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This paper proposes a novel controller design using adaptation based modified super twisting control to facilitate trajectory tracking and hovering maneuvers for the quadrotor. The controller gains of the existing modified super twisting control require bounds on the disturbance for trajectory tracking and hovering of the quadrotor. In this paper, the controller gains are adapted using the proposed dynamic adaptation law without knowing the actual disturbance or their upper bounds. The controller is designed within a nonlinear framework without performing linearization of quadrotor dynamics, which enables the proposed controller to remain effective even when the states deviate significantly from their nominal values. The performance of trajectory tracking and hovering of the quadrotor in the presence of disturbance is demonstrated using numerical simulations. In order to assess the effectiveness of the controller, the performance of the adaptation based modified super twisting control is compared to the existing modified super twisting control, and the proposed controller outperforms the existing one. • Adaptive modified super twisting control for robust trajectory tracking and hovering. • Dynamic adaptation law relaxes the need for an upper bound on disturbance. • Numerical simulations show the validity of the proposed approach. • Performance is compared with the existing modified super twisting control. • Efficacy is demonstrated with Dryden wind and sinusoidal disturbances. [ABSTRACT FROM AUTHOR]

A novel real-time control method, named SF-B control method, was proposed and a comprehensive numerical simulation study was conducted for feasibility analysis. The simulation utilized a combination of Python scripts and FDS. The SF-B control method incorporates automatic detection and localization of the fire source, and the existing ventilation velocity is corrected based on the real-time state of motion of smoke fronts and the back-layering length. The control logic of the SF-B control method was described in detail, and the criterion for determining stabilization was provided. After a comparison with PID control method, scenarios involving random constant HHRs, random fire locations and variable HRR were set up for separate simulations, and the results were compared with previous experimental findings. The outcomes demonstrate that the fluctuation of the SF-B control method is only about 1/10 of that of the PID control method, and the control performance is not significantly influenced by the HRR or fire location. It shows a more powerful control ability on smoke back-layering in scenarios involving variable HRR. This method exhibits excellent compatibility with different fire conditions, showcasing high ventilation control efficiency, robust control capabilities, and delivering reliable control results. [ABSTRACT FROM AUTHOR]

This paper proposes a novel hybrid offline–online neural identification-based robust adaptive control strategy for quadrotors subject to parameter uncertainties and external disturbances. A new method of using hybrid offline–online neural identification is developed to compensate for the residual force and moment caused by parameter uncertainties. Unlike previous methods that ignore the relevance of uncertainties in the time dimension, the proposed neural identification method mines temporal features of states from historical data by introducing long short-term memory (LSTM) networks, resulting in high identification accuracy. Furthermore, an online adaptation update law is designed to optimize the weights of the network estimates for strong robustness. Consequently, based on the identification of the network, a robust tracking controller on S E (3) is constructed, which is capable of attenuating the bounded disturbances by introducing anti-disturbance components. Finally, numerical simulations and experiments in the real physical world are carried out to verify the performance. The experimental results demonstrate that the proposed strategy not only achieves more accurate uncertainty identification in comparison to the existing methods, but also realizes a 44.28% reduction in the root-mean-square error (RMSE) of the position under the lump uncertainties, which illustrates enhanced robustness and generalizability. Video: https://youtu.be/3kIG5fcQaVE. • A hybrid offline–online neural identification-based control strategy is designed. • LSTM models trained based on meta learning are used to mine temporal correlation. • A hybrid offline–online neural identification method enhances the generalizability. • The proposed controller is robust to parameter uncertainty and external disturbance. • The control performance is evaluated through comparative simulations and experiments. [ABSTRACT FROM AUTHOR]

In this paper, the leader–follower robust synchronization issue is mainly addressed for reaction–diffusion neural networks (RDNNs) with multiple leaders and external disturbances under directed graphs. Based on the σ modification approach, we propose a novel distributed adaptive controller by adding a term − θ i (c i − 1) 2 to avoid the phenomenon of parameter drift, that is, the adaptive parameters grow to infinity. Meanwhile, different from the adaptive control algorithm proposed in the undirected graph, we introduce a new function χ i (t) to provide additional freedom for the design to achieve robust containment when confronted with external disturbances. Further, the robustness of tracking synchronization with one leader is guaranteed by the proposed adaptive controller when the external disturbances concerning L 2 norm are bounded. Finally, relevant numerical simulation graphics are displayed separately to verify the correctness of the related theoretical results. [ABSTRACT FROM AUTHOR]

A differential drive catamaran unmanned surface vehicle tracking control in presence of external disturbances and uncertainties is addressed. A super-twisting control considering a single adaptive gain is adopted, where the number of tuning parameters of this control gain is reduced compared with standard adaptive super-twisting approaches. Furthermore, the gain exhibits smooth dynamics compared with controls using discontinuous functions signals. An analysis of the closed loop stability is provided using a Lyapunov approach. The proposed adaptive super-twisting approach is designed to drive the vessel along time-varying trajectories in presence of perturbations, ensuring robustness, and practical finite-time convergence. Numerical simulations, and real-time experimental tests using a prototype, illustrate the control performance under payload uncertainty and external perturbations. For further demonstration, a comparison with some of the existing versions of the super-twisting control included one with variable gain is provided. [ABSTRACT FROM AUTHOR]

This paper mainly focuses on the cooperative robust consensus tracking problem of uncertain networked Euler-Lagrange systems (NELSs) with time-varying delays and output constraints. By systematically integrating the neural network (NN) adaptive technique and the logarithmic type Barrier Lyapunov Function (BLF) in combination with the additional robust control law, two distributed robust consensus schemes for uncertain NELSs are proposed for two cases of time-varying communication and input delays respectively, which can fully guarantee to constrain the output consensus error within a safety region simultaneously. Furthermore, numerical simulation examples are provided to demonstrate the comparable potential advantages of the proposed robust control law over some existing algorithms, including adaptability, stability, and robustness, as well as delay effects. • An NN-based robust controller is designed to effectively handle the uncertainty of NELSs. • Combining NN with BLF ensures the adaptability, stability, and robustness of the consensus tracking scheme for NELSs. • A unified analytical framework is developed by deriving upper bounds on the allowed values of communication and input delays. [ABSTRACT FROM AUTHOR]

