Local Feature Matching, a pivotal component of numerous computer vision tasks (e.g., structure from motion and visual localization), has been effectively addressed by Transformer-based methods. Nevertheless, these methods solely incorporate long-range context information among keypoints with a fixed receptive field, which constrains the network from appropriately reconciling the importance of features with diverse receptive fields to realize complete image perception, hence limiting feature matching accuracy. In addition, these methods employ a conventional handcrafted encoding approach to incorporate positional information of keypoints into visual descriptors, which limits the capability of networks to extract effective positional encoding message. In this study, we propose FMRT, a novel detector-free method that reconciles local features with diverse receptive fields adaptively and utilizes parallel networks to realize reliable positional encoding. Specifically, FMRT proposes a dedicated reconciliatory transformer (RecFormer) that contains a global perception attention layer to identify visual descriptors with different receptive fields and integrate global context information under various scales, a perception weight layer to measure the importance of various receptive fields adaptively, and a local perception feed-forward network to extract deep aggregated multi-scale local feature representation. Moreover, we introduce a novel axis-wise position encoder (AWPE) that views positional encoding as two keypoints encoding tasks along the row and column dimensions, decouples the x- and y-coordinates of keypoints into two independent 1D vectors, and designs two parallel network branches to explicitly encodes geometric correlations among keypoints, hence realizing reliable positional encoding. Extensive experiments indicate that FMRT yields impressive performance on multiple tasks, including relative pose estimation, visual localization, homography estimation, and image matching. Besides, we integrate FMRT into a localization framework and conduct a visual localization experiment in a real scene, which further demonstrate the superiority of FMRT. Note to Practitioners—This paper presents a novel approach to enhancing the performance of local feature matching in computer vision tasks. Traditional methods often rely on fixed receptive fields for integrating context among keypoints, which can limit the perception of the complete image and, consequently, the precision of feature matching. Our work introduces a Reconciliatory Transformer that not only addresses these limitations by effectively reconciling the importance of features across varying receptive fields but also improves the integration of positional information into visual descriptors. The techniques developed here can be adapted to a wide range of systems, e.g., image matching for computer vision and visual localization for autonomous driving, offering practitioners a tool to significantly improve the fidelity of feature matching, which is foundational for accurate interaction with the surrounding environment.

Although quadrotor has been widely used in practical engineering, its autonomous flight ability needs to be improved in complex operating environment. The autonomous flight problem for quadrotor with monocular vision is investigated, which is divided into control layer and decision layer in this article. Reinforcement learning method is utilized for hierarchical optimization to ensure that quadrotor completes narrow space traversal tasks safely and efficiently. First, considering the dynamic characteristics of the quadrotor with the motor speed as the control input, a parallel policy iteration algorithm is designed for the nonaffine nonlinear system, and the proposed controller can be learned online to improve the fundamental control performance. On this basis, the autonomous decision problem with visual information as input is modeled as a Markov decision process, and a curriculum learning mechanism is introduced to overcome the difficulties caused by sparse reward. At the same time, the clipping function is optimized to improve the learning efficiency of proximal policy optimization (PPO) algorithm for autonomous flight capabilities. Finally, the effectiveness of the proposed intelligent control and decision methods are verified through simulation.

Although deep deterministic policy gradient (DDPG) algorithm gets widespread attention as a result of its powerful functionality and applicability for large-scale continuous control, it cannot be denied that DDPG has problems such as low sample utilization efficiency and insufficient exploration. Therefore, an improved DDPG is presented to overcome these challenges in this article. Firstly, an optimizer based on fractional gradient is introduced into the algorithm network, which is conductive to increase the speed and accuracy of training convergence. On this basis, high-value experience replay based on weight-changed priority is proposed to improve sample utilization efficiency, and aiming to have a stronger exploration of the environment, an optimized exploration strategy for boundary action space is adopted. Finally, our proposed method is tested through the experiments of gym and pybullet platform. According to the results, our method speeds up the learning process, obtains higher average rewards in comparison with other algorithms.

