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In this paper, an event‐driven‐observer‐based distributed adaptive sliding mode fault compensation control method is presented for a class of nonlinear interconnected systems in the occurrence of actuator faults and disturbances. First, a novel event‐based estimator is designed to acquire the state, fault, and lumped disturbance information. Then, by utilizing the observer outputs, an adaptive tracking‐error‐based event condition is established, based on which a distributed sliding mode fault compensation controller is designed. In the proposed control framework, on the one hand, to enhance the robustness and tracking accuracy, two adaptive parameters are constructed; on the other hand, an adaptive event‐based control mechanism is integrated to save the transmission resources. The Zeno phenomenon avoidance performance is analyzed and, finally, the applications to an inverted pendulum system and a machine power system are given to verify the fault tolerance ability of the proposed method. [ABSTRACT FROM AUTHOR]

Multi-camera-based simultaneous localization and mapping (SLAM) has been widely applied in various mobile robots under uncertain or unknown environments to accomplish tasks autonomously. However, the conventional purely data-driven feature extraction methods cannot utilize the rich semantic information in the environment, which leads to the performance of the SLAM system being susceptible to various interferences. In this work, we present a semantic-aware multi-level information fusion scheme for robust global orientation estimation. Specifically, a visual semantic perception system based on the synthesized surround view image is proposed for the multi-eye surround vision system widely used in mobile robots, which is used to obtain the visual semantic information required for SLAM tasks. The original multi-eye image was first transformed to the synthesized surround view image, and the passable space was extracted with the help of the semantic segmentation network model as a mask for feature extraction; moreover, the hybrid edge information was extracted to effectively eliminate the distorted edges by further using the distortion characteristics of the reverse perspective projection process. Then, the hybrid semantic information was used for robust global orientation estimation; thus, better localization performance was obtained. The experiments on an intelligent vehicle, which was used for automated valet parking both in indoor and outdoor scenes, showed that the proposed hybrid multi-level information fusion method achieved at least a 10-percent improvement in comparison with other edge segmentation methods, the average orientation estimation error being between 1 and 2 degrees, much smaller than other methods, and the trajectory drift value of the proposed method was much smaller than that of other methods. [ABSTRACT FROM AUTHOR]

This paper studies the problem of fuzzy output control of discrete-time nonlinear systems with immeasurable state variables, time-varying delay, and data packet dropout. A T–S fuzzy model is used to represent the original nonlinear systems with time-varying delay, and the Bernoulli distribution is employed to describe the dropout process of the random packet. The two uncertainties of time delay and data packet dropout are considered in the system design. By constructing a fuzzy Lyapunov–Krasovskii functional and introducing a summation inequality based on Cauchy–Schwartz form, the existence condition of fuzzy static output feedback controller is established by using linear matrix inequalities (LMIs). The simulation example verifies the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, the nonlinear disturbance observer (NDO) based dynamic surface control (DSC) with interval type-2 fuzzy neural network (IT2FNN) approximator is proposed for flexible-joint manipulator with the input saturation and unknown nonlinear disturbance. The DSC technique has tremendous advantages in eliminating the 'explosion of complexity' problem. The IT2FNN approximator is used to deal with parameter uncertainties. The NDO is applied to estimate the unknown external disturbance and compensate the saturation constrain. From Lyapunov stability analysis, it is proved that with the proposed control scheme, all signals of the closed-loop system are semiglobally uniformly ultimately bounded. Simulation results are carried out to demonstrate the effectiveness of the proposed scheme. Compared with the adaptive DSC with neural network (NN) approximator and type-1 fuzzy (T1F) approximator, the tracking error of the proposed control scheme converges to a sufficiently small value. [ABSTRACT FROM AUTHOR]