This paper concerns with the issues of designing an improved-equivalent-input-disturbance (IEID) based robust two dimensional modified repetitive control (2D MRC) for a class of fuzzy systems in the presence of aperiodic disturbances. Specifically, IEID-estimator is implemented to the 2D MRC systems that estimates all types of disturbances and compensates them for assuring robust stability. In particular, the proposed 2D MRC system has two different type of behaviours such as continuous control and discrete learning independently. To obtain gains of the observer and the controller, an adequate set of robust stability conditions is derived in the form of a linear-matrix-inequalities. Finally, simulation results for three numerical examples are provided to depict the efficacy of the proposed control technique. • An IEID-based state tracking control mechanism is proposed for T-S Fuzzy systems. • To be specific, 2D MRC is designed in order to get the exact tracking performance. • The acquired theoretical outcomes are validated by the numerical simulation results. [ABSTRACT FROM AUTHOR]

Disturbance observer (DOB)-based robust control for a variable speed refrigeration system (VSRS) is described in this paper for accurate tracking of the reference temperature under model uncertainty and disturbances including external noise. In particular, a Q-filter, which is the core of a DOB, is designed by H ∞ (H-infinity) norm optimization to estimate the disturbance and to compensate the model uncertainty of VSRS. Simultaneous control of the oil outlet temperature and superheat is carried out using a dual single-input single-output proportional–integral (PI) control scheme with the designed observer in the inner loop. The effectiveness of the observer is evaluated by means of computer simulations and real experiments. The results showed that settling time and steady-state error were significantly reduced when DOB was applied, compared to when DOB was not applied even in the presence of model uncertainties and disturbances. These control results show that PI control with the designed DOB can facilitate robust control even when the VSRS model contains uncertainties and unforeseen disturbances are added to the system. [ABSTRACT FROM AUTHOR]

This paper presents a novel robust controller applied to a quadrotor vehicle for regulation and trajectory tracking tasks. In the proposed scheme, the quadrotor position is controlled by a proportional integral derivative (PID) controller, while the orientation control is achieved through a model-based controller. The proposed controller is combined with a power reduction methodology, which includes a controller-gains tuning stage using the cuckoo search algorithm, and a minimum jerk trajectory design stage. The performance of the new controller is assessed in a free-disturbance case and under the effect of parametric uncertainty and aero-dynamical disturbances. The new controller is compared against two linear PID controllers and a nonlinear sliding mode-based controller. Numerical simulations demonstrate the superiority of the proposed scheme as well as its robustness against different types of perturbations. Also, it is proven that the power demanded by any controller is reduced when using the power reduction methodology. • A novel robust PID controller for quadrotor vehicles is proposed. • Point-to-point and trajectory tracking control are achieved with the new controller. • A procedure for reduction of power consumption is developed. • The PID controller is applied to the quadrotors disturbed nonlinear dynamic model. • The new PID controller is compared with sliding mode and PID controllers. [ABSTRACT FROM AUTHOR]

This paper addresses the robust control problem of Twin Rotor Multi input multi output System (TRMS) via H 2 and H ∞ control techniques. An output error optimization technique is proposed to develop H 2 and H ∞ controllers for the well posed plant. Computer simulation results are presented for closed loop TRMS in hovering positions which show marked improvements over previous works. The simulated plant exhibits stable responses in hovering position at the desired pitch and yaw angles. Corrections are incorporated in model formulation to compensate control and sensor singularities. The output error optimization technique proposed in the present paper can be essentially adopted in controlling of 2 by 2 plants exhibiting non-minimum phase dynamics. [ABSTRACT FROM AUTHOR]

This paper solves the event-triggered passivity and synchronization problems for delayed multiple-weighted coupled reaction–diffusion neural networks (DMWCRDNNs) composed of non-identical nodes with and without parameter uncertainties. On one side, by designing appropriate event-triggered controllers, several passivity and synchronization criteria for DMWCRDNNs with certain parameters under the designed event-triggered conditions are derived based on the Lyapunov functional method and some inequality techniques. On the other side, we consider that the external perturbations may lead the parameters in network model to containing uncertainties, robust event-triggered passivity and synchronization for DMWCRDNNs with parameter uncertainties are investigated. Finally, two examples with numerical simulation results are provided to illustrate the effectiveness of the obtained theoretical results. [ABSTRACT FROM AUTHOR]

• A decentralized voltage regulation approach in distribution systems with DGs is presented. • PI decentralized controllers are tuned according to a MIMO design. • Zero voltage regulation error and robust stability are guaranteed. • Optimization of the voltage set-points is included. • Numerical simulations give evidence of the achieved robust voltage regulation. The presence of Distributed Generators (DGs) in distribution networks may cause voltage fluctuations due to the reverse power flow. To solve this problem the present paper proposes a decentralized approach to regulate the voltage amplitude of the nodes to which the DGs are connected. Each DG is equipped with a PI decentralized controller whose parameters are tuned using a MIMO design. Zero voltage regulation error, robust stability and tolerance to the disconnection of DGs are guaranteed. A IEEE test distribution system with 123 nodes and 15 PVs is simulated to assess the obtained performance. The numerical results demonstrate that the application of the proposed design can help to guarantee robust voltage regulation. [ABSTRACT FROM AUTHOR]

This paper presents robust nonlinear control of a variable-pitch quadrotor with the flip maneuver. Backstepping approach is chosen for nonlinear control design. A control allocation loop dynamically computes the blade pitch angle of each rotor. A systematic method to select controller gains is presented that ensures closed-loop stability. Detailed analysis of the flip maneuver in the presence of input saturation is presented for the first time. Performance of the proposed control law is first verified through simulation. This is then implemented on a PixHawk open source autopilot board and flight tests are performed on an off-the-shelf variable-pitch quadrotor frame. [ABSTRACT FROM AUTHOR]