Connecting multiple aerial vehicles to a rigid central platform through passive spherical joints holds the potential to construct a fully-actuated aerial platform. The integration of multiple vehicles enhances efficiency in tasks like mapping and object reconnaissance. This paper proposes a control and state estimation framework for the Integrated Aerial Platform (IAP), enabling it to perform versatile tasks like object reconnaissance and physical interactive tasks with only onboard sensors. In the framework, the 6D motion control serves as the low-level controller, while the high-level controller comprises a 6D admittance filter and a perception-aware attitude correction module. The 6D admittance filter, serving as the interaction controller, is adaptable for aerial interaction tasks. The perception-aware attitude correction algorithm is carefully designed by adopting a geometric Model Predictive Controller (MPC). This algorithm, incorporating both offline and online calculations, proves to be well-suited for the intricate dynamics of an IAP. A 6D direct wrench controller is also developed for the IAP. Notably, both the interaction controller and the direct wrench controller operate without reliance on force/torque sensors. Instead, a wrench observer algorithm is devised, considering external disturbances. Additionally, based on the kinematics constraints of the multiple aerials in the platform, a fusion algorithm for multiple Visual-Inertial Odometry (VIO) and kinematics constraints is developed, providing more accurate localization. A prototype of the IAP is constructed, and its capabilities are demonstrated through experiments including perception-aware object reconnaissance, aerial mapping, aerial peg-in-hole task, and 6D contact wrench generation. All experiments are conducted exclusively with onboard sensors. These tasks exemplify the merits of the proposed IAP and validate the effectiveness of the proposed control framework and fusion algorithm.

This article investigates the high-angle-of-attack (high-AOA) maneuver control problem for aircraft through finite-time techniques and neural learning. Considering the performance degradation of the flush air data sensing system at high-AOA maneuvers, a Kalman filtering approach based on force equations is implemented to estimate airflow angles online using signals from the inertial navigation system, even when aerodynamic coefficients are unknown. With the filtered signals, a finite-time adaptive neural controller is developed to generate the desired control moment and achieve rapid tracking of high-AOA commands, where both neural network (NN) and disturbance observer (DOB) are integrated to estimate composite disturbances. To enhance learning performance, an adaptive evaluation signal is constructed using online recorded data to update NN and DOB parameters. The deflections of thrust vector nozzles and aerodynamic control surfaces are finally obtained by solving an optimal control allocation problem. The practical finite-time uniformly ultimately bounded stability is proved through Lyapunov analysis. Herbst Maneuver simulations demonstrate that the proposed design achieves superior performance in both tracking accuracy and learning capabilities.

Rapid sampling from the environment to acquire available frontier points and timely incorporating them into subsequent planning to reduce fragmented regions are critical to improve the efficiency of autonomous exploration. We propose HPHS, a fast and effective method for the autonomous exploration of unknown environments. In this work, we efficiently sample frontier points directly from the LiDAR data and the local map around the robot, while exploiting a hierarchical planning strategy to provide the robot with a global perspective. The hierarchical planning framework divides the updated environment into multiple subregions and arranges the order of access to them by considering the overall revenue of the global path. The combination of the hybrid frontier sampling method and hierarchical planning strategy reduces the complexity of the planning problem and mitigates the issue of region remnants during the exploration process. Detailed simulation and real-world experiments demonstrate the effectiveness and efficiency of our approach in various aspects. The source code will be released to benefit the further research1.

This article concentrates on adaptive tracking control of strict-feedback uncertain nonlinear systems with an event-based learning scheme. A novel neural network (NN) learning law is proposed to design the adaptive control scheme. The NN weights information driven by the prediction-error-based control process is intermittently transmitted in the event-triggered context to the NN learning law mainly for signal tracking. The online stored sampled data of NN driven by the tracking error are utilized in the event context to update the learning law. With the adaptive control and NN learning law updated via the event-triggered communication, the improvements of NN learning capability, tracking performance, and system computing resource saving are guaranteed. In addition, it is proved that the minimum time interval for triggering errors of the two types of events is bounded and the Zeno behavior is strictly excluded. Finally, simulation results illustrate the effectiveness and good performance of the proposed control method.