• It is the first time to discuss the exponential stability analysis for DTQVNNs with leakage delay and discrete time-varying delays by employing Lyapuinov-Krasovskii functional and matrix inequality techniques. • The exponential stability of DTQVNNs is investigated directly rather than by real decomposition method or plural decomposition method. To obtain the results of stability, a new quaternion-valued linear matrix inequality(QVLMI) is provided. • The existence, uniqueness and exponential stability criteria are proposed in the form of QVLMI and complex- valued linear matrix inequalities(CVLMIs). In this paper, we consider the issue of exponential stability analysis for discrete-time quaternion-valued neural networks(DTQVNNs) with leakage delay and discrete time-varying delays. The DTQVNNs are investigated directly rather than through real decomposition method or plural decomposition method. Firstly, the existence and uniqueness of the equilibrium point for DTQVNNs are proposed by using homeomorphic mapping theorem and Cauchy-Schwarz inequality. Then, based on Lyapuinov-Krasovskii functional and matrix inequality, a delay-dependent sufficient condition is provided to guarantee the exponential stability of the equilibrium point for DTQVNNs. Finally, a numerical example is utilized to illustrate the effectiveness of the obtained results. [ABSTRACT FROM AUTHOR]

Since the non-singleton fuzzy logic controllers (NFLCs) can effectively reflect the uncertainty brought by the inputs, they are used for balance control and position control of the mobile two-wheeled self-balancing robot (MTWSBR) in this paper. The similarity between the inputs and the antecedent fuzzy sets as the firing strength, that is, the similarity-based non-singleton fuzzy logic controller (Sim-NFLC), is proposed to deal with the problem of information loss caused by the standard non-singleton fuzzy logic controller (Sta-NFLC) in terms of the interaction of the inputs and antecedents. A comparative study among singleton fuzzy logic controllers (SFLCs), Sta-NFLCs and Sim-NFLCs, and interval type-2 fuzzy logic controllers (IT2FLCs) and general type-2 fuzzy logic controllers (GT2FLCs) are also shown. The simulation results show that the performance of Sim-NFLCs is better than that of SFLCs and Sta-NFLCs. The similarity-based general type-2 fuzzy logic controller (Sim-NGT2FLC) gets the best performance in handling the input uncertainty. [ABSTRACT FROM AUTHOR]

Finite-time stability and stabilization problems for a class of interval type-2 (IT2) fuzzy time-delay systems are studied. In this paper, we extend the concept of finite-time stability to IT2 fuzzy time-delay systems. Based on the Lyapunov stability method, integral inequality and some advanced matrix inequalities, a sufficient condition is proposed to guarantee finite-time stability of IT2 fuzzy time-delay systems. Then, by virtue of the results on finite-time stability and Finsler's lemma, we propose an IT2 fuzzy state feedback controller which can guarantee the closed-loop system is finite time stable. The problem of finite time stabilization can be solved with con complementarity linearization iterative algorithm. Finally, two numerical examples are provided to verify the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

A gain scheduled dynamic surface control (GSDSC) based on neural network disturbance observer (NNDOB) is developed for a class of uncertain underactuated mechanical systems with multiple equilibria. Equilibrium manifold linearization (EML) transforms the nonlinear system into an equivalent model using scheduling variables. The NNDOB with a filtering adaptive law is constructed to approximate arbitrary disturbances. The dynamic surface controller is designed based on the NNDOB and EML model, which efficiently solves the mismatches and overcomes the problem of “explosion of complexity” resulting from repeated differentiation of virtual control variables. Moreover, the robust stability of the closed-loop system is proved by Lyapunov stability theorem. Finally, we apply the proposed control scheme to a power line inspection robotic system. The simulation results illustrate the approximation capability of NNDOB and the effectiveness of the presented control scheme. [ABSTRACT FROM AUTHOR]

This paper concerns the issue of stabilization and stability analysis for Takagi–Sugeno (T–S) fuzzy systems with time delay. A new type of Lyapunov–Krasovskii functional (LKF), including Non-Quadratic Lyapunov functional and triple integral term, is introduced to obtain stability conditions of fuzzy time-delay systems. A Wirtinger-based double integral inequality is used to estimate integral term, and the free weighting variable technique is also employed for controller syntheses and stability analysis. Additionally, less conservative and newer stability conditions with delay-dependent are proposed in the form of linear matrix inequalities (LMIs). Furthermore, Several examples are provided to illustrate how effective the suggested approaches are. [ABSTRACT FROM AUTHOR]

