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In this article, finite-time-prescribed performance-based adaptive fuzzy control is considered for a class of strict-feedback systems in the presence of actuator faults and dynamic disturbances. To deal with the difficulties associated with the actuator faults and external disturbance, an adaptive fuzzy fault-tolerant control strategy is introduced. Different from the existing controller design methods, a modified performance function, which is called the finite-time performance function (FTPF), is presented. It is proved that the presented controller can ensure all the signals of the closed-loop system are bounded and the tracking error converges to a predetermined region in finite time. The effectiveness of the presented control scheme is verified through the simulation results. [ABSTRACT FROM AUTHOR]

Most traditional methods for extracting key points from the 3-D point cloud are based on the geometric features of points, and they pose problems such as low accuracy. In order to solve these problems, this article proposes a novel approach based on 2-D image mapping, making it able to achieve highly accurate localization of key points. Specifically, it works as follows: input images are first selected for Harris corner detection; the three pairs of marker points of the images and the point cloud are then selected to calculate the transformation matrix $T$ between them; next, the image key points are mapped onto the 3-D points through the transformation matrix $T$ , for which the extraction of key points is achieved. Experimental results show that the proposed algorithm is able to greatly improve the extraction accuracy of key points in comparison with traditional algorithms. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI)-powered autonomous vehicles (AVs) can integrate different machine learning (ML) techniques to build up a complex autonomous driving system. However, single AV intelligence is not enough to cope with ever-changing driving environments. The underlying reason is that, with current neural network design and training algorithms, it is challenging for the driving model to generalize to diverse driving environments all at once due to sample inefficiency and the curse of dimensionality. Powerful computing resources and massive amount of data can be used to train a good driving model offline. However, the driving model obtained offline might fail in corner case scenarios. In this paper, we propose an intelligence networking framework among AVs assisted by multi-access edge computing (MEC) with end-to-end learning for demonstration. In this framework, driving road is divided into segments and data is collected for each road segment separately. Assisted by MEC networks, a continuously updated driving model is produced in near real-time for each road segment when the environment changes. By dividing the road into segments, we aim to reduce the burden of generalization since a single model only needs to adapt to a specific road segment. Simulation results show that our solutions can produce updated driving model for each road segment to adapt to environmental changes better than the existing scheme. Upcoming AVs can then adapt to changing environments by downloading the updated driving model. [ABSTRACT FROM AUTHOR]

This article presents a fuzzy finite-time command filtering output feedback control method for a class of nonlinear systems. A fast convergent output feedback control algorithm based on backstepping finite-time command filtering is developed. Fuzzy logic system is used to estimate uncertain functions in nonlinear systems. A fuzzy state observer is designed to measure the unknown state. The developed finite-time command filtering feedback control method overcomes well the computational complexity problem due to the calculation of the derivatives of virtual control signals. A compensation mechanism is also introduced to compensate for the error caused by the filter. The proposed method ensures not only that all signals in the closed-loop system are finite-time bound, but also that the tracking error converges to a small neighborhood around the origin. The effectiveness of the proposed method is demonstrated in the simulation results. [ABSTRACT FROM AUTHOR]

This article investigates the stabilization control and stabilizing data-rate condition problems for networked control systems, which transmit signals from the sensor to the controller over the communication network with denial-of-service (DoS) attacks. Considering a class of DoS attacks that only constrain its frequency and duration, we aim to explore the constraint condition for stabilization and minimum stabilizing data rate of the networked control systems. The framework consists of two main parts. The first part considers the stabilizing control by the state-feedback approach under ideal bandwidth capacity. While the second part characterizes the average stabilizing data rate in terms of the eigenvalues of system matrix and DoS constraint functions to explicitly reveal the relationship between the attacks and the network bandwidth capacity. The stabilizing result is novel in the sense that the DoS-attack intensity, which is characterized by its frequency and duration, can vary for different time intervals. With this feature, the minimum average data-rate condition can vary for different time intervals according to the intensity of DoS attacks. [ABSTRACT FROM AUTHOR]

