profit are introduced for the two cases. In addition, a bi-criterion MINLP model is proposed to maximize availability and to minimize cost for the first case. It is shown that the corresponding epsilon-constrained model, where the availability is maximized subject to parametrically varying upper bound of the cost, can be reformulated as a convex MINLP. Availability is also incorporated in the superstructure optimization of process flowsheets. The performances of the proposed models are illustrated with a methanol synthesis and a toluene hydrodealkylation process.

In this brief, a novel scheme to design an antiwindup gain by ensuring local stability for nonlinear parameter varying systems having an input saturation is derived. Antiwindup compensator (AWC) design is provided for a dynamic output feedback controller that meets the desired closed-loop stability and performance specifications in the absence of the input saturation. A linear matrix inequality-based condition by application of Lyapunov theory, a local sector condition, an upper bound on the nonlinearity, and parametric bounds is formulated for the AWC design to ensure asymptotic and $\mathcal {L}_{2}$ stability. In contrast to the conventional approaches for nonlinear systems, the proposed AWC approach accounts for parametric variations, considers computationally simple static antiwindup, is straightforward for implementation, is useful for the existing control system, and can reduce the design conservatism. The proposed AWC design approach is tested for a practical scenario on the dc servo system control under armature nonlinearity, load variations, and control input saturation. Both simulation and experimental results are provided.

The occurrence of traveling shock waves associated to the sudden opening or closing of a valve, known as the hammer effect, is important from the perspective of control design because it imposes an upper bound constraint on the maximum bandwidth achievable for the controlled system. This article addresses the problem of characterizing hydraulic hammer effect of the heating fluid in a solar power tower. The transfer function that relates the increments of the pressure inside the pipe and the fluid velocity is computed, as well as its poles and zeros. In this way, it is possible to obtain finite dimensional linear approximations for this transfer function with a prescribed complexity. As an application, the traveling waves of pressure and velocity of a fluid used for temperature control in a typical solar tower circuit are simulated.

The bearing capacity factor Nσ of a strip footing resting on homogenous rock masses is investigated by a lower-bound approach with the modified Hoek–Brown failure criterion. Three types of admissible stress fields — three stress legs, nine stress legs, and infinite stress legs — are used to calculate the bearing capacity factor. The new analytical expressions are derived, and the results are compared with existing solutions. Agreement shows that the new analytical expressions are effective for evaluating the bearing capacity factor. The influences of parameters in the modified Hoek–Brown failure criterion on the bearing capacity are discussed. Design tables are presented for practical use in engineering.

For the demand side management, the elastic power loads can be scheduled to achieve load balancing and to minimize the fluctuation of the overall load. In a process of power supply, the inelastic power loads can be regarded as a group of parameters. Then the demand response can be utilized to realize the optimal allocation of the elastic power loads. Importantly, the power load balancing can reduce the cost from power generations since no cost is spent on the requirement of frequency control. This paper focuses on the optimal allocation of the elastic load for load balancing. A load balancing problem, with the node and the group power upper bound constraints, is investigated in this paper. Water-filling algorithm is proposed for exactly and efficiently computing the optimal solutions to the power load balancing optimization problem with lower degree polynomial computational complexity. The proposed algorithm can be applied to solve the target optimization problems with large-scale due to utilization of non-derivative water-filling method. To the best of the authors’ knowledge, there is no existing algorithm reported in the open literature that can compute the exact solution to the target problem.

This note develops a discrete-time predictor feedback control scheme for continuous-time multiagent systems with input and communication time delays. With measurement of locally available relative state signals among neighbored agents, the controller is able to achieve consensus for a large variety of linear open-loop agent dynamics, including exponentially unstable systems. Moreover, the new design can deal with arbitrarily large time delays in some special scenarios. The feature of discrete-time and relative state measurement substantially saves implementation cost compared with the existing methods in the literature. Numerical simulation demonstrates the effectiveness of the proposed theoretical design.

This paper investigates the problem of salvo attack guidance law design for multiple missiles against a stationary target considering communication delay. The proposed guidance law requires no information on time-to-go and consists of two guidance stages. More specifically, a simple decentralized midcourse guidance law is proposed to provide desired initial conditions for the latter phase. As for the second guidance phase, all missiles are switched to classical pure proportional navigation guidance law. Considering the fact that the communication delay is inevitable in real applications, the corresponding allowed maximum upper bound of the communication delay among all interceptors is also derived analytically. Nonlinear numerical simulations clearly confirm the effectiveness of the proposed formulation.

A new numerical upper-bound method applicable to three-dimensional stability problems in undrained clay is proposed and applied in the analysis of tunnel face stability. Comparisons with the elasto-plastic finite element method (FEM) reveal that this method could provide sufficiently accurate upper-bound prediction for the tunnel face stability factors. For tunnels with large cover depth, the obtained results greatly improve the existing upper-bound solutions. Their corresponding velocity fields are investigated, which show a similar evolving pattern with those of the FEM. A discussion is made to compare the various advantages of multi-block mechanisms and continuous mechanisms in tunnel face stability.