This article investigates the finite-time control of the strict-feedback nonlinear system using composite learning based on the historical stack. The controller design adopts the backstepping scheme while the nonlinear function is introduced to avoid the singularity problem. The first-order Levant differentiator is introduced to obtain the filtered command signal and the compensation signal is further constructed. To indicate the learning performance, the historical data over the moving time window are analyzed to construct the predictor error using the maximum-minimum singular value algorithm. Furthermore, the finite-time neural update law is proposed. The stability of the closed-loop system is analyzed via the Lyapunov approach. The performance of the proposed method is verified using simulations.

This article investigates the fault detection problem of polynomial fuzzy systems with parameter uncertainty, external disturbance, and measurement noise. Based on the$H_{-}/L_{\infty }$observer and ellipsoidal analysis, a fault detection observer is designed, which is sensitive to faults while robust to disturbances. By using the given$H_{-}$and$L_{\infty }$performance indexes, the design conditions of fault detection observer are derived in sum of squares. The$H_{-}/L_{\infty }$fault detection observer can generate the residual for fault detection. We consider the fault-free condition that the admissible residual values are contained in compact sets. Based on the generated residual, the ellipsoidal analysis is used to evaluate the residual and detect the fault. Finally, the feasibility and effectiveness are verified through the simulation of a numerical nonlinear model.

This article investigates the adaptive learning control for a class of switched strict-feedback nonlinear systems with external disturbances and input dead zone. To handle unknown nonlinearity and compound disturbances, a collaborative estimation learning strategy based on neural approximation and disturbance observation is proposed, and the adaptive neural switched control scheme is studied in a dynamic surface control framework. In the adaptive learning control design, to obtain the evaluation information of uncertain learning, the prediction error is constructed based on the composite learning scheme. Then, the prediction error and the compensated tracking error are applied to construct the adaptive laws of switched neural weights and switched disturbance observers. The system stability analysis is carried out through the Lyapunov approach, where the switching signal with average dwell time is considered. Through the simulation test, the effectiveness of the proposed adaptive learning controller is verified.

This article investigates the robust adaptive learning control for space robots with target capturing. Based on the momentum conservation theory, the impact dynamics is constructed to derive the relationship of generalized velocity in the pre-impact and post-impact phase. Considering the nonlinear dynamics with contact impact, the robust control using nonsingular terminal sliding mode (NTSM) and fast NTSM is designed to achieve the fast realization of the desired states. Furthermore, for the unknown dynamics of the combination system after capturing a target, the adaptive learning control is developed based on neural network and disturbance observer. Through the serial-parallel estimation model, the prediction error is constructed for the update of adaptive law. The system signals involved in the Lyapunov function are proved to be bounded and the sliding mode surface converges in finite time. Simulation studies present the desired tracking and learning performance.

This paper investigates the predefined-time hierarchical coordinated adaptive control on the hypersonic reentry vehicle in presence of low actuator efficiency. In order to compensate for the deficiency of rudder deflection in advantage of channel coupling, the hierarchical design is proposed for coordination of the elevator deflection and aileron deflection. Under the control scheme, the equivalent control law and switching control law are constructed with the predefined-time technology. For the dynamics uncertainty approximation, the composite learning using the tracking error and the prediction error is constructed by designing the serial-parallel estimation model. The closed-loop system stability is analyzed via the Lyapunov approach and the tracking errors are guaranteed to be uniformly ultimately bounded in a predefined time. The tracking performance and the learning accuracy of the proposed algorithm are verified via simulation tests.