The Structure-from-Motion (SfM) algorithm is widely used for point cloud reconstruction. However, one drawback of conventional SfM based methods is that the obtained final point sets may contain holes and noise, which could degrade the estimation of reconstructed objects especially for smooth surfaces with few features. The other drawback is the accuracy and speed of SfM based methods depend on the uncertain number of images. To overcome these limitations, this paper proposes a novel 3D reconstruction method for unstructured objects based on the structure from motion in combination with the structured light, in which the point sets of structured light and the point sets of structure from motion can come from different target objects. Since the two point sets coming from multiple sensors do not scale well for register, making it difficult to find corresponding points, a scaled principal component analysis algorithm is proposed for the registration to overcome the impact due to large scale variance. With a large scale factor, a recalculated registration center is proposed via feature region segmentation to achieve point cloud registration again. The two point sets are matched using the proposed optimization method to complete 3D reconstruction. Surface reconstruction is performed using the Poisson algorithm to obtain a smooth surface. The proposed method is tested on some simple structured objects and real-life data of complex unstructured objects collected using range sensors. Compared with several state-of-the-art algorithms, experimental results confirm its potential for surface reconstruction from depth data calculated from the two sets. [ABSTRACT FROM AUTHOR]

Localization accuracy is a fundamental requirement for Simultaneous Localization and Mapping (SLAM) systems. Traditional visual SLAM (vSLAM) schemes are usually based upon the assumption of static environments, so they do not perform well in dynamic environments. While a number of vSLAM frameworks have been reported for dynamic environments, the localization accuracy is usually unsatisfactory. In this article, we present a novel motion detection and segmentation method using Red Green Blue-Depth (RGB-D) data to improve the localization accuracy of feature-based RGB-D SLAM in dynamic environments. To overcome the problem due to undersegmentation generated by the semantic segmentation network, a mask inpainting method is developed to ensure the completeness of object segmentation. In the meantime, an optical flow-based motion detection method is proposed to detect dynamic objects from moving cameras, allowing robust detection by removing irrelevant information. Experiments performed on the public Technical University of Munich (TUM) RGB-D data set show that the presented scheme outperforms the state-of-art RGB-D SLAM systems in terms of trajectory accuracy, improving the localization accuracy of RGB-D SLAM in dynamic environments. [ABSTRACT FROM AUTHOR]

In this article, the synchronization control problem of bilateral teleoperation systems with time-varying delays and input dead zones is addressed. A novel radial basis function network (RBFN)-based adaptive finite-time synchronization control scheme is proposed, where system uncertainties, asymmetric time-varying delays, and dead-zone phenomena are considered simultaneously. Specifically, an RBFN is designed to approximate system uncertainties and unknown nonlinearities. The approximation error as well as the time-associated uncertainty and the nonlinear margin of the dead zone is compensated by an adaptive compensator. Using a Lyapunov–Krasovskii function and the finite-time stability criteria, the system is proved to be semiglobally practically finite-time stable. The theoretical analysis is given to prove the stability of the closed-loop system. The tracking performance of the designed controller is demonstrated by comparative simulation studies and experiment results. [ABSTRACT FROM AUTHOR]

This paper addresses the synchronization control problem of leader–follower multiagent systems with each follower described by a class of high-order nonlinear multiple-input–multiple-output (MIMO) dynamics in the presence of time delays and actuator faults. A distributed synchronization scheme with guaranteed synchronization performance based on the radial basis function neural network (RBF NN) is introduced. We propose an augmented quadratic Lyapunov function by incorporating the lower bounds of control gain matrices and the actuator healthy indicator, and the problems caused by the unknown time-varying control gain matrices, actuator faults, and coupling terms among agents are solved. Meanwhile, the output of followers can track that of the leader and the steady state, and the transient performance of synchronization can be guaranteed, while all the other signals in the closed-loop system are guaranteed to be bounded. Finally, numerical analysis has been carried out to verify the effectiveness of the proposed controller. [ABSTRACT FROM AUTHOR]

