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Players-based emotion recognition can help the understanding game players’ emotional states, contributing to the improvement of the game's quality and value. This article develops a hybrid neural network learning framework called convolutional smooth feedback fuzzy network (CSFFN) to detect a player's emotional states in real-time during a gaming process based on electroencephalogram (EEG) signals. Specifically, CSFFN rationally combines a convolutional neural network (CNN), a fuzzy neural network (FNN), and a recurrent neural network (RNN). CNN not only captures spatial characteristics between EEG signals from different channels but also eliminates noise from EEG signals, improving the accuracy and anti-noise performance in game emotion recognition. FNN extracts the membership degree of a player's different emotional states, further improving the emotion recognition accuracy. Since a player's current emotional state is influenced by the previous emotional states during the game process, RNN is employed to capture the temporal characteristics of EEG signals, better improving the emotion recognition accuracy. Experimental results show that CSFFN has higher recognition accuracy and noise resistance in identifying four emotional states (happiness, sadness, superiority, and anger) compared to support vector machine (SVM) with different kernels, linear discrimination analysis (LDA), AlexNet, and VGG16 methods. [ABSTRACT FROM AUTHOR]

Existing visual-based fatigue detection methods usually monitor drivers’ fatigue by capturing their facial features, including eyelid movements, yawn frequency and head pose. However, these approaches typically do not take drivers’ biological signals into consideration. An accurate model for fatigue detection requires combining both facial behavior and biological data. This paper proposes a novel non-intrusive method for driver multimodal fusion fatigue detection by extracting eyelid features and heart rate signals from the RGB video. The multimodal feature fusion method could significantly increase the accuracy of fatigue detection. Specifically, we established two fatigue detection models based on heart rate and the PERCLOS value respectively with one-dimensional Convolutional Neural Network (1D CNN), where the PERCLOS refers to the percentage of eyelid closure over the pupil. Finally, the outputs of the two models are weighted to achieve the multimodal fusion fatigue detection. Simulation results show that our method yield better performance than traditional methods. [ABSTRACT FROM AUTHOR]

Point cloud completion aims to infer the complete point clouds from incomplete ones, which is used in remote sensing applications such as reconstructing and autonomous driving. However, most existing methods cannot recover accurate structure details of the object. In this article, we propose point shift network (PS-Net). Our main contributions lie in the following three-folds. First, we propose a multiresolution encoder, which extracts and fuses multiresolution point cloud features hierarchically, thus avoiding information loss caused by a single global feature. Second, we design a multiresolution point cloud generation structure, which can be combined with the multiresolution encoder to generate gradually dense point clouds, avoiding the problem of nonuniformly density of the single-layer decoder. Third, we design the shift network (SN), which is used to generate shift vectors to shift the coordinates of each point cloud, so as to further fine-tune the coordinate positions of point clouds, achieving more accurate prediction. We conduct comprehensive experiments on the ShapeNet, KITTI, ScanObjectNN, and ModelNet40 datasets, which demonstrate that the proposed PS-Net achieves better performance than the existing methods and verify the robustness of the proposed method. This article contributes a new method to point cloud completion, realizes fine point cloud shape completion, and brings new possibilities to the research of autonomous driving, registration, and reconstruction. [ABSTRACT FROM AUTHOR]

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]

While various biomimetic robotic haptic sensors are often utilized to measure multifarious physical interactions to identify material properties under human exploratory procedures (EPs), traditional methods are unable to fuse well multimodal measurements under EPs. In order to solve this problem, an innovative hybrid joint group kernel sparse coding model for material recognition under EPs is proposed. First, a series of feature representations according to the characteristics of different measures are introduced. Second, we propose an innovative hybrid fusion framework based on statistical learning with kernel sparsity to effectively fuse the measurements from robotic EPs. Third, a matched optimization algorithm is constructed to deal with the innate optimization problem in the proposed framework. Finally, we develop an evaluation based on three different classification levels (coarse, medium, and fine) with the public dataset containing 184 material classes to verify the proposed method and compare with some other similar kernel methods. The evaluation results show that the accuracy of coarse, medium, and fine classification of the proposed hybrid fusion framework is 98.4%, 96.2%, and 98.4%, respectively. The framework can be extended easily to other fusion tasks with multiple measurements. [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]

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]

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]

Driver fatigue monitoring is very important for driving safety, and many intricate factors while driving make fatigue monitoring harder. To effectively predict driving fatigue, this article proposes a new deep learning framework called TSK-type convolution recurrent fuzzy network (TCRFN) based on the spatial and temporal characteristics of electroencephalogram (EEG) signals. In TCRFN, the convolution block is first introduced to extract spatial dependencies from EEG signals. Furthermore, since EEG noise has a strong spatial dependence, this convolutional neural networks is used to reduce the impact of noise. Additionally, a new local feedback method in fuzzy neural network is proposed to process the EEG signals, which can better capture the temporal dependencies from EEG signals. Finally, a logarithmic spatial firing layer function is used in the proposed TCRFN. The activation performance of this function is smoother, which allows more feature numbers and provides better prediction. The experimental results show that the proposed TCRFN model has better antinoise performance and prediction accuracy compared with other widely used and state-of-the-art models. [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]

In this article, a novel human–machine interface, in which two Leap Motion (LM) controllers and a coil are attached to a Cartesian platform to provide contactless electromagnetic force feedback for enhancing the accuracy and efficiency of human–robot manipulation tasks is presented. To implement such an interface, an interval Kalman filter, an improved particle filter, and a mean filter are integrated to estimate accurately the position and orientation of the hand gesture tracked by the two LM controllers, and to smoothen the movement of the Cartesian platform. The back propagation neural network is employed to regulate the electric currents of the coil attached to the Cartesian platform for accurate force feedback. A series of comparative experiments are performed, and the results show that the presented interface greatly improved the efficiency and accuracy of human–robot manipulation tasks in comparison with existing methods, indicating its great potentials for many industry scenarios. [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]