Abstract Cascading failures are emergent phenomena on interdependent networks and plenty of models have been proposed to explore the features of cascading failures. However, few of them focus on energy-supported dependence relations between interdependent networks including control nodes. In this paper, a load-based energy-supported cascading failure model is proposed on interdependent networks, where the energy control factor λ and control nodes are considered. According to the numerical simulations, it is found that as the energy control factor λ increases, both interdependent Erdős–Rényi (ER) networks and scale-free (SF) networks become more vulnerable. Meanwhile, with the increase of energy control factor λ , interdependent SF networks become more robust than ER networks against random failures. In addition, increasing the power exponent γ can improve the robustness of interdependent SF networks. Finally, although coupling preferences and control node setting strategies have few impacts on cascading failures on interdependent ER networks, the assortative coupling preference (AC) and betweenness control node setting strategy (BS) can improve the robustness of interdependent SF networks. Highlights • An energy-supported cascading failure model is proposed on interdependent networks. • Increasing factor λ makes interdependent SF networks more robust than ER networks. • Increasing exponent γ improves the robustness of interdependent SF networks. • The AC and BS can improve the robustness of interdependent SF networks. [ABSTRACT FROM AUTHOR]

Abstract Grid voltage parameters are essential for the real-time control and protection in power systems. Current approaches mainly focus on the phase-locked loop methods, but which are slow and sensitive to voltage variations and disturbances. In this paper, the estimation of grid voltage parameters is recast as an observer design problem. A second-order sliding mode observer (SMO) is designed to estimate the grid voltage parameters based on the exosystem model. Moreover, the asymptotic stability of the closed-loop SMO is proved. Simulation and experimental results are presented to illustrate the effectiveness of the proposed approach, which achieves fast response and accurate estimation Highlights • An exosystem model is introduced to represent the sinusoidal voltage. • A lowest-order sliding mode observer is designed to estimate grid voltage parameters. • The proposed approach achieves fast response and strong robustness with global stability. • The effectiveness is verified by both simulation and experimental results. [ABSTRACT FROM AUTHOR]

Highlights • We introduced an additional objective function of the ability of peak load regulation to deal with the reverse peak load regulation characteristic of wind power; • The novelty of the paper is to propose a novel multi-objective robust optimization model for unit commitment considering peak load regulation ability and temporal correlation of wind powers. • This paper combined Constraint-and-Column Generation and Normalized Normal Constraint to deal with the proposed multi-objective robust optimization problem. • The widely-used IEEE 118-bus test system is used to verify the proposed robust optimization model. Abstract With the increasing applications of wind energy & power technologies, the stochastic fluctuation and reverse peak load regulation characteristic of wind power brings great challenges to unit commitment of power systems. It is difficult to meet the demand of the peak load regulation with the wind power integration for the existing robust unit commitment models when only considering the cost. In order to solve this problem, an additional objective function of the ability of peak load regulation is constructed for unit commitment solutions. As a result, a novel multi-objective robust unit commitment model considering both the cost and peak load regulation is proposed. Moreover, probabilistic scenario analysis is applied to constraint the model of the fluctuant wind power, and temporal correlation of wind powers is also considered to further improve the economy in the long run. In order to deal with multi-objective problem for the robust optimization solutions, the above-mentioned problem is decomposed into two stages, i.e., a main problem and a subproblem. The main problem is to determine unit commitment and the subproblem is a "min–max optimization" one to obtain the unit outputs. Finally, the simulation analysis based on IEEE118 node system verified the robustness and the validity of the proposed model. [ABSTRACT FROM AUTHOR]

Abstract This paper deals with the modeling of bearing behavior and damage evolution of single-lap bolted composite interference-fit joints. Shear nonlinearity constitutive relations for composites are defined with the Ramberg-Osgood equation. The anisotropic damage model established on continuum damage mechanics incorporates extended 3D failure criteria and exponential damage evolution rule to describe damage initiation and material stiffness degradation, respectively. Numerical simulations of composite joints based on the proposed model under varying interference-fit sizes, bolt preload levels and composite layups are conducted and validated by experimental tests. Results show the interface voids of joint-hole in micro-scale left by hole-drilling would compensate part of the nominal interference-fit size and make the real fit sizes smaller than the numerical ones. The strain contours and hyperechoic strips detected in bearing specimens are accurately characterized by damage patterns in joint models, whereas the predicted load-displacement behavior shows over-conservative errors caused by excessive bolt inclination. The fiber breakage and matrix crushing in micro-morphology on the bearing plane are well captured in numerical model, suggesting robust ability of the proposed model in application. [ABSTRACT FROM AUTHOR]

Abstract In this paper, two control methods are proposed to control mismatched dc–dc buck converters. In the first method, called Method I, a multiple surface sliding mode control is proposed to handle mismatched load uncertainty. A major problem associated with multiple surface sliding mode control viz. 'explosion of terms' is handled by a disturbance observer. Another method, Method II, based on simultaneous state and disturbance observer is proposed as a further improvement over Method I in terms of sensor requirement. The practical stability of the proposed schemes is proved. The performance of both the methods are assessed for regulation and tracking of output voltage under various uncertainties and is compared with a method based on extended state observer. It is shown by simulation and experimentation that the transient and steady state performance of both the controllers are satisfactory. Highlights • Two new control methods are proposed to control a mismatched buck dc–dc converter. • One of the control methods overcomes the problem of 'explosion of terms's associated with multiple surface sliding control as well as it reduces the requirement of load current sensor. • In the second method, a simultaneous state and disturbance observer is proposed so as to save on load current as well as inductor current sensors. • Both the control methods are validated by simulation and experimentation. • Both methods can regulate the output voltage and can track the desired reference voltage. [ABSTRACT FROM AUTHOR]

Abstract This paper studies the formation of multiple mobile agents with double integrator dynamics and their target tracking. An algorithm consisting of an observer and a feedback control law of the nonsmooth type is proposed for the purpose of achieving finite-time formation and target tracking. The development is based on the Multi-input Multi-output (MIMO) super-twisting like approach aiming at finite time convergence without chattering. In addition to the formation; tracking, chattering prevention and robustness is also provided by the sliding mode mechanism which is demonstrated by simulations. Highlights • Formation of multiple mobile agents and their target tracking is studied. • An observer based feedback control algorithm for finite-time tracking is proposed. • The design is based on the Multi-input Multi-output super twisting like approach. • Tracking and chattering prevention of the robust system are shown by simulations. [ABSTRACT FROM AUTHOR]