In surgical simulation, the Navier–Stokes (N–S) equation is commonly employed to imitate the physical characteristics of bleeding and the smooth particle hydrodynamics (SPHs) algorithm is applied to solve the numerical solution of the N–S equation. However, blood is viscous, incompressible and non-Newtonian fluid whose physical properties cannot be fully incorporated by the simple N–S equation, and the kernel approximation of the SPH algorithm may lead to both edge and volume distortions plus high computational cost. In this paper, both the tension force and the effect of platelets on the viscous force of bleeding particles are incorporated into the N–S equation in order to render more realistic visual effect and biological features of bleeding in surgical simulation. Constant core radius of the kernel function of the SPH algorithm is substituted with a function of particle density, avoiding potential edge distortions in simulating bleeding area. A repulsive force between particles is introduced, which effectively prevents volume distortions. Besides, accelerated search for particles based on the cube mesh improves the computational efficiency. The simulation results show that the presented simulation method leads to smooth bleeding surface and improves the visual effects of edge and volume in comparison with existing methods, and relatively high computational efficiency can be achieved as well. [ABSTRACT FROM AUTHOR]

This paper addresses the finite-time tracking control problem for a class of nonaffine nonlinear systems with unknown dead zones using an adaptive fuzzy control scheme. The unknown nonlinear functions of the system are approximated by the fuzzy logic systems and a finite-time stability theorem is used to construct the control signal. The novelty of the proposed control method is that the tracking system can reach the stable equilibrium within a finite period of time without the knowledge of the boundaries of the dead zone parameters. The simulation results show that the system is semi-global practical finite-time stable and the tracking deviation converges to a small neighborhood of zero in a finite period of time. [ABSTRACT FROM AUTHOR]

The iterative closest point (ICP) algorithm is one of the most widely used methods for point sets’ registration. However, ICP is very sensitive to the selection of initial points and is easy to fall into local optimum. To address this problem, many techniques have been developed. In this study, a two-step registration method is proposed for two 3-D point sets’ registration, which is achieved by a combination of rough and fine registrations. Specifically, a multidiscriminant parameter feature (MDPF) extraction approach is developed and embedded into the rough registration stage in order to find new corresponding point pairs for the fine registration. Three geometric features are chosen after experimental investigation for key points selection. By using the threshold discriminant condition to determine the key points and the distance constraint to eliminate the wrong point pairs, the feature points can be extracted, and the final transform parameters can be derived based on these feature points. In order to improve the computational efficiency for fine registration, the center of gravity is created to find the closest point in solving the transformation matrix, which is especially beneficial for registering complex surfaces. Experimental results show that the proposed method outperforms the traditional ICP approach and some typical existing improved algorithms in terms of the root-mean-square error (RMSE), the total number of feature points, and the execution time. In particular, the performance is improved 40% in terms of the RMSE and 50% in terms of the execution time in comparison with ICP on some benchmark data sets. Experiments also demonstrate that reliable reconstruction results can be obtained for both real outdoor and indoor environments. [ABSTRACT FROM AUTHOR]

Steerable bevel-tip needles offer higher maneuverability independent of insertion depth and consequently are preferred for many needle-steering applications compared with symmetric-tip needles. Using these needles, the clinician can reach previously inaccessible targets using traditional stiff needles, thus helping improve the efficiency of needle insertion procedures significantly. However, due to their nonholonomic kinematics inside biological tissue, path planning of these needles is complicated and requires a great deal of care. Rapidly exploring random-tree (RRT)-based approaches are proper candidates for intraoperative planning of needle motion due to their fast computation and simple implementations. They also work well in high-dimensional configuration spaces and under nonholonomic kinematic constraints, both of which are the characteristics of steerable bevel-tip needle motion inside soft tissue. We developed a new heuristic-based RRT planner to reach multiple targets inside soft tissue without having to completely retract, reorient, and reinsert the needle toward each separate target, resulting in significantly less tissue damage compared with the conventional sequential insertion of the needle toward each target. Moreover, the proposed planner can have real clinical applications, where the limited size of the workspace as well as the needle’s limited natural curvature imposes significant limitations on the needle path-planning problem inside soft tissue. Simulations demonstrate the efficiency of the proposed planner. The maximum targeting error of all targets is 1 mm and the needle’s inserted length is decreased up to 35% compared with the sequential insertion of the needle for each target. [ABSTRACT FROM AUTHOR]

This paper addresses the trajectory tracking control problem of a class of nonstrict-feedback nonlinear systems with the actuator faults. The functional relationship in the affine form between the nonlinear functions with whole state and error variables is established by using the structure consistency of intermediate control signals and the variable-partition technique. The fuzzy control and adaptive backstepping schemes are applied to construct an improved fault-tolerant controller without requiring the specific knowledge of control gains and actuator faults, including both stuck constant value and loss of effectiveness. The proposed fault-tolerant controller ensures that all signals in the closed-loop system are semiglobally practically finite-time stable and the tracking error remains in a small neighborhood of the origin after a finite period of time. The developed control method is verified through two numerical examples. [ABSTRACT FROM AUTHOR]