This article studies the robust intelligent control for the longitudinal dynamics of flexible hypersonic flight vehicle with input dead zone. Considering the different time-scale characteristics among the system states, the singular perturbation decomposition is employed to transform the rigid-elastic coupling model into the slow dynamics and the fast dynamics. For the slow dynamics with unknown system nonlinearities, the robust neural control is constructed using the switching mechanism to achieve the coordination between robust design and neural learning. For the time-varying control gain caused by unknown dead-zone input, the stable control is presented with an adaptive estimation design. For the fast dynamics, the sliding mode control is constructed to make the elastic modes stable and convergent. The elevator deflection is obtained by combining the two control signals. The stability of the dynamics is analyzed through the Lyapunov approach and the system tracking errors are bounded. The simulation is conducted to demonstrate the effectiveness of the proposed approach.

In this article, a robust self-learning fault-tolerant control (FTC) strategy is proposed to deal with the tracking control problem of the hypersonic flight vehicle (HFV) with uncertainties, actuator faults, and external disturbances. First, an adaptive baseline controller is constructed to achieve stable tracking, in which neural networks are introduced to approximate the unknown dynamics, adaptive laws are formulated to compensate the unknown lumped disturbances, and the Nussbaum technique is applied to address the time-varying actuator faults. Then, to improve the command tracking performance of the baseline controller, a data-driven auxiliary controller which can adaptively adjust the action–critic network weights over time along with the tracking deviation to obtain the optimal control signals in the sense of performance index is developed based on action-dependent heuristic dynamic programming technology. Finally, a comprehensive robust self-learning FTC law is constructed by synthesizing the baseline controller and the auxiliary controller, which leads to good robustness and tracking performance of the closed-loop HFV system. The stability and the superiority of the proposed control algorithm are verified by the Lyapunov theory and comparative numerical simulations, respectively.

In this article, the problem of$H_{\infty }$codesign for nonlinear control systems with unmatched uncertainties and adjustable parameters is investigated. The main purpose is to solve the adjustable parameters and$H_{\infty }$controller simultaneously so that better robust control performance can be achieved. By introducing a bounded function and defining a special cost function, the problem of solving the Hamilton–Jacobi–Isaacs equation is transformed into an optimization problem with nonlinear inequality constraints. Based on the sum of squares technique, a novel policy iteration algorithm is proposed to solve the problem of the$H_{\infty }$codesign. Moreover, one modified algorithm for optimizing the robust performance index is given. The convergence and the performance improvement of new iteration policy algorithms are proved. Simulation results are presented to demonstrate the effectiveness of the proposed algorithms.

The actuator failure compensation control problem of robotic systems possessing dynamic uncertainties has been investigated in this paper. Control design against partial loss of effectiveness (PLOE) and total loss of effectiveness (TLOE) of the actuator are considered and described, respectively, and a disturbance observer (DO) using neural networks is constructed to attenuate the influence of the unknown disturbance. Regarding the prescribed error bounds as time-varying constraints, the control design method based on barrier Lyapunov function (BLF) is used to strictly guarantee both the steady-state performance and the transient performance. A simulation study on a two-link planar manipulator verifies the effectiveness of the proposed controllers in dealing with the prescribed performance, the system uncertainties, and the unknown actuator failure simultaneously. Implementation on a Baxter robot gives an experimental verification of our controller.

This article concentrates on the event-based collaborative design for strict-feedback systems with uncertain nonlinearities. The controller is designed based on neural network (NN) weights adaptive law. The controller and NN weights adaptive law are only updated at the triggering instants determined by a novel composite triggering threshold. Considering the conservativeness of event condition, the state-model error is integrated into constructing the composite condition and NN weights adaptive law. In the context of the proposed mechanism, the requirements of system information and the allowable range of event-triggering error are relaxed. The number of triggering instants is greatly reduced without deteriorating the system performance. Moreover, the stability of the closed-loop is proved by the Lyapunov method following time-interval and sampling instants. Simulation results show the effectiveness of the scheme proposed in this article.