Rare earth permanent magnets (REPMs) play an essential role in various applications such as renewable energy production, and aerospace and defense related products. Rare earth elements (REEs) such as neodymium and dysprosium are used in REPMs, and the supply of these REEs has experienced volatility. To mitigate this risk, REEs may be recovered from end-of-life (EOL) products such as computer hard disk drives (HDDs). To facilitate REE/REPM recycling, this paper develops an operation and inventory management strategy to explore the profitability 1) under uncertain market supply and 2) with varying component/material values whose demand also faces significant uncertainties. The resulting strategy provides recommendations for the ordering and processing quantities associated with REPM containing products. An upper bound solution on the recovery profit was proposed to assess the performance of the developed strategy. We found that the proposed strategy helps increase the overall profit, and its performance is close to the upper bound. Finally, several scenarios were evaluated to examine how market conditions affect profit. To the best of authors’ knowledge, this research is the first study on REPM recycling that provides a promising strategy to the relevant industry.

Bearing-supported shafts are widely used in various machines. Due to harsh working environments, bearing performance degrades over time. To prevent unexpected bearing failures and accidents, bearing performance degradation assessment becomes an emerging topic in recent years. Bearing performance degradation assessment aims to evaluate the current health condition of a bearing through a bearing health indicator. In the past years, many signal processing and data mining based methods were proposed to construct bearing health indicators. However, the upper and lower bounds of these bearing health indicators were not theoretically calculated and they strongly depended on historical bearing data including normal and failure data. Besides, most health indicators are dimensional, which connotes that these health indicators are prone to be affected by varying operating conditions, such as varying speeds and loads. In this paper, based on the principle of squared envelope analysis, we focus on theoretical investigation of bearing performance degradation assessment in the case of additive Gaussian noises, including distribution establishment of squared envelope, construction of a generalized dimensionless bearing health indicator, and mathematical calculation of the upper and lower bounds of the generalized dimensionless bearing health indicator. Then, analyses of simulated and real bearing run to failure data are used as two case studies to illustrate how the generalized dimensionless health indicator works and demonstrate its effectiveness in bearing performance degradation assessment. Results show that squared envelope follows a noncentral chi-square distribution and the upper and lower bounds of the generalized dimensionless health indicator can be mathematically established. Moreover, the generalized dimensionless health indicator is sensitive to an incipient bearing defect in the process of bearing performance degradation.

The nested array, implemented by nonuniform antenna placement, is an effective approach to achieve $O(N^2)$ degrees of freedom (DOF) with an antenna array of $N$ antennas. Such DOF refers to the number of directions of incoming signals that can be resolved. With the increased number of DOF, an important application of nested array is to nullify interference signals from multiple directions. In this paper, we apply nested array in a massive multiple input multiple output (MIMO) heterogeneous network (HetNet) for interference management. With a nested array, a base station (BS) can nullify a certain number of interference signals based on their directions. Then, a key design issue is how to select the set of interference sources to be nullified at each BS. As the DOF of each BS is used to resolve both desired signals and interference, the number of interference signals that can be nullified depends on the number of users served by the BS. Thus, user association is another factor that impacts the system performance and should be jointly considered with interference nulling. We formulate the joint interference nulling scheduling and user association problem as an integer programming problem, aiming to maximize the sum rate of all users subject to BS DOF constraints. We first investigate the case of interference nulling with a given user association, and propose a scheme to solve a relaxed problem as well as derive a performance upper bound. Then, we propose a distributed joint interference nulling and user association scheme based on a poly matching between users and BSs. Simulation results show that the proposed schemes effectively improve the sum rate and achieves a near optimal performance.

A novel method of layout optimization.Pareto optimal layout of multistatic radar.Available volume is proposed as performance metric.Pareto optimal results based on dynamic game.Low computational complexity. Layouts influence the performance of location accuracy greatly in multistatic radar. The layout in classical regular shape may not be an excellent choice. In this paper, Pareto optimal layout of multistatic radar is proposed based on dynamic game theory. The available volume associated with the Cramer-Rao lower bound (CRLB) of location accuracy is proposed as the utility metric to evaluate the performance of the layout. Simulation results show that different initial layouts may lead to different final layouts. Although these final layouts are different they are all Pareto optimal and similar in performance.