This paper is initiated by considering an emerging practical issue that dc microgrids should be able to operate with a large-signal stability sense when feeding both resistive loads and constant power loads (CPLs). To be more specific, the stability should be ensured in the presence of large variations of integrated renewable sources and CPLs, system internal uncertainties, external disturbances, coupled interactions, and other adverse effects. From a control point of view, we intentionally propose a general solution to realize the exact decentralized tracking control task for interconnected systems. First, an alternative finite-time feedforward decoupling mechanism is presented, which is essentially different from existing design approaches via feedback domination or recursive cancellation processes. Second, a composite controller can be straightforwardly built from the system information since it is detached from stability analysis. One major advantage of the proposed design framework is that it reduces the design complexity and therefore facilitates the practical implementations. As a direct application, a simple decentralized composite controller is constructed for an autonomous dc microgrid system. Both numerical simulation and experimental comparison results show that a large-signal stability is achieved for dc microgrids under a range of different situations. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive neural control approach for nonstrict-feedback nonlinear systems in presence of unmodeled dynamics, unknown control directions and input dead-zone nonlinearity. To handle the difficulty due to uncertain control directions, Nussbaum gain functions are applied. Based on the structural characteristic of radial basis function neural networks, a backstepping-based adaptive neural control algorithm is developed. The main contributions of this paper lie in the fact that a backstepping-based neural control algorithm is developed for nonstrict-feedback nonlinear systems with unmodeled dynamics, unknown control directions and actuator dead-zone, and the total number of adaptive laws is not greater than the order of control system. As a beneficial result, the controller is much easier to be implemented in practice with less computational burden. A simulation example is given to reveal the viability of the presented approach. It is demonstrated by both theoretical analysis and simulation study that the presented control strategy ensures the semiglobally uniform ultimate boundedness of all closed-loop system signals. [ABSTRACT FROM AUTHOR]

This paper is concerned with the trajectory tracking control problem of a class of nonaffine stochastic nonlinear switched systems with the nonlower triangular form under arbitrary switching. Fuzzy systems are employed to tackle the problem from packaged unknown nonlinearities, and the backstepping and robust adaptive control techniques are applied to design the controller by adopting the structural characteristics of fuzzy systems and the common Lyapunov function approach. By using Lyapunov stability theory, the semiglobally uniformly ultimate boundness in the fourth-moment of all closed-loop signals is guaranteed, and the system output is ensured to converge to a small neighborhood of the given trajectory. The main advantages of this paper lie in the fact that both the completely nonaffine form and nonlower triangular structure are taken into account for the controlled systems, and the increasing property of whole state functions is removed by using the structural characteristics of fuzzy systems. The developed control method is verified through a numerical example. [ABSTRACT FROM AUTHOR]

An integrated model-based control and scheduling scheme is proposed for the load frequency control (LFC) of large-scale power systems under the distributed structure and uncertainties. Specifically, the limited bandwidth constraint is considered when state observation is exchanged over shared communication networks. Each area controller uses the explicit models of its own and neighboring areas to predict state observations when the actual one is not available. At each transmission instant, the state observation of the scheduled area is broadcasted to the relevant areas and the model-based controllers are partially updated. By properly scheduling the transmission sequence and intervals, the stability of the power system can be guaranteed with a substantial reduction of the bandwidth usage and this is proven by performing a thorough theoretical analysis. Simulation results of a four-area power system verify that the proposed distributed model-based control scheme integrated with a proper scheduling strategy can greatly enhance the performance and the resiliency to parameter uncertainty in large-scale power systems. [ABSTRACT FROM PUBLISHER]

The state-feedback decentralized stabilization problem is considered for interconnected nonlinear systems in the presence of unmodeled dynamics. The functional relationship in affine form between the strong interconnected functions and error signals is established, which makes backstepping-based fuzzy control successfully generalized to strong interconnected nonlinear systems. By combining adaptive control with both backstepping design and the approximation property of fuzzy systems, an adaptive decentralized control algorithm is developed. It is demonstrated by both theoretical analysis and simulation study that the proposed control strategy ensures semiglobally uniformly ultimately bounded of all signals within the closed-loop systems. [ABSTRACT FROM PUBLISHER]