Hydropower station is vital for the stable growth of the national economy. How to timely warn the possible faults of hydropower stations has become an increasingly popular research topic. The traditional detection model is difficult to detect the small abnormal changes in the data, and these abnormal changes are often the precursor of faults. To improve the sensitivity of the traditional detection model, this study introduced a weight factor into the traditional LSTM detection model. By using the correction mechanism, the LSTM correction model makes the prediction model never deviate from the normal track following the appearance of abnormal data. This ensures that the model can generate large residuals after abnormal data occur so that we can detect these abnormal data in time. Finally, this paper puts forward two factors related to equipment health and integrates these two factors to form a health index. The results show that the LSTM correction model based on the health index can not only detect small changes that cannot be detected by traditional detection models but also knows the wear and tear of equipment during operation based on the changes in health indicators. [ABSTRACT FROM AUTHOR]

Deep Reinforcement Learning (DRL) algorithms have been widely studied for sequential decision-making problems, and substantial progress has been achieved, especially in autonomous robotic skill learning. However, it is always difficult to deploy DRL methods in practical safety-critical robot systems, since the training and deployment environment gap always exists, and this issue would become increasingly crucial due to the ever-changing environment. Aiming at efficiently robotic skill transferring in a dynamic environment, we present a meta-reinforcement learning algorithm based on a variational information bottleneck. More specifically, during the meta-training stage, the variational information bottleneck first has been applied to infer the complete basic tasks for the whole task space, then the maximum entropy regularized reinforcement learning framework has been used to learn the basic skills consistent with that of basic tasks. Once the training stage is completed, all of the tasks in the task space can be obtained by a nonlinear combination of the basic tasks, thus, the according skills to accomplish the tasks can also be obtained by some way of a combination of the basic skills. Empirical results on several highly nonlinear, high-dimensional robotic locomotion tasks show that the proposed variational information bottleneck regularized deep reinforcement learning algorithm can improve sample efficiency by 200–5000 times on new tasks. Furthermore, the proposed algorithm achieves substantial asymptotic performance improvement. The results indicate that the proposed meta-reinforcement learning framework makes a significant step forward to deploy the DRL-based algorithm to practical robot systems. [ABSTRACT FROM AUTHOR]

• The path planning and path smoothing are combined through the high-order continuous Bezier curve. • An improved QPSO that incorporates two search mechanisms from bald eagle search (BES) is proposed. • The concept of semi-randomization is infused in some of our modifications in BES-MQPSO. • Enhanced intensive exploitation is performed by incorporating BES search and swoop mechanisms. • It may be the first time to apply BES tricks to help to enhance QPSO and into the path planning field. The design of path planning algorithms to obtain a smooth path that satisfies constraints such as obstacle avoidance, minimized cost, and dynamic feasibility for mobile robots in the workspace is a hot issue in the field of intelligent robotics. The metaheuristic optimization algorithm, which aims to solve path planning problems, is more efficient compared to traditional algorithms. In the current metaheuristic approaches, avoiding local minimum traps is the core goal of the scheme. In this paper, with the benefit of a high-order continuous Bezier curve, we construct an optimization problem of smooth path planning based on the length and the requirement of collision-free safety as constraints of the robot's expected path. Then, the smooth path planning problem is transformed into an optimization problem that searches the locations of control nodes of the Bezier curve. We solve the constructed path planning problem with our modified quantum particle swarm optimization algorithm (QPSO). Specifically, to overcome the shortcomings of the current PSO and QPSO-based particle swarm algorithms like prematurity, unbalanced global search, local exploitation, and local minima, we implement an improved QPSO algorithm denoted as BES-MQPSO that incorporates the search and swoop mechanisms from bald eagle search (BES) strategies. The method consists of four improved modifications: an improved local attractor, improved feature length, BES-enhanced intensive exploitation Strategy, and random variation mechanism. Our proposed BES-MQPSO with these four modifications is tested with benchmark functions and then applied to the smooth path planning problem. Finally, the superiority of BES-MQPSO is confirmed, and the ideal smooth path of the mobile robot is successfully planned. The planned path length is reduced by 4.45% and 2.82% compared to e-QPSO and SDEQPSO respectively, while the standard deviation is reduced by 67.46% and 69.82%, respectively. [ABSTRACT FROM AUTHOR]