Highlights • We show how crowds can be simulated within the PBD framework by augmenting it with agent-based planning velocities. • We adopt the position-based frictional contact constraints of granular materials to model local collision avoidance. • XSPH viscosity approximates collective group behavior. Our model permits the natural development of agent groups. • We develop a novel long-range collision avoidance constraint to deal with anticipatory collisions. • We demonstrate "multi-species" crowd coupling by supporting spatially varying Lagrangian physical properties. Graphical abstract Abstract We introduce a crowd simulation method that runs at interactive rates for on the order of a hundred thousand agents, making it particularly suitable for use in games. Our new method is inspired by Position-Based Dynamics (PBD), a fast physics-based animation technique in widespread use. Individual agents in crowds are abstracted by particles, whose motions are controlled by intuitive position constraints and planning velocities, which can be readily integrated into a standard PBD solver, and agent positions are projected onto constraint manifolds to eliminate colliding configurations. A variety of constraints are presented, enabling complex collective behaviors with robust agent collision avoidance in both sparse and dense crowd scenarios. [ABSTRACT FROM AUTHOR]

Abstract One of the most crucial tasks in hydrocarbon field development, it is an optimal wells placement. Simulation-based well placement optimization can be intricate to implement or computationally expensive. In this paper, a novel approach for the reduction of computational challenges by combining population-base optimization algorithm with self-adapt reservoir clusterization method to determine initial well placement position for the similar producing zones is presented. The developed methodology supported initial well placement location and allowed to shrink search space area. As a result, the computational time was decreased by 18% for particle swarm optimization and by 21% for genetic algorithm. Additionally, the project profitability was increased by 0.24% in case of genetic algorithm utilization and 0.59% for particle swarm optimization, what confirms the necessity for precise well placement localization. The average convergence factor for the employed algorithms increased from 18.43% to 60.78% for genetic algorithm and from 41.71% to 46.96% for particle swarm algorithm. An additional advantage of the developed clustering method is a notable reduction in time to reach high solution diversity in particle swarm algorithm. Increasing SOM training time for the large data set is negligible in comparison with full reservoir simulation run. The proposed methodology is based on the typical numerical model without requirement of additional dataset or streamline and can be easily transferred from field to field. The power and the workflow utility are demonstrated by the results of significant improvement for the algorithm's convergence rate and time. Highlights • Novel self-adapt methodology to enhance well placement optimization was developed. • Significant reduction of optimization time was observed. • Artificial Kohonen neural network was implemented to solve complex engineering problem. [ABSTRACT FROM AUTHOR]

Highlights • This paper handles the problem of composite binary hypothesis testing when an accurate probability model is not available. • Under this framework, a robust generalization of the Gaussian quasi score test is developed. • The proposed generalization, assumes a Gaussian model after applying a transform to the probability measure of the data. • The considered measure-transformation is structured by a non-negative data weighting function, called MT-function. • Proper selection of the MT-function leads to significant mitigation of the model mismatch effect. Abstract This paper deals with the problem of composite binary hypothesis testing when an accurate parametric probability model is not available. Under this framework, a robust generalization of the Gaussian quasi score test (GQST) is developed. The proposed generalization, called measure-transformed (MT) GQST assumes a Gaussian probability model after applying a transform to the probability measure (distribution) of the data. The considered measure-transformation is structured by a non-negative data weighting function, called MT-function. By proper selection of the MT-function, we show that, unlike the GQST, the proposed MT-GQST can gain resilience against heavy-tailed noise outliers, leading to significant mitigation of the model mismatch effect (introduced by the normality assumption), and yet, have the implementation advantages of the standard GQST (arising from the convenient Gaussian model). The proposed MT-GQST is applied for testing the vector parameters of linear and nonlinear multivariate data models. Simulation examples illustrate its advantages as compared to the GQST and other robust detectors. [ABSTRACT FROM AUTHOR]

Abstract This research is motivated by the problem of regenerative chatter vibration, which is a pronounced problem in machining. The problem of chatter vibration is especially critical in the internal turning process, where the material is removed from the inner part of a rotating workpiece using a slender bar. Active mechatronic solutions to the problem of regenerative chatter vibration in internal turning are mostly given by means of piezoelectric actuators embedded into the boring bar. However, due to the volume needed for accommodation of the actuators, it is difficult to apply these solutions to narrow bars. This paper presents a novel actuation approach with a potential application for active control of regenerative chatter vibration in internal turning. This approach involves a bar whose clamping has an ability to move perpendicularly to the bar axis. In addition, it involves two actuators: one of them is placed near the bar clamping, and another one is placed at the bar clamping. The presented actuation approach is compared to the related approaches in the literature. Additionally, this paper presents a method for robustness analysis of the actively controlled cutting process, which is based on the structured singular value methodology. Experiments performed on a specially designed testbed imitating the internal turning process demonstrate the potential applicability of the introduced actuation approach, as well as the utility of presented robustness analysis method. [ABSTRACT FROM AUTHOR]

Abstract Materials with crimped fibers have special properties that can be effectively explored only when using a micro–macro perspective. In this framework, a novel constitutive model based on a multiscale structural rationale is introduced. Material micromechanics, depending on fiber straightening mechanisms, is described introducing a beam model which drives material model response. This rationale leads to a quasi-analytical formulation, coupling the advantages of purely-analytical and computational approaches. The proposed model is also proven to be polyconvex. Furthermore, a finite-element formulation is developed, enriched by a quasi-analytical core associated with the multiscale constitutive formulation. Different solution strategies are tested in order to optimize the numerical performances in terms of accuracy, robustness and cost. Moreover, a mixed finite element formulation based on a simplified-kinematics-for-anisotropy (SKA) is introduced. For the tested boundary value problems, the SKA-element is an optimal choice in terms of displacement and fiber stress convergence behavior, especially for coarse meshes. Highlights • The mechanics of flexible composites and textiles is affected by crimped fibers. • A novel micro–macro constitutive approach is developed. • Material micromechanics is described by means of a beam model. • The multiscale formulation is implemented in a finite-element framework. • Anisotropic locking phenomena is treated through a mixed SKA-formulation. [ABSTRACT FROM AUTHOR]