Increasing wind power (WP) integration is forcing conventional units to go through more frequent and significant cycling operations, which would accelerate wear and tear to unit components and eventually affect the unit's lifespan. In this context, this paper proposes a hierarchical scheme to control the power of plug-in electric vehicles (PEVs) to mitigate unit ramp cycling (URC) operations. A general-form representation of the URC operation is proposed for the first time. At the top level of the hierarchical scheme, a system net load variation range (NLVR) is constructed first to capture the uncertainty in WP forecasts, and then the PEV power is scheduled to reshape the NLVR so as to minimize the URC operations that can be caused by the possible net load realizations in the NLVR. Based on updated WP forecasts, the middle-level dispatch model exempts overscheduled anti-URC regulation onus on PEVs to promote PEV charging. At the bottom level, a decentralized PEV charging control strategy is used to implement the PEV power dispatch instruction. Simulation results verify that the proposed scheme can avert the URC operations effectively, while preserve most of the desired PEV charging energy. Simulation results also show that the proposed scheme is more capable of withstanding WP forecast errors compared with its deterministic version and a benchmark scheme.

Whilst aluminum alloys are widely used in military aircraft, space vehicle, and satellites, most of the work on the durability and damage tolerance (DADT) assessment of additively manufactured (AM) parts has focused on other types of materials. AM Scalmalloy®, which has a yield strength greater than 450 MPa and an elongation greater than 10%, appears to have the potential to meet the certification requirements needed for load bearing “aluminum alloy” aerospace parts. However, the ability to characterize crack growth is central to certification. As a result the present paper addresses crack growth in the AM Scalmalloy®. It is shown that when expressed as per the Hartman-Schijve crack growth equation, the curves for AM Scalmalloy® essentially collapse onto a single curve that is associated with the growth of both long and short cracks in the aluminum alloy AA7075-T7351. It is suggested that this finding has the potential to simplify the process for determining the upper bound growth curves required in NASA HDBK-5010 for the certification of AM space parts.

In this study, the two-stage supply chain scheduling problem with multiple customers and multiple manufacturers is considered. The first stage consists of m manufacturers (suppliers), while the second stage contains q vehicles, each of which distributes the batches from the manufacturers to the customers. Multiple customers and average lead time are two most important issues in practice; however, no study has been carried out so far to investigate these two issues together for the two-stage supply chain scheduling problem. The main contribution of this study is to coordinate production and distribution decisions to obtain an effective scheduling in a two-stage supply chain that contains multiple customers and multiple manufacturers. A mixed integer linear optimization model is developed to formulate the problem with the average lead time objective. Because the problem has been shown to be NP-hard, a hybrid artificial bee colony and simulated annealing (HABCSA) algorithm is introduced and used to solve the problem. In addition, a lower bound (LB) and several structural properties for the problem are presented and different batching mechanisms are developed based on these properties. For the purpose of performance analysis of HABCSA with different batching mechanisms, detailed computational experiments are conducted using random instances which are generated based on real aluminum production data for different capacity levels. The experimental results indicate that the HABCSA heuristic consistently outperforms the Genetic Algorithm (GA) and the Artificial Bee Colony (ABC) algorithm for each capacity level.

The use of augered cast-in-place (ACIP) piles for transportation infrastructure requires an appropriate reliability-based design (RBD) procedure. In an effort to improve the accuracy of an existing design model and calibrate appropriate resistance factors, this study presents a significantly revised RBD methodology for estimating the shaft and toe bearing capacity of ACIP piles using a large database consisting of static loading tests in predominately granular soils. The proposed design models are unbiased, as opposed to those currently recommended. Based on the reasonable assumption that a finite lower-bound resistance limit exists, lower-bound design lines are developed for shaft and toe bearing resistance by applying a constant ratio to the proposed design models. Resistance factors are calibrated at the strength or ultimate limit state (ULS) for ACIP piles loaded in compression and tension for two commonly used target probabilities of failure with and without lower-bound limits. For piles loaded in compression, separate resistance factors are calibrated for the proposed shaft and toe bearing resistance models. The inclusion of a lower-bound limit for piles loaded in tension results in a 24%–50% increase in the calibrated resistance factor. For piles loaded in compression, the application of a lower-bound limit results in a 20%–150% increase in the calibrated resistance factor, and represents a significant increase in useable pile capacity. Although the impact of a lower-bound limit on resistance factor calibration is directly dependent on the degree of uncertainty in the distribution of resistance, this effect is outweighed by the type of distribution selected (i.e., normal, lognormal) at more stringent target probabilities of failure due to differences in distribution shape at the location of the lower-bound limit. A companion paper explores the use of the revised ULS model in a reliability-based serviceability limit state design framework.