In this paper, we propose an observer-based adaptive output feedback flight tracking system for the miniature aerial vehicle (MAV) in the presence of bounded uncertainty. The proposed design has two parts. First, a state feedback based nonlinear adaptive control algorithm is designed by assuming that all the states are available for feedback. The convergence analysis with the state feedback based design is derived by using the Lyapunov method. Second, we replace the unknown velocity states by a linear observer to develop adaptive output feedback flight tracking system for the MAV. The convergence analysis with the observer-based output feedback design is shown by using a singularly perturbed method. The analysis shows that the performance achieved under state feedback can be recovered by using an output feedback based design. Evaluation results are given to demonstrate the effectiveness of the proposed design for real-time applications. [ABSTRACT FROM PUBLISHER]

This paper considers the master and slave synchronization control for bilateral teleoperation systems with time delay and backlash-like hysteresis. Based on radial basis functions neural networks’ approximation capabilities, two improved adaptive neural control approaches are developed. By Lyapunov stability analysis, the position and velocity tracking errors are guaranteed to converge to a small neighborhood of the origin. The contributions of this paper can be summarized as follows: 1) by using the matrix norm established using the weight vector of neural networks as the estimated parameters, two novel control schemes are developed and 2) the hysteresis inverse is not required in the proposed controllers. The simulations are performed, and the results show the effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]

While the RGB2GRAY conversion with fixed parameters is a classical and widely used tool for image decolorization, recent studies showed that adapting weighting parameters in a two-order multivariance polynomial model has great potential to improve the conversion ability. In this paper, by viewing the two-order model as the sum of three subspaces, it is observed that the first subspace in the two-order model has the dominating importance and the second and the third subspace can be seen as refinement. Therefore, we present a semiparametric strategy to take advantage of both the RGB2GRAY and the two-order models. In the proposed method, the RGB2GRAY result on the first subspace is treated as an immediate grayed image, and then the parameters in the second and the third subspace are optimized. Experimental results show that the proposed approach is comparable to other state-of-the-art algorithms in both quantitative evaluation and visual quality, especially for images with abundant colors and patterns. This algorithm also exhibits good resistance to noise. In addition, instead of the color contrast preserving ratio using the first-order gradient for decolorization quality metric, the color contrast correlation preserving ratio utilizing the second-order gradient is calculated as a new perceptual quality metric. [ABSTRACT FROM PUBLISHER]

Integrating distributed energy storage systems (DESSs) into the distribution system can facilitate the high-level penetration of renewable energy source-based distributed generations (RES-DGs). To mitigate irregularly time-varying power outputs from RES-DGs, supervisory controllers of DESSs need to allocate corresponding power set points for DESS inverter primary controllers. Consequently, operating conditions of DESS inverters determined by allocated power set points could suddenly change over a wide range and seriously threaten the stability of DESSs. To guarantee DESSs operate stably over a wide range of conditions, this paper proposes a stochastic stability enhancement method embedded in the DESS grid-connected dc-ac inverter controllers. Specifically, by considering the stochastic change of DESS operating conditions as a Markov chain process, the small-signal expression of the distribution system is modeled as a Markov jump linear system (MJLS). Based on this MJLS model, a mode-dependent supplementary controller is developed for the DESS grid-connected dc-ac inverter by solving a set of linear matrix inequalities. This controller can adjust its control gains according to the measured real-time operating mode to enhance the stochastic stability of the distribution system. Studies of the Canadian urban benchmark distribution system have shown the proposed controller can enable the distribution system operate in a stable manner when its operating condition stochastically varies. [ABSTRACT FROM PUBLISHER]

This paper is concerned with the observer-based fuzzy output-feedback control for stochastic nonlinear multiple time-delay systems. On the basis of the consistent form of virtual input signals and increasing characteristics of the system upper bound functions, a variable splitting technique is employed to surmount the difficulty occurred in the nonlower-triangular form. In the controller design procedure, a state observer is first designed, and then an adaptive fuzzy output-feedback control method is presented by combining backstepping design together with fuzzy systems’ universal approximation capability. The proposed adaptive controller guarantees the semi-global boundedness of closed-loop system trajectories in terms of fourth-moment. Two simulation examples are displayed to demonstrate the feasibility of the suggested controller. [ABSTRACT FROM PUBLISHER]