Insulator inspection is important for high-voltage transmission line safety. However, existing object-detection algorithms cannot complete the task robustly in complex aerial environments. To improve the performance of the detection task, this paper proposes an algorithm, called Faster R-Transformer, which combines the advantage of Convolutional neural networks and the self-attention mechanism for aerial insulator detection. First, the D-ResNet performs feature extraction to obtain a larger receptive field. Then, the RPN module is used for region recommendation, and the Align Pooling module is used for region Pooling. Finally, the self-attention mechanism is adopted in the proposed region to perform local region feature weighting. The Faster R-Transformer was evaluated in complex aerial environments, and the evaluation showed that the proposed method was capable of detecting insulators of various sizes under different illuminations and viewpoints. We also explored hyperparameters of Faster R-Transformer to meet the requirements of various scenarios. [ABSTRACT FROM AUTHOR]

In order to overcome the influence of model uncertainty and external disturbance on the trajectory tracking accuracy of four-wheel omnidirectional mobile robot (FM-OMR), a new adaptive trajectory tracking control scheme based on interval type 2 fuzzy neural network approximator (IT2FNNA) is proposed in this paper. Based on the kinematics and dynamics model of FM-OMR, a dual loop trajectory controller is constructed. To improve the adaptive ability of IT2FNNA, an adaptive adjustment method for the network output is proposed. In addition, to improve the approximation accuracy of the IT2FNNA, a bias strategy is proposed based on the FM-OMR model. Finally, the circle track and "8" track are simulated and tested. The simulation and experimental results show that the bias strategy and adaptive factor really improve the approximation ability and accuracy of IT2FNNA, and the proposed adaptive control scheme has smaller fluctuations, higher accuracy and better steady-state performance. [ABSTRACT FROM AUTHOR]

Loop closure detection (LCD) is an indispensable part of simultaneous localization and mapping (SLAM) systems, making it significant for eliminating accumulative trajectory error and constructing a consistent map. Focusing on visual SLAM, we propose a LCD method using local-global similarity measurement strategies. As with most deep learning-based methods, we treat the LCD problem as a classification problem. We use a pretrained VGG-16 network to obtain advanced features and a multiscale feature extraction module to integrate the features of different receptive fields. Finally, we overcome the problems of dynamic object occlusion and large-area repetitive texture using a local-global similarity measurement module to measure the similarity between current and historical frames in all dimensions. This approach decouples feature extraction from classification determination, which enables us to use the advantages of both supervised and unsupervised learning to cope with the imbalance in the number of nonloop and loop pairs. Experimental results on three different datasets showed that the proposed method is significantly better than the state-of-the-art appearance-based LCD methods in dynamic urban, suburban, and highway scenarios. [ABSTRACT FROM AUTHOR]

To overcome the difficulty in tracking the trajectory of an inspection robot inside a transformer, this paper proposes a distributed model predictive control method. First, the kinematics and dynamics models of a robot in transformer oil are established based on the Lagrange equation. Then, by using the nonlinear model predictive control method and following the distributed control theory, the motion of a robot in transformer oil is decoupled into five independent subsystems. Based on this, a distributed model predictive control (DMPC) method is then developed. Finally, the simulation results indicate that a robot motion control system based on DMPC achieves high tracking accuracy and robustness with reduced computing complexity, and it provides an effective solution for the motion control of robots in narrow environments. [ABSTRACT FROM AUTHOR]

In this paper, a novel general type-2 fuzzy sliding mode controller for motion balance adjusting of a power-line inspection robot is developed. The combination method between general type-2 fuzzy system and sliding mode control is different from the existing works. In this paper, sliding mode control is used to finish the motion balance adjusting work. The sliding mode surface is designed according to the Ackermann's formula. And the general type-2 fuzzy system is used to strengthen the anti-interference ability of the power-line inspection robot. The general type-2 fuzzy sliding mode controller is developed by substituting an element of the sliding mode control law by the output of general type-2 fuzzy system. The novel combination method makes it possible to enhance the anti-interference ability of PLI robot while achieving motion balance control. According to the simulation results, the effectiveness of the proposed controller is proved. And it can also be known that the proposed method is not limited to the GT2FS, and its IT2FS and T1FS counterpart can also strengthen the anti-interference ability in different level. [ABSTRACT FROM AUTHOR]