This paper considers the design of online transmission strategies for slotted energy harvesting point-to-point communication systems in wireless fading channels. Online transmission strategies decide the amount of energy allocated to each transmission slot based on the energy harvested amounts and channel gains observed in the current and previous time slots. Offline strategies, in contrast, assume non-causal knowledge of future energy arrivals and channel gains. We adopt a worst case design objective. For a given online policy, we are interested in computing its maximum rate gap that is defined as the difference between the offline and online rates, maximized over all possible energy arrivals and channel states. The competitive rate gap is then defined as the minimum maximum rate gap over all possible online strategies. Here, we obtain, within a constant, the maximum rate gap for the Myopic policy, which equally distributes the available energy over the remaining slots, and provide an upper and a lower bound on the competitive rate gap. Moreover, we propose a new online policy targeting the competitive rate gap. Numerical results show that the policy proposed performs close to the competitive rate gap lower bound in constant and arbitrarily varying channels, and obtains good performance with real energy harvesting traces.

Motivated by networked anti-missile defense applications, a cooperative target assignment and dwell allocation (CTADA) algorithm is developed for multiple target tracking (MTT) in phased array radar (PAR) network. The basis of the CTADA scheme is to not only optimize the target-to-radar assignment, but also effectively allocate the limited time resource of each PAR to its responsible targets, such that the MTT performance could be efficiently improved in overload situations (the number of targets greatly exceeds the number of PARs). We formulate the resource allocation framework as a mathematical optimization problem, and use the normalized Bayesian Cramer-Rao lower bound as its objective function. The resulting optimization problem consists of two adaptable parameters, one for target-to-radar assignment and the other for dwell allocation. By exploiting the unique relationship between these two adaptable parameters, an efficient two-step solution technique, which consists of a convex relaxation step and a heuristic dividing step, is developed for the CTADA optimization problem. Simulation results verify the superiority of the proposed CTADA algorithm, in terms of the worst case tracking accuracy of the multiple targets.

To intercept maneuvering targets at desired impact angles, a three-dimensional terminal guidance problem is investigated in this study. Because of a short terminal guidance time, a finite-time impact angle control guidance law is developed using the fast nonsingular terminal sliding mode control theory. However, the guidance law requires the upper bound of lumped uncertainty including target acceleration, which may not be accurately obtained. Therefore, by adopting a novel reaching law, an adaptive sliding mode guidance law is provided to release the drawback. At the same time, this method can accelerate the convergence rate and weaken the chattering phenomenon to a certain extent. In addition, another novel adaptive guidance law is also derived; this ensures systems asymptotic and finite-time stability without the knowledge of perturbations bounds. Numerical simulations have demonstrated that all the three guidance laws have effective performances and outperform the traditional terminal guidance laws. Keywords: Adaptive control, Finite-time guidance law, Impact angle, Sliding mode control, Three-dimensional guidance law

A methodology for optimizing variable pedestrian evacuation guidance in buildings with convex polygonal interior spaces is proposed. The optimization of variable guidance is a bi-level problem. The calculation of variable guidance based on the prediction of congestion and hazards is the upper-level problem. The prediction of congestion provided the variable guidance is the lower-level problem. A local search procedure is developed to solve the problem. The proposed methodology has three major contributions. First, a logistic regression model for guidance compliance behavior is calibrated using a virtual reality experiment and the critical factors for the behavior are identified. Second, the guidance compliance and following behaviors are considered in the lower-level problem. Third, benchmarks are calculated to evaluate the performance of optimized variable guidance, including the lower bound of the maximum evacuation time and the maximum evacuation time under a fixed guidance. Finally, the proposed methodology is validated with numerical examples. Results show that the method has the potential to reduce evacuation time in emergencies.

In this article, a combination of backstepping control and fast terminal sliding mode control is considered for controlling a ducted fan engine of thrust-vectored aircraft subject to parameter uncertainties and external disturbances. To increase the speed of convergence of the system states to the equilibrium points or to decay state errors to zero, the fast terminal sliding mode controller is used. For the first design, to compensate for uncertainties and also incoming disturbances to the system, a new backstepping fast terminal sliding mode control (BFTSMC) is derived. At the second design, to estimate the upper bound value of the lumped uncertainty in the BFTSMC, an adaptive rule is proposed, and an adaptive backstepping fast terminal sliding mode controller (ABFTSMC) is obtained. The asymptotic global stability of the closed-loop system is proved by Lyapunov stability theorem. Various scenarios are considered; then, the designed controllers are compared with traditional sliding mode controller (SMC) and adaptive SMC (ASMC) in the presence of external disturbances. Simulation results show that the proposed controllers can have a faster transient response and higher robustness against different types of disturbances in comparison with SMC and ASMC. Some advantages of the proposed controllers are simplicity in design and implementation, improving closed-loop system stability and better tracking.