In this brief, the problem of synchronization control is investigated for a class of fractional-order chaotic systems with unknown dynamics and disturbance. The controller is constructed using neural approximation and disturbance estimation where the system uncertainty is modeled by neural network (NN) and the time-varying disturbance is handled using disturbance observer (DOB). To evaluate the estimation performance quantitatively, the serial-parallel estimation model is constructed based on the compound uncertainty estimation derived from NN and DOB. Then, the prediction error is constructed and employed to design the composite fractional-order updating law. The boundedness of the system signals is analyzed. The simulation results show that the proposed new design scheme can achieve higher synchronization accuracy and better estimation performance.

The tracking control is investigated for a class of uncertain strict-feedback systems with robust design and learning systems. Using the switching mechanism, the states will be driven back by the robust design when they run out of the region of adaptive control. The adaptive design is working to achieve precise adaptation and higher tracking precision in the neural working domain, while the finite-time robust design is developed to make the system stable outside. To achieve good tracking performance, the novel prediction error-based adaptive law is constructed by considering the estimation performance. Furthermore, the output constraint is achieved by imbedding the barrier Lyapunov function-based design. The finite-time convergence and the uniformly ultimate boundedness of the system signal can be guaranteed. Simulation studies show that the proposed approach presents robustness and adaptation to system uncertainty.

This article addresses the output feedback control of micromechanical (MEMS) gyroscopes using neural networks (NNs) and disturbance observer (DOB). For the unmeasured system states, the state observer and the high gain observer are constructed. The adaptive NNs are investigated to approximate the nonlinear dynamics, including the known nominal terms and the system uncertainties caused by environmental fluctuations. For the time-varying disturbances, the DOB is utilized. The sliding mode control is employed to enhance the robustness. Through simulation verification, the output feedback control using NNs and DOB can adapt to the dynamics of MEMS gyroscope with unmeasured system speed, while an expected effective tracking performance is obtained in the presence of unknown system nonlinearities and external disturbances.

In this article, adaptive state feedback stabilization is addressed for a class of delayed uncertain systems with external disturbance, where the time delays and the bound parameters of the delayed states and disturbance are assumed to be unknown. By introducing a new Lyapunov–Krasovskii functional, we present a memoryless control strategy to stabilize nonlinear time-delay systems through newly defined control gain function. The main contribution of this article lies in the construction of control gain function. Multiple gain functions can be revealed in a function set. The asymptotic stability of the closed delayed nonlinear system is ensured. Simulation examples are presented to show the effectiveness of the proposed method.

In this article, a robust adaptive learning control strategy for uncertain single-input-single-output systems in strict-feedback form and controllability canonical form (CCF) is studied. For the strict-feedback system, the dynamic surface control is introduced while for the controllability canonical system, sliding-mode control is further constructed. The finite-time design is introduced for fast convergence. Under the switching mechanism, the intelligent design and the robust technique work together to obtain robust tracking performance. Once the states run out of the domain of intelligent control, the robust item will pull the states back while inside the neural working domain, the composite learning is developed to achieve higher approximation precision by building the prediction error for the weight update. The closed-loop system stability is analyzed via the Lyapunov approach. Especially for the CCF, the finite-time convergence is achieved while the system signals are globally uniformly ultimately bounded. Simulation studies on the general nonlinear systems and the flight dynamics show that the new design scheme obtains better tracking performance with higher precision and stronger robustness.

This article investigates the composite learning fuzzy control for a class of stochastic nonlinear strict-feedback systems subject to dynamics uncertainty. The fuzzy logic system is built to model the unknown system nonlinearity. The highlight is that different from previous studies using only tracking error for fuzzy weight updating, the accuracy of fuzzy learning is emphasized in this study. The serial-parallel estimation model with fuzzy approximation and gain compensation is constructed to acquire the prediction error such that the composite fuzzy updating law is designed with more accurate feedback information. The stochastic stability analysis ensures the uniformly ultimate boundedness of the system signals in mean square. Through the simulation tests on a numerical example with different stochastic disturbances and one-link manipulator dynamics, it is proved that the proposed composite learning scheme can solve the system uncertainty effectively and make the closed-loop system track the reference command with satisfactory accuracy.