In this paper, we propose a method to assess the collision risk and a strategy to avoid the collision for solving the problem of dynamic real-time collision avoidance between robots when a multi-robot system is applied to perform a given task collaboratively and cooperatively. The collision risk assessment method is based on the moving direction and position of robots, and the collision avoidance strategy is based on the artificial potential field (APF) and the fuzzy inference system (FIS). The traditional artificial potential field (TAPF) has the problem of the local minimum, which will be optimized by improving the repulsive field function. To adjust the speed of the robot adaptively and improve the security performance of the system, the FIS is used to plan the speed of robots. The hybridization of the improved artificial potential field (IAPF) and the FIS will make each robot safely and quickly find a collision-free path from the starting position to the target position in a completely unknown environment. The simulation results show that the strategy is effective and useful for collision avoidance in multi-robot systems. [ABSTRACT FROM AUTHOR]

In this paper, adaptive general type-2 fuzzy neural network control for motion balance adjusting of a power-line inspection robot is developed. It is used to enhance the anti-interference ability of the controlled plant. General type-2 fuzzy system is adopted because of its ability to more effectively handle uncertainties which may exist as external disturbances and parameter perturbations. The structure of general type-2 fuzzy system is designed by mimicking the neural network. The adaptive laws can be obtained based on the sliding mode control theory. This provides a kind of dynamic general type-2 fuzzy system whose membership functions and consequent parts are changing adaptively. The proposed controller is used to control an under-actuated non-linear power-line inspection (PLI) robot. Different simulation conditions are considered to test the anti-interference ability of the proposed controller. Simulation results indicate that the proposed method can strengthen the PLI robot's anti-interference ability in a better way as compared to its interval type-2 fuzzy counterpart and PD controller. [ABSTRACT FROM AUTHOR]

This paper focuses on the path planning of mobile robot. Fuzzy logic is employed to deal with the uncertainty in the process of path planning. The hierarchical interval type-2 fuzzy method is obtained by combining the hierarchical fuzzy and interval type-2 fuzzy method, which is used in the path planning of mobile robot. Hierarchical fuzzy structure can simplify complex system and get fuzzy rules more easily. For multi input system, it can also solve the problem of rule explosion. Compared with type-1 fuzzy, interval type-2 fuzzy can better deal with the uncertainty in the process of path planning. Finally, in order to get a better path, genetic algorithm is used to optimize the membership function in the fuzzy path planner. Through the simulation experiment, the proposed hierarchical type-2 fuzzy planning method can effectively solve the path planning problem. Compared with the type-1 fuzzy method, the interval type-2 fuzzy method and the hierarchical type-1 fuzzy method, the proposed method obtains better results. [ABSTRACT FROM AUTHOR]

This study focuses on the problem of stability analysis and stabilisation for discrete time‐delay systems. A new type of augmented Lyapunov–Krasovskii functional (LKF) is proposed to derive less conservative stability and stabilisation conditions. A novel inequality is developed to handle more general double summation terms which arise from the forward difference of augmented LKF. Several zero equalities are introduced to further relax the existing results. Using some advanced convexificator techniques, the derived stability and stabilisation conditions are presented in terms of linear matrix inequalities. Several numerical examples are provided to demonstrate the advantage of the proposed approach. [ABSTRACT FROM AUTHOR]