Abstract A concept of bypass rewiring on directed networks is proposed, and random bypass rewiring on infinite directed random networks is analytically and numerically investigated with double generating function formalisms and simulations. As a result, it is derived that random bypass rewiring makes infinite directed (undirected) random networks extremely robust for arbitrary occupation probabilities if and only if in-degree of every node except a fixed number of nodes is equal to the out-degree (every node except a finite number of nodes has even degree); random bypass rewiring can make the percolation threshold 0 on infinite directed (undirected) random networks. From the results on infinite random networks, it is generalized that a finite network has a strongly connected spanning subnetwork which has an Eulerian path or cycle if and only if there exists an way of bypass rewiring to make the finite network extremely robust for every combination of removed nodes; Eulerian networks are extremely robust with bypass rewiring for every combination of removed nodes. The generalized results say that bypass rewiring improves connectivity and robustness of not only infinite networks but also real-world networks, like the Internet, with a finite number of nodes. Highlights • A concept of bypass rewiring on directed networks is proposed. • Random bypass rewiring can guarantee extreme robustness of random networks. • Bypass rewiring can make the percolation threshold 0. • Bypass rewiring guarantees extreme robustness of Eulerian networks. • Bypass rewiring can guarantee extreme robustness of the Internet topology. [ABSTRACT FROM AUTHOR]

Highilights • Optical image authentication scheme using dual polarization decoding is purposed and validated. • We examine a sparse encoding method based on image division to process the original image. • The proposed scheme resolves the alignment problem of previous polarization encoding methods. • Our verification system exhibits high robustness and flexibility against attacks and interference. • Results might be useful for polarization-encoding based optical verification. Abstract We report on an optical image authentication scheme using dual polarization decoding configuration. We examine a sparse encoding method based on image division to process the original image. Next, the sparse original image is separated into two noise-like structure images with random polarization parameters which are digitally encoded. During the decoding stage, the encoded images are sent into a dual polarization decoding configuration where a pixelated polarizer with sparse distribution of transmission angle is employed for image recovering. The proposed scheme avoids complicated recording of complex information and resolves the alignment problem of previous polarization encoding methods. Then we examine numerical simulations to show that the proposed scheme is well suited to recover the original image when decryption keys are correctly used. We find evidence that the verification system exhibits high robustness and flexibility against attacks and interference. We expect that these results might be useful for polarization-encoding based optical verification. [ABSTRACT FROM AUTHOR]

Abstract Uncertainty and disturbance widely exist in the process industry, which may deteriorate control performance if not well handled. The uncertainty and disturbance estimator (UDE) emerges as a promising solution by treating the external disturbances and internal uncertainties simultaneously as a lumped term. To overcome its limitation caused by time delay, a modified UDE (MUDE) has been proposed recently. However, its parameter tuning relies heavily on trial-and-error, thus being time-consuming in balancing the robustness and performance. To this end, this paper aims to develop an automatic tuning procedure for the MUDE-based control system. The quantitative relationship between system performance and the scaled parameters is empirically built. Iterative Feedback Tuning (IFT) is utilized to approximate the nominal model towards actual process. Through the empirical formula and optimized model, an automatic design procedure is proposed after taking into account the system robustness and output performance simultaneously. Simulation results show the superiority of the closed-loop performance over the original MUDE controllers. The experimental results validate the feasibility of the method proposed in this paper, depicting a promising prospect in the practical application. Highlights • Control system is designed after balancing robustness and output performance. • Control parameters are automatically tuned through iterative feedback tuning. • The proposed scheme reduces dependency on the process model and estimator. [ABSTRACT FROM AUTHOR]

Highlights • Introduced Dynamic Institutions combine (i) the normative control of OCMAS systems, with (ii) the expressivity and robustness of BPMN systems, with (iii) planning capabilities of standard planning systems (i.e. STRIPS) and (iv) performance of statically compiled systems. • DI can be employed in various scenarios. Its architecture allows Dis to be deployed in the distributed manner, where high-performance simulation can run on a separate machine to the visualisation machine, improving the system performance and user experience. • Using our version of case-based planning, we have increased the number of dynamically planning agent in real-time at an acceptable framerate from around 40–4000. This also includes almost 10-fold increase to our previous results. Graphical abstract Abstract In commercial video games and simulations, non-player characters are capable of quite complex behaviour. Very often though, each class of non-player characters (that we further call virtual agents) is manually programmed or scripted. This means that instead of possessing some level of intelligence, allowing the agent to decide dynamically on the actions it needs to perform, we supply the agent with a list of possible situations that may arise in the game. For each such situation, we give the agent a pre-programmed script that tells it how to behave. Producing such scripts for every role an agent might play in a game or simulation is a very costly exercise. This may be acceptable in commercial game development, where budgets for modern games are sometimes comparable to budgets of Hollywood movies, but not adequate for research simulations and indie games. In this paper, we discuss how indie games and research simulations can be enriched with the sophisticated social behaviour of virtual agents in a semi-automatic manner through the use of AI planning. By supplying agents with roles and developing a computational model of their needs, we can use AI planning (also known as dynamic planning) to increase the complexity of agent behaviour dramatically and at the same time achieve a high degree of automation and reduce the development costs. AI planning is gaining popularity in games development, but it is often discarded due to performance issues. We will show how to improve the performance of planning process through the use of dynamic institutions and case-based planning. We will illustrate the aforementioned ideas on an example of developing a Virtual Reality simulation of everyday life in Ancient Mesopotamia. [ABSTRACT FROM AUTHOR]

Abstract Purpose Determination of the minimum number of gradient directions (N min) for robust measurement of spherical mean diffusion weighted signal (S ¯). Methods Computer simulations were employed to characterize the relative standard deviation (RSD) of the measured spherical mean signal as a function of the number of gradient directions (N). The effects of diffusion weighting b -value and signal-to-noise ratio (SNR) were investigated. Multi-shell high angular resolution Human Connectome Project diffusion data were analyzed to support the simulation results. Results RSD decreases with increasing N, and the minimum number of N needed for RSD ≤ 5% is referred to as N min. At high SNRs, N min increases with increasing b -value to achieve sufficient sampling. Simulations showed that N min is linearly dependent on the b -value. At low SNRs, N min increases with increasing b -value to reduce the noise. RSD can be estimated as σ S ¯ N , where σ = 1/SNR is the noise level. The experimental results were in good agreement with the simulation results. The spherical mean signal can be measured accurately with a subset of gradient directions. Conclusion As N min is affected by b -value and SNR, we recommend using 10 × b / b 1 (b 1 = 1 ms/μm2) uniformly distributed gradient directions for typical human diffusion studies with SNR ~ 20 for robust spherical mean signal measurement. [ABSTRACT FROM AUTHOR]