As environmental awareness grows, sustainable scheduling is attracting increasing attention. The purposes of this paper are obtain the lower bound of energy-efficient flexible job shops with machine selection, job sequencing, and machine on-off decision making via a new mathematical model and to discover more energy-efficient rules with easy implementation in real practice via an efficient Gene Expression Programming (eGEP) algorithm. This paper first formulates a novel mixed-integer linear mathematical model to achieve effective machine selection, job sequencing, and machine off-on decision making. Then for the purpose of avoiding the empirical combination, five attributes exerting direct influence on the total energy consumption are extracted and consequently involved in the evolutionary process of eGEP. Furthermore, diversified rule mining operations with multi-gene representation and self-study are designed to enhance the search space and solutions quality. And, unsupervised learning is utilized in which global best and current worst are set to guide evolution direction since the learning progress has no prior knowledge. Experimental results show that machine off-on decisions efficiently reduce the total energy consumption; and, the discovered rules reach the lower bound calculated by GAMS/CPLEX in small problems and have significant superiority over other dispatching rules in energy saving.

State estimation (SE) has become an important aspect of real-time management of electrical distribution systems (EDS). However, it is a challenging problem, mainly due to the network model complexity and the lack of a reliable measurement infrastructure capable to cope with a variety of network configurations. This work presents an Extended Optimal Power Flow (E-OPF) for SE in EDS that considers different network configurations. The active and reactive power loads are handled as extended variables in the optimization model that are constrained by upper and lower bounds obtained from historical data. The objective function combines the state estimation error (SEE) from the known Weighted Least Squares (WLS) approach with additional indexes related to the state variables, which improve the SE process. The combination of different indexes and the handling of power loads as optimization variables without needing precise historical data are novelty for SE and consist of the main contributions for an EDS that, in general, has few available data. The computational efficiency when different topologies are considered and in situations where the WLS estimator might fail is also a suitable feature of the proposed approach. The known 16-bus and 33-bus test systems are used to assess the proposed methodology. An illustrative application of the proposed approach in a Phasor Measurement Unit (PMU) allocation problem is also presented.

A centrifuge test is performed to investigate the ultimate lateral capacity of a free-head rigid monopile foundation in normally consolidated (NC) clay. Based on the test result, an upper-bound velocity field is constructed to illustrate the evolution of the soil resisting mechanisms with the progressive rotation and the effect on reaction forces during loading. Simultaneously finite element analyses are also employed to model the centrifuge test and verify the conclusion of the upper bound analysis. Finally, an empirical expression is presented for predicting the upper-bound collapse loads of monopiles with different eccentricity/length ratios and length/diameter ratios in NC clay.

This paper provides a comprehensive scaling law-based performance analysis for multicell multiuser massive multiple-input-multiple-output (MIMO) downlink systems. Imperfect channel state information (CSI), pilot contamination, and channel spatial correlation are all considered. First, a sum-rate lower bound is derived by exploiting the asymptotically deterministic property of the received signal power, while keeping the random nature of other components in the signal-to-interference-plus-noise-ratio (SINR) intact. Via a general scaling model on important network parameters, including the number of users, the channel training energy and the data transmission power, with respect to the number of base station antennas, the asymptotic scaling law of the effective SINR is obtained, which reveals quantitatively the tradeoff of the network parameters. More importantly, pilot contamination and pilot contamination elimination (PCE) are considered in the analytical framework. In addition, the applicability of the derived asymptotic scaling law in practical systems with large but finite antenna numbers are discussed. Finally, sufficient conditions on the parameter scalings for the SINR to be asymptotically deterministic in the sense of mean square convergence are provided, which covers existing results on such analysis as special cases and shows the effect of PCE explicitly.

As the conventional spatial modulation (SM) system lacks an efficient downlink multiple access scheme, this paper proposes a novel downlink nonorthogonal multiple access aided multiple-input multiple-output SM system with finite alphabet inputs. Mutual information (MI) is used to characterize the system's achievable spectral efficiency (SE). Because the MI lacks a closed-form formulation, in this paper we propose a computational-efficient lower bound to provide an approximation to the simulated MI. Besides, preprocessing is also designed at the receiver to mitigate the detrimental effect of channel correlation, which leads to striking a higher SE performance. Simulation results not only confirm the accuracy of our SE analysis, but also substantiate the superior SE performance achieved by the proposed preprocessing.

To provide passengers with strict regularity of train operation, this research is devoted to modeling and solving the multi-periodic train timetabling problem to simultaneously optimize operation periods, arrival times, and departure times of all period types of trains on a double-track rail network. Based on the construction of a weighted directed graph, a multi-path searching model, namely, a 0-1 linear programming model, is built to minimize the total travel time of all period-types of trains subject to many operational constraints, including station parking capacity and train minimum load factors. After decomposing this model by introducing some Lagrangian multipliers to relax its complicated constraints, a solution algorithm, including a multi-path simultaneous searching sub-algorithm for each period-type of train, is designed to optimize both the feasible and dual solutions, which correspond to the upper and lower bounds, respectively. Finally, the performance, convergence, sensitivity, and practicability of our method are analyzed using many instances on both a small rail network and the high-speed railway between Beijing and Shanghai in China.