In recent times, deep learning frameworks have become the preferred solution for visual object detection tasks, including texture defect detection. However, in real-world industrial applications, such as operation and maintenance of gas-insulated switchgear (GIS) equipment, collecting a large number of samples in advance is difficult because opening GIS equipment affects its normal operation; moreover, only four real-world GIS equipment defect samples are available currently. Most deep learning methods perform poorly when trained using a small number of labelled samples or when there is a lack of computational resources. In addition, the features extracted by multi-layer convolution and down-sampling are often unsuitable for small-size and low-contrast defects. However, the structure of the repetitive textured surface is simple and easy to model. Hence, this paper proposes a texture-defect detection method using principal components analysis (PCA) and histogram-based outlier score (HBOS) that requires only a small number of unlabelled samples and low computational complexity. First, PCA is utilized to extract features from the training patches to enhance the difference between the defect and background regions. Then, non-saliency suppression is applied for feature fusion to obtain the saliency map. Finally, the saliency value is converted to the defect score, and the defect is located by HBOS. Experiments on two public datasets and a GIS simulation equipment demonstrate that the proposed method is suitable for product surface defect detection and operation and maintenance in power distribution equipment. Moreover, compared with traditional texture feature-based and deep learning methods, the proposed method yields better results for small-size and low-contrast defects with only four unlabelled training samples per class. [ABSTRACT FROM AUTHOR]

This paper focuses on studying observer-based controller synthesis for nonlinear plant subject to parameter uncertainties, which is represented as interval type-2 T–S fuzzy model. An interval type-2 fuzzy augmented system is formed by an interval type-2 T–S fuzzy model, an interval type-2 fuzzy observer and an interval type-2 fuzzy controller connected in a closed loop. Based on the Lyapunov stability theory, the stability of the interval type-2 fuzzy augmented systems is investigated. Some matrix inequality techniques are applied to derive stability conditions in the form of linear matrix inequalities (LMIs). With the consideration of the information of lower and upper membership functions, more relaxed stability conditions are obtained to guarantee the system stability. Finally, two numerical example are provided to illustrate the effectiveness of the proposed conditions. [ABSTRACT FROM AUTHOR]

In this study, a general type-2 fractional order fuzzy PID (GT2FO-FPID) controller is proposed to fulfil the balance adjustment of the Power-line Inspection (PLI) robot system. It is a combination of Mamdani general type-2 fuzzy logic controller (GT2-FLC) and fractional PID controller. Since the PLI robot system is an under-actuated system, it's necessary to get complete information of the system. However, when all state variables are treated as input to the controller, there is a problem with the rule explosion. Because of this, the information fusion method is adopt to solve the problem and simplify the controller design. At the same time, fractional-order integral-differential operators and input-output scaling factors, which are taken as design variables and optimized by genetic algorithm (GA). To assess the performance of proposed controller based on symmetry criterion, we compared it against existing controllers, i.e., interval type-2 fractional order fuzzy PID (IT2FO-FPID), type-1 fractional order fuzzy PID (T1FO-FPID), and conventional fractional order (FOPID) controllers. Furthermore, to show the anti-inference ability of the proposed controller, three common perturbed process are tested. Finally, simulation results show that the GT2FO-FPID controller outperforms other controllers in the presence of external perturbations on the PLI robot system. [ABSTRACT FROM AUTHOR]

In this paper, a general type-2 fuzzy logic controller (GT2FLC), which is optimized by the particle swarm optimization (PSO) algorithm, is applied to a power-line inspection (PLI) robot. The information fusion is used to design the GT2FLC to avoid the rule explosion. The proposed controller has the ability to deal with uncertainties when the PLI robot works on the insulated access cable. In order to compare the performance of the proposed controller with that of other controllers, the type-1 fuzzy logic controller (T1FLC) and the interval type-2 fuzzy logic controller (IT2FLC) are both optimized by the PSO to adjust the PLI robot. To show the ability of different controllers to deal with uncertainties, external disturbances and parameter perturbations are added to the PLI robot. According to simulations, the performance of the proposed controller is better than that of other controllers, and the proposed controller has better ability to deal with uncertainties. [ABSTRACT FROM AUTHOR]

Highlights: [•] A multi-axis surface encoder, which can measure 6-DOF motions of a planar motion stage, has been developed. [•] It has been verified that the surface encoder could distinguish 2nm step motions in the Δx-, Δy- and Δz-directions. [•] It has been verified that the surface encoder could distinguish 0.1arcsecond step motions in the θx- and θy-directions, 0.3arcsecond step motion in the θz-direction. [•] The peak-to-valley amplitudes of the interpolation errors were less than ±7nm in X-, Y- and Z-directions, respectively. [•] Nonlinear error components were within 2.5arcseconds for θ x , θ y and θ z , respectively. [ABSTRACT FROM AUTHOR]