Abstract Cooperative Localisation (CL) is a robust technique used to improve localisation accuracy in multi-robot systems. However, there is a lack of research on how CL performs under different conditions. It is unclear when CL is worthwhile, and how CL performance is affected if the system changes. This information is particularly important for systems with robots that have limited power and processing, which cannot afford to constantly perform CL. This paper investigates CL under varying sensor qualities (position accuracy, yaw accuracy, sample rate), communication rates, and number of robots for both homogeneous and heterogeneous multi-robot systems. Trends are found in MATLAB simulations using the UTIAS dataset, and then validated on Kobuki robots using an OptiTrack-based system. We find that yaw accuracy has a substantial effect on performance, a communication rate that is too fast can be detrimental, and heterogeneous systems are greater candidates for cooperative localisation than homogeneous systems. Highlights • We alter and analyse parameters affecting Cooperative Localisation (CL). • Simulations are performed in MATLAB, and validated on hardware-in-the-loop experiments. • Our findings will help determine the suitability of CL for a system. [ABSTRACT FROM AUTHOR]

Highlights • Development of highly valid kinetic model of a plant factory cultivating salad (SPF) by system dynamics. • Effective closed-loop control law of SPF was obtained by stored-energy (Lyapunov) function and dissipative theory. • Both digital and real-time simulation were conducted for verification and validation. Abstract Internal variations and external disturbances often adversely influence normal operation of plant factories and their production stability. In this research, a salad-cultivating plant factory (abbreviated as SPF) was considered as a dissipative system, its highly valid kinetic model was developed using system dynamics and experimental data. Optimal closed-loop control law of light intensity, temperature and aerating rate was then obtained based on the gradient of stored-energy (Lyapunov) function of the SPF derived from Hamilton-Jacobi-Isaacs equation. Both digital and real-time simulation results showed that the SPF closed-loop control system with optimal control law could continuously dissipate the stored energy, namely internal variation and external disturbances resulting from insect and microbial damage and other environmental fluctuations with different strengths to stabilize the SPF at an operating point and maintaining the good yield of salad with desired dynamic response characteristics to control actions. This research will lay a theoretical and method foundation for construction and operation of facility agricultural systems and closed artificial ecosystems. [ABSTRACT FROM AUTHOR]

Cascading failures are widely found in hyper-networks. In this paper, a new cascading failure model based on couple map lattice is proposed in terms of the hyper-adjacent matrix and joint degree. The cascading failure model is rather universal describing the cascading failure behavior of k uniform hyper-network and many typical complex networks. Simulation results show that 3 uniform scale-free hyper-network is robust against random attacks but is vulnerable to deliberate attacks. In addition, a scale-free hyper-network is more robust than a general complex network against the same attack. Finally, an approximate solution to the disturbance threshold of k uniform scale-free hyper-network is obtained. [ABSTRACT FROM AUTHOR]

Abstract The parameter optimization method for multivariable systems is extended to the controller design problems for multiple input multiple output (MIMO) square fractional-order plants. The algorithm can be applied to search for the optimal parameters of integer-order controllers for fractional-order plants with or without time delays. Two examples are given to present the controller design procedures for MIMO fractional-order systems. Simulation studies show that the integer-order controllers designed are robust to plant gain variations. Highlights • Extend a parameter optimization method to controller design problems for MIMO FO-plants. • The methodology can be applied to fractional-order plants with or without time delays. • The robustness tests show that the controller is robust to the plant gain variations. [ABSTRACT FROM AUTHOR]

Abstract This paper investigates the multi-objective attitude tracking problem of a flexible spacecraft in the presence of disturbances, parameter uncertainties and imprecise collocation of sensors and actuators. An integrated robust H∞ controller, including an output feedback component and a feedforward component, is proposed, and its gains are calculated by solving Linear Matrix Inequalities. The output feedback component stabilizes the integrated control system while the feedforward component can drive the attitude motion to track the desired angles. The system robustness against disturbances, parameter uncertainties and imprecise collocation is addressed by the H∞ approach and convex optimization. Numerical simulations are finally provided to assess the performance of the proposed controller. Highlights • The attitude tracking control of flexible spacecraft is a multi-objective problem. • An integrated robust H∞ controller is proposed to perform attitude tracking. • The proposed controller is robust to imprecise collocation of sensors and actuators. • The system robustness is addressed by the H∞ approach and convex optimization. [ABSTRACT FROM AUTHOR]

One of the challenges of the installation of subsea equipment is the precise positioning of the equipment in a desired location on the seabed. This positioning procedure is still commonly performed manually by an operator. This article presents a trajectory tracking control system design for the positioning of a subsea equipment. This work will be done in two stages. First, a motion planning for dynamic positioning system of platform is presented. For this, an analytical solution of motion equation of the riser, which is modeled as a damped cable, is detailed. This analytical solution when compared to numerical solutions has the advantage of generating more precise and smooth trajectories for the system input and output. Second, we propose a tracking control system to ensure that the subsea equipment follows the desired trajectory in the presence of disturbances. More precisely, a linear state-feedback control law is proposed using a reduced model, which is obtained from a modal reduction with delay insertion. This delay insertion provides a high precision low order model. This fact highly increase the robustness of the control system to the measurement noise. Numerical simulations and experimental tests are presented to evaluated the proposed control strategy. [ABSTRACT FROM AUTHOR]

Microalgae are used for the industrial production of high value compounds. The aim in continuous bioreactors is to obtain the highest biomass production. It is necessary to guarantee that the bioprocesses attain and maintain the optimal reference biomass C X * ( t ), despite endogenous and exogenous disturbances. This paper describes the numerical simulation of the application of active disturbance rejection control (ADRC) to control the dilution rate ( D(t) ) in a continuous culture of the microalga Chlorella vulgaris . To reduce the bioprocess to a “SISO” system, the authors chose the dilution rate, D(t), to be the only control signal. The control proposal was illustrated and evaluated through a numerical simulation using MATLAB/Simulink™ environment. The performance of the ADRC was tested by the application of external perturbations and variation of parameters over a nominal case. At nominal conditions, D(t) was always maintained within the physical limits imposed by the bioprocess. Step and smooth type signals, at 96.4%·| D max ( t )|, were imposed as external perturbation on the control signal input, D(t) . The controller response kept the output signal C X ( t ) within an insignificant 0.0043%·| C Xmax ( t )|. The algal culture had a strongly asymmetric response to variations of the ideal maximum growth rate, μ max ( t ) ± 30%·| μ max ( t )|, and of the nominal light intensity, I in ( t ) ± 30%·| I in ( t )|. Nonetheless, the controller promptly returned the output signal to its reference value, C X ( t )*. The numerical test of the control proposal, in summary, showed that the ADRC strategy ensures excellent reference tracking capability and robustness towards parametric uncertainties, un-modeled dynamics, and external disturbances. [ABSTRACT FROM AUTHOR]