Airport gate assignment (AGA) is a critical issue for airport operations management. It aims to assign flights to gates according to their arrival and departure times. To tackle flight delays in airports, we propose a robust airport gate assignment (RAGA) to minimize the ( 1 − α ) -quantile of the total real-time overlap between consecutive flights at the same gate, namely, the total gate blockage time, so that the realized total gate blockage time is worse than its quantile with a probability, at most α. Given any constant, we develop an asymptotically tight upper bound for the violation probability that total gate blockage time is worse than the constant. Based on the upper bound, a solution-dependent uncertainty budget is introduced to develop a robust counterpart (RCP) for the RAGA. We further develop a solution technique for the RCP by transforming the problem into a finite number of tractable binary programmings. An empirical study of the Shuangliu International Airport (CTU) indicates that our proposed robust approach for AGA outperforms existing methods.

Summary By using the integrals of the signals to construct the triggering condition, integral-based event-triggered control can relax the requirement of persistent decrease on the Lyapunov function and, then, may yield better sampling performance. This paper studies the disturbance rejection problem for the integral-based event-triggered control systems with transmission delays and observer-based output feedbacks. An integral-based triggering condition is employed to generate the events. Two asynchronous models are implemented in the different sides of the networks. The model in the observer node is used to detect the events, whereas the model in the controller node is used to calculate the control signals. This structure contributes to avoiding the Zeno behavior, and then, an estimation on the lower bound of the interevent times is provided. Moreover, the criteria on the parameter in the triggering condition and on the bounds of the transmission delays are given to guarantee the desired disturbance rejection performance. Finally, a numerical example is provided to illustrate the efficiency and feasibility of the obtained results.

This paper investigates the sliding mode control (SMC) problem for switched systems. The parameter uncertainties are norm-bounded, and the upper bound of the nonlinear term and the loss of effectiveness of actuator faults are unknown. A novel adaptive sliding mode controller is constructed such that the influences of actuator faults and unknown nonlinearity can be effectively attenuated and the state variables can reach the predefined common sliding surface. Based on average dwell time strategy, the exponential stability condition of the switched systems is obtained. Finally, simulation results are presented to show the effectiveness of the proposed techniques.

Operation of on-demand services like taxis, dynamic ridesharing services, or vehicle sharing depends significantly on the positioning of idle vehicles to anticipate future demand and operational states. A new queueing-based formulation is proposed for the problem of relocating idle vehicles in an on-demand mobility service. The approach serves as a decision support tool for future studies in urban transport informatics and design of new types of urban mobility systems like carsharing, ridesharing, and smart taxis. A Lagrangian Decomposition heuristic is developed and compared with a relaxed lower bound solution. Using New York taxicab data, the proposed algorithm reduces the cost by up to 27% compared to the myopic case.

In this paper, a model-based perimeter control policy for large-scale urban vehicular networks is proposed. Assuming a homogeneously loaded vehicle network and the existence of a well-posed Network Fundamental Diagram (NFD), we describe a protected network throughout its aggregated dynamics including nonlinear exit flow characteristics. Within this framework of constrained optimal boundary flow gating, two main performance metrics are considered: (a) first, connected to the NFD, the concept of average network travel time and delay as a performance metric is defined; (b) second, at boundaries, we take into account additional external network queue dynamics governed by uncontrolled inflow demands. External queue capacities in terms of finite-link lengths are used as the second performance metric. Hence, the corresponding performance requirement is an upper bound of external queues. While external queues represent vehicles waiting to enter the protected network, internal queue describes the protected network’s aggregated behavior. By controlling the number of vehicles joining the internal queue from the external ones, herewith a network traffic flow maximization solution subject to the internal and external dynamics and their performance constraints is developed. The originally non-convex optimization problem is transformed to a numerically efficiently convex one by relaxing the performance constraints into time-dependent state boundaries. The control solution can be interpreted as a mechanism which transforms the unknown arrival process governing the number of vehicles entering the network to a regulated process, such that prescribed performance requirements on travel time in the network and upper bound on the external queue are satisfied. Comparative numerical simulation studies on a microscopic traffic simulator are carried out to show the benefits of the proposed method.

Perimeter control based on the Macroscopic Fundamental Diagram (MFD) is widely developed for alleviating or postponing congestion in a protected region. Recent studies reveal that traffic conditions might not be improved if the perimeter control strategies are applied to unstable systems where high demand generates heavy and heterogeneously distributed traffic congestion. Therefore, considering stability of the targeted traffic system is essential, for the sake of developing a feasible and then optimal control strategy. This paper sheds light on this direction. It integrates a stability characterization algorithm of MFD system equations into the Model Predictive Control (MPC) scheme, and features respectively an upper and a lower bound of the feasible control inputs, to guarantee system stability. Firstly, the dynamics of traffic heterogeneity and its effect on the MFD are analyzed, using real data from Guangzhou in China. Piecewise affine functions of average flow are proposed to capture traffic heterogeneity in both regional and subregional MFDs. Secondly, stability of a three-state two-region system is investigated via stable equilibrium and surface boundaries analysis. Finally, a three-layer hierarchical control strategy is introduced for the studied two-region heterogeneous urban networks. The first layer of the controller calculates the stable surface boundaries for the given traffic demands and then determines the bounds of control input (split rate). An MPC approach in the second layer is used to solve an optimization problem with two objectives of minimizing total network delay and maximizing network throughput. Heterogeneity among the subregions is minimized in the last layer by implementing simultaneously a subregional perimeter flow control and an internal flow control. The effectiveness and stability of the proposed control approach are verified by comparison with four existing perimeter control strategies.