With the development of Industry 4.0, future robots will no longer be fixed on the production line for doing repetitive tasks but will be adaptable and versatile. This means that they can correct their own parameters almost instantaneously. However, traditional offline calibration methods cannot meet such requirements. This article proposes an online robot calibration method that can provide quickly identify robotic kinematic parameters without stopping the robot, thereby greatly improving the operating efficiency of the robot. The method can also play a role in improving position accuracy even if the robot works in a complicated and fickle environment. The proposed method applies an inertial measurement unit (IMU) and a position marker fixed on the robot tool to measure the robot’s orientation and position. A hybrid filter, which integrates the unscented Kalman filter (UKF) and iterative particle filter (IPF), is developed to further improve the measurement accuracy of the robotic orientation and position, respectively. UKF is also adopted to reckon the errors of the robotic kinematic parameters in order to obtain better results. Particularly, the proposed method does not cover the complicated procedures, such as image capturing or laser tracking for improving the accuracy and efficiency of kinematic identification. To testify this method, a series of experiments have been performed. The experimental results indicate that the proposed method shows autonomy and precision in the robotic calibration process. [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]

The precise tracking control problem for n-link flexible-joint (FJ) robotic systemsis addressed in this paper. A new adaptive fuzzy command filtered control strategy is presented, where fuzzy logic systems are utilized to approximate the unknown nonlinearities of FJ robot systems. Compared with existing backstepping-based methods, the proposed scheme can not only overcome the so-called “explosion of complexity” problem, but also reduce filter errors because of the introducing of an error compensation mechanism. Moreover, regardless of the number of fuzzy rules, only one parameter is required to be adjusted online, which reduces significantly the computational cost. The proposed scheme can guarantee that all the signals in the closed-loop system are semiglobally uniformly ultimately bounded, and the tracking error eventually converges to a small neighborhood around zero. The simulation results of a two-link robot system confirm our theoretical analysis and a comparison study demonstrates the advantages of the design method in comparison with existing results, such as the backstepping method and the dynamic surface control method. [ABSTRACT FROM AUTHOR]

An accurate and realistic brain tissue deformation model with real-time performance is very important for virtual neurosurgical simulation. In this paper, a new finite element method (FEM) brain tissue deformation model, which is based on the optimization implicit Euler method, is introduced. Biomechanical properties of brain tissue such as anisotropy and viscoelasticity are incorporated into the model, which provides more accurate and realistic imitation of the deformation of brain tissue. A descent method with GPU-based implementation is used to solve the optimization problem, which makes it possible to achieve a high degree of computational efficiency. Simulation results show that both the anisotropic and viscoelastic behaviors are presented in the deformation model. The GPU-based implementation of the proposed model significantly improves the computational efficiency over CPU-based FEM models with the implicit integration scheme. Moreover, the result of the proposed model converges to the exact solution of implicit Euler integration after 96 iterations. The proposed model was implemented on the development of a neurosurgical simulator. A relative high degree of realistic brain tissue deformation was rendered at a refreshment rate of 32.5 frames/s on a regular PC. [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]

With the development of Industry 4.0, future robots will no longer be fixed on the production line for doing repetitive tasks but will be adaptable and versatile. This means that they can correct their own parameters almost instantaneously. However, traditional offline calibration methods cannot meet such requirements. This article proposes an online robot calibration method that can provide quickly identify robotic kinematic parameters without stopping the robot, thereby greatly improving the operating efficiency of the robot. The method can also play a role in improving position accuracy even if the robot works in a complicated and fickle environment. The proposed method applies an inertial measurement unit (IMU) and a position marker fixed on the robot tool to measure the robot’s orientation and position. A hybrid filter, which integrates the unscented Kalman filter (UKF) and iterative particle filter (IPF), is developed to further improve the measurement accuracy of the robotic orientation and position, respectively. UKF is also adopted to reckon the errors of the robotic kinematic parameters in order to obtain better results. Particularly, the proposed method does not cover the complicated procedures, such as image capturing or laser tracking for improving the accuracy and efficiency of kinematic identification. To testify this method, a series of experiments have been performed. The experimental results indicate that the proposed method shows autonomy and precision in the robotic calibration process. [ABSTRACT FROM AUTHOR]

In this paper, we propose artificial and virtual-impedance interaction force reflection-based shared control strategy for bilateral telemanipulation of miniature aerial vehicle (MAV). The shared input for the master is designed by combining the velocity of the slave–MAV system with the scaled position–velocity of the master manipulator. The master input also uses the reflected interaction force between the slave and remote environment. The interaction force between environment and MAV is modeled by using two different approaches as the artificial and virtual impedance force field. The reflected interaction force applies to the human operators' hand through the haptic manipulator. The interaction force provides a situational awareness about the remote environment, guiding the operator to navigate and control the MAV for a safe and stable interaction with the remote environment. The shared control input for the slave is designed by combining the velocity of the MAV with the scaled position–velocity of the master manipulator. The convergence analysis of the closed-loop bilateral shared control system is shown by using the Lyapunov method. The analysis shows that the closed-loop system is input-to-state stable and ultimately bounded in the presence of passive and nonpassive interaction with the environment and the human operator. The design is implemented and evaluated on a quadrotor MAV with both artificial and virtual impedance force field based interaction interfaces to demonstrate the effectiveness for the real-time interaction with the environment. [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]