Accurate modeling plays an important role in solar cell simulation. In order to reveal the applicability and superiority of Special Trans function based single diode model (SBSDM), this paper presents a comprehensive comparison of SBSDM, Lambert W function based single diode model (LBSDM) and exponential-type single diode model (SDM). The performance difference of SBSDM, LBSDM and SDM is verified and compared in two aspects: (1) different fitness to the measured I–V data of solar cells and (2) different parameter extraction performance. To be objective and reproducible, the reported parameter values of standard datasets and measured datasets are employed to validate the fitness difference of the three models. The comparison results indicate that SBSDM always exhibits better fitness than LBSDM and SDM in representing the I–V characteristics of various solar cells and can provide a closer prediction to actual maximum power points. With the help of a ranking based branch selection strategy, a modified Nelder-Mead simplex (MNMS) algorithm is proposed to test the parameter extraction performance of SBSDM, LBSDM and SDM. The comparison results reveal that the time computational efficiency of SBSDM is inferior to SDM but superior to LBSDM. SBSDM always achieves superior accuracy and convergence speed than LBSDM and SDM, although lacking enough statistical robustness. Due to these superiorities, SBSDM is quite promising and envisaged to be the most valuable model for solar cell parameter extraction and PV system simulation. [ABSTRACT FROM AUTHOR]

Abstract The number of micro-satellites launched by commercial rockets and designed by universities and institutions is experiencing rapid growth, inevitably increasing the amount of the space debris of the same size. This paper adopts a low-thrust tethered space tug system to achieve debris deorbit. A switched low-propulsion force control method using only two constant-thrust modes to achieve both deorbit and libration control is presented. This kind of control method benefits from its robustness and low demand on the output accuracy of the thruster. The harmonic-like libration dynamics of the tethered space tug system around the local horizontal configuration is discussed and the stability of the control method is proved. Moreover, the sufficient condition for the switching sustainability is presented. Afterwards, the control effects of such a system are illustrated using numerical simulations. A modified control law to adapt practical demands shows the flexibility of the switching control methodology. [ABSTRACT FROM AUTHOR]

This paper presents an aerodynamic model-based robust adaptive control algorithm for close formation flight. The leader aircraft is assumed to be at level and straight flight which characterizes the most common scenario for close formation flight. The formation aerodynamic effects are assumed to be unknown, but an online formation aerodynamic model is available to predict those effects. In light of the online formation aerodynamic model, a robust adaptive control algorithm is thereafter developed on the follower aircraft to counteract the unknown formation aerodynamic effects and obtain highly accurate formation tracking performance. The proposed control algorithm is composed of a baseline controller and an integrator-augmented robust adaptive controller, which can efficiently deal with both matched and mismatched formation aerodynamic effects and external disturbances. The major advantage of the proposed design is that it can achieve at least ultimate bounded tracking control with certain transient performance guaranty. The efficiency and robustness of the proposed control design are eventually validated via numerical simulations of close formation flight at two different scenarios. [ABSTRACT FROM AUTHOR]

This paper investigates the relative position and attitude tracking control of non-cooperative fly-around mission in the presence of parameter uncertainties, external disturbances, input saturation and full state constraints. Firstly, an integrated and coupled 6 DOF relative motion dynamic model is established, which is consisted of relative position model depicted in the line-of-sight (LOS) frame and relative attitude model described by Modified Rodriguez parameters (MRPs). Subsequently, by using the backstepping control method, an integrated robust adaptive anti-windup control scheme is proposed, in which uncertain parameters and unknown upper bound of the disturbances are estimated by adaptive technique, and the adverse effects caused by input saturation are reduced by the designed anti-windup compensator. To guarantee the full state constraints satisfied all the time, the barrier Lyapunov function method is incorporated into the backstepping control design. Rigorous stability proofs show that the designed robust adaptive controller guarantees that the relative motion states not only can be restricted in the prescribed constraint regions, but also can converge into the small regions with good robustness. Finally, numerical simulation results demonstrate the effectiveness and performance of the designed control scheme. [ABSTRACT FROM AUTHOR]

In order to improve the reliability of wind turbines, avoid serious accidents and reduce operation and maintenance (O&M) costs, it is important to effectively detect faults of wind turbines operating in harsh environment. This paper proposes a radically data-driven fault detection and diagnosis (FDD) method for wind turbines, which implements deep belief network (DBN). The DBN requires no knowledge of physical model, instead, it employs historical data without any pre-selection. The method has been evaluated in a wind turbine benchmark simulink model, in comparison with four model-based algorithms and four data-driven methods, and the results have shown that the proposed method achieves the highest accuracy. Moreover, extensive evaluation has been taken to analyse the robustness of proposed method, and the simulation results indicate the stable performance of proposed method in faults diagnosis of wind turbine. [ABSTRACT FROM AUTHOR]

Cascade phenomenon may lead to catastrophic disasters which extremely imperil the network safety or security in various complex systems such as communication networks, power grids, social networks and so on. In some flow-based networks, the load of failed nodes can be redistributed locally to their neighboring nodes to maximally preserve the traffic oscillations or large-scale cascading failures. However, in such local flow redistribution model, a small set of key nodes attacked subsequently can result in network collapse. Then it is a critical problem to effectively find the set of key nodes in the network. To our best knowledge, this work is the first to study this problem comprehensively. We first introduce the extra capacity for every node to put up with flow fluctuations from neighbors, and two extra capacity distributions including degree based distribution and average distribution are employed. Four heuristic key nodes discovering methods including High-Degree-First (HDF), Low-Degree-First (LDF), Random and Greedy Algorithms (GA) are presented. Extensive simulations are realized in both scale-free networks and random networks. The results show that the greedy algorithm can efficiently find the set of key nodes in both scale-free and random networks. Our work studies network robustness against cascading failures from a very novel perspective, and methods and results are very useful for network robustness evaluations and protections. [ABSTRACT FROM AUTHOR]