Hysteresis in sensors and actuators can often be effectively compensated for by implementing an inverse hysteresis model in series with the sensor or the actuator. However, an apparent problem enters when the hysteresis characteristics vary over time in an unpredicted manner. Here, we derive an adaptive hysteresis compensation method for the case when we only have observations that are very sparse in time and magnitude. Contrary to previous methods it is based on reduced memory sequences and a preidentified initial model, which makes it possible to use only a few adaption parameters. In particular, we investigate the Preisach model (PM). Using a Bernstein polynomial basis for the PM, it is shown that invertibility translates into linear constraints, which ensures that the overall identification problem for the initial PM is convex. The dependence on PM initial conditions may have negative effects on hysteresis compensation and model adaptation. We give general conditions for losing this dependence and also an upper bound for the maximum error it may cause. The method is experimentally applied to a sensor for measurement of torque in a shaft. At times, the shaft is unloaded and consequently the torque can then be independently observed as being zero. This kind of problem leads to a nonlinear parameterization, but with very few parameters to update, which is successfully achieved using an extended Kalman filter. The method essentially removes the effects of hysteresis, fatigue, and aging for the intended use of the sensor.

Reliably predicting and estimating roof collapse in underground cavities such as mine goafs and power caverns is a serious concern in mining and geotechnical engineering fields. An analytical expression for a three-dimensional (3D) failure mode of a generic-shaped cavity crown is derived according to the upper bound theorem via limit analysis, where the Hoek-Brown yield criterion represents rock mass strength. The proposed method is validated by comparison against the extant research; the comparison also suggests that the roof profile plays a crucial role in cavity stability. Specific example is given to ellipsoidal cavity and the stability graph with respect to roof profile and cavity span is obtained and discussed. The effects of numerous factors on potential collapse are investigated based on the numerical calculation results. These results may provide workable guidelines for the optimization of roof shapes and support designs in minimizing rock detachment above cavity roofs in practice.

In modern tall and slender structures, dynamic responses are usually the dominant design requirements, instead of strength criteria. Resonance is often a threatening phenomenon for such structures. To avoid this problem, the fundamental eigenfrequency, an eigenfrequency of higher order, should be maximized. An optimization problem with this objective is constructed in this paper and is applied to a high-rise building. Using variational method, the objective function is maximized, contributing to a particular profile for the first mode shape. Based on this preselected profile, a parametric formulation for flexural stiffness is calculated. Due to some near-zero values for stiffness, the obtained formulation will be modified by adding a lower bound constraint. To handle this constraint some new parameters are introduced; thereby allowing for construction of a model relating the unknown parameters. Based on this mathematical model, a design algorithmic procedure is presented. For the sake of convenience, a single-input design graph is presented as well. The main merit of the proposed method, compared to previous researches, is its hand calculation aspect, suitable for parametric studies and sensitivity analysis. As the presented formulations are dimensionless, they are applicable in any dimensional system. Accuracy and practicality of the proposed method is illustrated at the end by applying it to a real-life structure.

This paper deals with the dynamic increase factor (DIF) to consider the dynamic effects in the nonlinear static analysis of RC frame buildings subjected to sudden loss of a first-storey column. The study applies nonlinear static and dynamic analyses and focuses on typical seismically designed reinforced concrete buildings. The analysis of these structures until failure requires considering both the geometric and material nonlinearities since the behaviour following sudden column loss is inelastic and possibly implicate catenary effects. Moreover, quantifying the robustness of this type of structure requires the implementation of detailed three-dimensional models. This paper investigates the effects of building properties including the number of storeys, the number of bays and the location of the removed column. The results show that the buildings designed for seismic loads reveal enough capacity to avoid the global collapse, both considering and neglecting the contribution of RC floor slab. The progressive collapse resistance is greater for the external column removal scenario than for the internal column removal scenario. Both the number of floors and the number of bays are not very sensitive parameters for the progressive collapse resistance. The nonlinear static approach leads to a conservative estimation of the collapse resistance when a DIF of 2 is used as the dynamic amplification factor. For the study cases, the response of the building subjected to column loss never involves the hardening phenomenon associated with the catenary action. In this situation, the DIF decreases monotonically with increasing vertical deflection. The lower bound value of DIF is very close to 1, which is the typical value of structures that fully develop their inelastic behaviour after column removal. The simulation results show that the floor slabs can greatly improve the progressive collapse resistance, but have a minor influence on the dynamic increase factor. Thus, the simplification of the problem into 3D bare frames can lead to an accurate estimation of DIF with less computationally intensive analyses.