Adaptive neural intelligent control is investigated for a class of pure-feedback nonlinear time-delay systems with unmodeled dynamics in nonlower-triangular form, which views the lower-triangular structure as a special structure. A variable partition technique is applied to surmount the difficulty in the nonlinear functions of whole state variables. By utilizing the backstepping recursive design approach and the universal approximation capability of neural networks, an adaptive neural controller is systemically designed. Then, based on the utilization of Lyapunov–Krasovskii functionals, the semiglobally uniform boundedness of all closed-loop signals is guaranteed. Finally, the suggested control method is verified through a numerical example. The main advantage of this paper is that an intelligent control method is developed for pure-feedback nonlinear systems with state time delay, unmodeled dynamics and nonlower triangular form. Further developments will focus on how to deal with the problem of output feedback control of pure-feedback nonlinear time-delay systems with unmodeled dynamics and nonlower-triangular structure. [ABSTRACT FROM AUTHOR]

A stability-enhancement inverter controller is proposed and its impact on the small-signal stability of distribution systems with multiple photovoltaic-based distributed generators (PV-DGs) is investigated. In specific, the random variation of the power output from multiple PV-DGs is considered. Being different from a single grid-connected PV system, the spatial correlation between PV-DGs and the system stability sensitivity with respect to different PV-DGs among the feeder are also taken into account. The small-signal stability analysis of the distribution system is performed using a probabilistic approach. As the PV-DG power output is randomly variable, the distribution system is not operating in a fixed mode. Consequently, the critical eigenvalues of the system move randomly. Through the Gram–Charlier expansion method, the probability density function of the critical eigenvalues of the distribution system is approximated and the probabilities of the system stability and instability are calculated. Taking the probability of the system instability as the performance metric, the effects of different system parameter settings of the grid-interfacing inverter controller on the distribution system are analyzed. The verification of the analytical results is carried out through Monte-Carlo time-domain simulations of the distribution system. The results suggest several methods to reduce the probability of system instability, including installation of the proposed inverter controller, choosing a large substation capacitor and/or choosing large capacity load and substation transformers. [ABSTRACT FROM AUTHOR]

As the penetration level of photovoltaic (PV) generators into the grid is rapidly increasing, the effect of a variable PV power output on the stability of power systems cannot be ignored. Due to the stochastic characteristics of PV power generation, deterministic analysis approaches are not able to fully reveal the impact of high-level PV integration. This paper investigates the impact of the stochastic PV generation on the dynamic stability of grid-connected PV systems by using a probabilistic small-signal analysis approach. The sensitivity of the critical eigenvalue to the variation of solar irradiance is obtained. With the knowledge of the sensitivity relationship and the statistics of solar irradiance data, the probability density function (pdf) of the real part of the critical eigenvalue is approximated by Gram–Charlier expansion. This pdf is then used to calculate the probability of the stochastic small-signal stability of a power system. The impacts of important system parameters on the stochastic stability of the system are also analyzed. It has been found that these system parameters can significantly affect the stochastic stability of the system. Results of Monte Carlo and time-domain simulations of the grid-connected system verify the effectiveness of the proposed stochastic stability analysis method. [ABSTRACT FROM PUBLISHER]

Low-bandwidth communication channels are used to support the information exchange between a microgrid centralized controller and local controllers in the secondary frequency control of an islanded microgrid. However, the impact of the inherent time delay in these communication channels on the microgrid performance has not been taken into account when the secondary frequency controller is designed. This paper investigates the effect of the communication delays on the secondary frequency control of an islanded microgrid with multiple distributed generators. A small-signal model-based method is introduced for the microgrid to find delay margins below which the microgrid can remain stable. By performing a series of trial studies, the relationships between secondary frequency control gains and delay margins are obtained. A gain scheduling approach is also proposed to compensate the effect of the communication delay on the secondary frequency control. Results from the Canadian urban distribution system have verified that communication delays can adversely affect the microgrid secondary frequency control, and the proposed gain scheduling approach can improve the robustness of the microgrid secondary frequency controller to communication delays. [ABSTRACT FROM PUBLISHER]