In this paper, we propose two adaptive control schemes to solve the output tracking control problem of a class of uncertain switched nonlinear systems. The first is a robust scheme constructed based on common Lyapunov function method, aiming to achieve the insensitivity of closed-loop system to parameter switches by estimating the bound on switching parameters. It is analyzed that the system stability under arbitrary switching is established with such a scheme, but the control input required may be relatively large since the estimate of the bound on switching parameters is utilized. To avoid this, we further propose a direct adaptive control scheme by estimating the switching parameters directly, and derive a dwell-time condition for parameter switches based on an extended multiple Lyapunov functions method newly developed, such that the global boundedness of all the closed-loop signals and the convergence of tracking error to a residual around zero are ensured. The effectiveness of the proposed control schemes is illustrated through simulation studies. [ABSTRACT FROM AUTHOR]

This paper introduces an observer-based sliding mode control strategy for stabilizing the attitude of flapping micro-air vehicles in the presence of unknown uncertainties and external disturbances. First, a finite-time observer is designed to estimate the lumped disturbances. Next, a controller is developed to provide attitude stabilization with high robustness where the lumped disturbances could be completely rejected after a finite amount of time. The stability of the closed-loop system is rigorously proven. By employing MATLAB/Simulink software, a numerical simulation example including a comparison between the proposed method and a conventional sliding mode controller is provided to illustrate the effectiveness of the proposed method and to confirm the theoretical results. [ABSTRACT FROM AUTHOR]

In this paper, a new approach is presented for getting a solution of the mobile robot path planning problem based on Adaptive Particle Swarm Optimization (APSO). The proposed APSO algorithm is smarter than conventional PSO and widely used for solving the real time problems. In this work an objective function is framed considering the distance between robot to goal and obstacle respectively. The objective function is optimized with of APSO for solving the path planning process of robot. The different simulated experiments are performed to test the ability of the proposed algorithm. The performance of the robot path planning using APSO is compared to the performance of the conventional PSO in terms path length and time in static environments. It is focused that using new approach the robot can successfully avoid obstacle and reach the target with shorter time than conventional PSO. [ABSTRACT FROM AUTHOR]

This paper proposes a composite fault detection scheme for the dynamics of high-speed train (HST), using an unknown input observer-like (UIO-like) fault detection filter, in the presence of wind gust and operating noises which are modeled as disturbance generated by exogenous system and unknown multi-source disturbance within finite frequency domain. Using system input and system output measurements, the fault detection filter is designed to generate the needed residual signals. In order to decouple disturbance from residual signals without truncating the influence of faults, this paper proposes a method to partition the disturbance into two parts. One subset of the disturbance does not appear in residual dynamics, and the influence of the other subset is constrained by H ∞ performance index in a finite frequency domain. A set of detection subspaces are defined, and every different fault is assigned to its own detection subspace to guarantee the residual signals are diagonally affected promptly by the faults. Simulations are conducted to demonstrate the effectiveness and merits of the proposed method. [ABSTRACT FROM AUTHOR]

This paper describes a new, innovative method by which multiple mobile robots can be detected by a laser scanner. Each robot incorporates bars in its construction which generate a significant pattern in the laser scan. The proposed technique allows a robust detection and control of successively moving robots, despite the partial shadowing through the bars. In this paper the optimal number of bars and their best arrangement for detection is shown. Furthermore the impact of different bar diameters is described. Increased visibility of the bars by the use of multiple laser scanners and their positioning to ensure detection of the robots is also described. Finally it describes the accuracy that can be achieved with this system. The position accuracy was determined by trials on an actual system. Simulations and experiments confirm that this is a reliable and precise method for position determination of multiple robots using a single sensor. [ABSTRACT FROM AUTHOR]

In this paper, the performance of metro networks is studied from a network science perspective. We review the structural efficiency of metro networks on the basis of a passenger’s intuitive routing strategy that optimizes the number of transfers and the distance traveled.A new node centrality measure, called node occupying probability , is introduced for evaluating the level of utilization of stations. The robustness of a metro network is analyzed under several attack scenarios. Six metro networks (Beijing, London, Paris, Hong Kong, Tokyo and New York) are compared in terms of the node occupying probability and a few other performance parameters. Simulation results show that the New York metro system has better topological efficiency, the Tokyo and Hong Kong systems are the most robust under random attack and target attack , respectively. [ABSTRACT FROM AUTHOR]

The decorrelated neural network ensemble (DNNE) algorithm can be used to construct an effective soft measuring model through an analytical solution comprising submodels of multiple randomized neural networks. However, DNNE exhibits one major shortcoming: the scope of the random input weights and biases is set to a default range of [−1, 1], which cannot ensure the universal approximation capability of the resulting prediction model. The three other learning parameters including the number of the ensemble submodels’ hidden nodes, ensemble size, and regularizing factor of DNNE are also data dependent. Moreover, DNNE suffers from high computation complexity when processing a high-dimensional large-scale dataset. Feature selection can improve the interpretation of the model. To address the above-mentioned problems, a combinatorial optimization method based on the adaptive genetic algorithm is used to simultaneously optimize the input features and learning parameters of the DNNE model. The evolution processes of these modeling parameters are demonstrated in detail. Simulation results based on four datasets with different dimensions and sizes validate the effectiveness of the proposed approach. The results also indicate that the random parameter scope assignment significantly influences the generalization performance and the other learning parameters of the prediction model. [ABSTRACT FROM AUTHOR]

This paper deals with a class of neutral shunting inhibitory cellular neural networks with time-varying delays and D operator. Using the differential inequality theory and Lyapunov functional method, a set of sufficient conditions which ascertains that the existence and exponential stability of anti-periodic solutions of neutral shunting inhibitory cellular neural networks with time-varying delays and D operator are derived. Computer simulations are delineated to substantiate the correctness of our theoretical predictions. The obtained results of this paper are new and complement some earlier works. [ABSTRACT FROM AUTHOR]