This paper presents a control law that can counter both random disturbance and inertia matrix perturbation in quadrotor attitude stabilization. The control law consists of an ordinary proportional-derivative control and a disturbance compensation. The disturbance compensation is designed by creating a virtual force that always attracts the quadrotor’s state variables back to the equilibrium point. Numerical simulations demonstrate that the control law can counter the effect of the disturbance and perturbation by reducing the upper bound of solution and reducing the vibration effectively.

In-plane action is often the primary load-carrying mechanism of reinforced concrete structures. The plate bending action will be secondary, and the behaviour of the structure can be modelled with a reasonable accuracy using a generalised three-dimensional plane stress element. In this paper, the formulation of such an element is given and the Mohr–Coulomb and von Mises criteria are presented for second-order cone programming. Three examples of increasing complexity are used to analyse the performance of the element and the convergence rate and to demonstrate the potential of the proposed element.

Maximum Torque Per Ampere (MTPA) based on motor parameters is a common approach to achieve high efficiency and torque density in Interior Permanent Magnet Synchronous Machine Drives (IPMSMs). However, uncertainty (e.g., due to identification errors, magnetic saturation, or temperature variation) results in undesired deviation from the optimal operating trajectory. To solve this problem, MTPA tracking methods have been proposed, which exploit signal injection to search the minimum current point for a certain load torque in a closed-loop fashion. Closed-form design of the MTPA tracking loop dynamics has never been addressed in past literature and represents the main topic of this paper. A recent and efficient tracking method has been considered for the analysis and case study, i.e., [14]. Nonlinear small-signal gain of the loop can be calculated in closed form, leading to two valuable results. Dynamics can be programmed by optimal design of the tracking regulator, and online adaptation can be applied, making the designed MTPA tracking dynamics invariant with the operating point. A straightforward and effective solution is proposed for the regulator design, which allows us to obtain the desired bandwidth and first-order tracking response in the whole range of operation, being also suitable for auto-tuning and online adaptation. The method has been studied analytically and in simulation, also considering the influence of noise and parametric uncertainties. Finally the technique has been implemented on the hardware of a commercial industrial drive, proving the effectiveness of the proposal. The concepts described in this paper, design approach and adaptation strategy, analyzed here for the first time, are general and can be applied to any control scheme implementing closed-loop MTPA tracking.

This paper proposes a new adaptive-robust control (ARC) strategy for a tracking control problem of a class of uncertain Euler–Lagrange systems. The proposed adaptive-robust time-delay control (ARTDC) amalgamates the ARC strategy with the time-delay control (TDC). It comprises three parts: a time-delay estimation part, a desired dynamics injection part, and an adaptive-robust part. The main feature of the proposed ARTDC is that it does not involve any threshold value in its adaptive law; thus, it allows the switching gain to increase or decrease whenever the error trajectories move away or close to the switching surface, respectively. Thus, compared with the existing ARC schemes, ARTDC is able to alleviate the over- and underestimation problems of the switching gain. Moreover, the stability analysis of ARTDC provides an upper bound for the selection of sampling interval and its relation with controller gains. The proposed ARTDC shows improved tracking performance compared with the TDC and the existing adaptive sliding-mode control in simulations as well as in experiments with a multiple-degree-of-freedom system.

We consider scheduling for energy harvesting channels in which the users incur processing cost per unit time that they are on. The presence of processing costs forces the users to operate in a bursty mode. We consider online transmission scheduling where the users know the energy harvests only causally as they happen, and need to determine the optimum transmit powers and the optimum burst durations on the fly. We first consider the single-user channel. For this case, we first consider the case of independent and identically distributed (i.i.d.) Bernoulli energy arrivals, and then extend our analysis to the case of general i.i.d. energy arrivals. We determine the exactly optimum online policy for Bernoulli arrivals and propose a nearly optimum online policy for general arrivals. The proposed policy is near-optimum in that it performs within a constant gap from the optimum policy for all energy arrivals and battery-sizes. We then extend our analysis to the case of two-way energy harvesting channels with processing costs; in this case, the users incur processing costs for being on for transmitting or receiving data. We consider the case where the users harvest energy from fully correlated energy sources. We determine the exactly optimum online power schedule for i.i.d. Bernoulli energy arrivals and develop a nearly optimum online power schedule for general i.i.d. energy arrivals. Our proposed policy is distributed, which users can apply independently with no need for cooperation or coordination between them.