This article proposes a neural terminal sliding mode controller (TSMC) with finite-time (FT) convergence for the uncertain MEMS gyroscope dynamics. To address the uncertainty, considering the periodic tracking property of MEMS gyroscopes, a composite learning mechanism driven by the learning performance evaluation signal is applied to learn the system dynamics. By selecting the terminal sliding mode surface, the TSMC is constructed with error feedback and feedforward compensation. Under the TSMC with the composite learning, the system tracking can be guaranteed to be with FT convergence, while the weights of the learning system will converge in FT. The stability of the closed-loop system is analyzed by the Lyapunov approach. The highlight is that the design can obtain the learning knowledge while the information can be directly reused in repeated tasks with no need of online update. The effectiveness of the proposed method is verified via reference signal tracking of MEMS gyroscopes, while the controller using the stored knowledge achieves better tracking performance of faster convergence and higher tracking accuracy with no need of weight update.

This article proposes a virtual leader-based coordinated controller for the nonlinear multiple autonomous underwater vehicles (multi-AUVs) with the system uncertainties. To achieve the coordinated formation, a virtual AUV is set as the leader, while the desired command is designed using the relative position between each AUV and the virtual leader. The controller is designed based on the back-stepping scheme, and the online data-based learning scheme is used for uncertainty approximation. The highlight is that compared with previous learning methods which mostly focus on stability, the learning performance index is constructed using the collected online data in this article. The index is further used in the composite update law of the neural weights. The closed-loop system stability is analyzed via the Lyapunov approach. The simulation test on the five AUVs under fixed formation shows that the proposed method can achieve higher tracking performance with improved approximation accuracy.

This paper investigates the robust adaptive neural control of nonminimum phase hypersonic flight vehicle using composite learning. To overcome the nonminimum phase behavior, the output redefinition is employed and the attitude subsystem is transformed to the internal subsystem and the input-output subsystem. For the input-output subsystem, the adaptive neural control works together with the robust control to follow the reference command of pitch angle derived from the internal subsystem. Furthermore, the sliding mode control is constructed in a similar way. For the update of the neural weights, the composite learning is constructed using the prediction error. The stability of the closed-loop system is analyzed via the Lyapunov approach and the ultimately uniform boundedness of the tracking errors can be guaranteed. The effectiveness of the methodology is illustrated by the simulation results.

This article proposes a serial-parallel estimation model (SPEM)-based sliding mode control (SMC) of MEMS gyroscope. For the system nonlinearity, the linear-in-parameterized dynamics are formulated and the updating law of the parameter vector is given. For the system uncertainty, the radial basis function (RBF) neural network (NN) is utilized. To improve the approximation accuracy of the compound nonlinearity, the updating laws of the parameter vector and RBF NN weight are constructed by the tracking error and the filtered modeling error derived from SPEM. Furthermore, the fast terminal (FT) SMC is employed to achieve finite-time convergence. The simulation results show that the proposed controller obtains higher tracking accuracy and faster convergence, while the compound nonlinearity approximation is with higher precision.

In this paper, an event-based distributed cooperative learning (DCL) law is proposed for a group of adaptive neural control systems. The plants to be controlled have identical structures, but reference signals for each plant are different. During control process, each agent intermittently broadcasts its neural network (NN) weight estimation to its neighboring agents under an event-triggered condition that is only based on its own estimated NN weights. If communication topology is connected and undirected, the NN weights of all neural control systems can converge to a small neighborhood of their optimal values. The generalization ability of NNs is guaranteed in the event-triggered context, that is, the approximation domain of each NN is the union of all system trajectories. Furthermore, a strictly positive lower bound on the interevent intervals is also guaranteed to avoid the Zeno behavior. Finally, a numerical example is given to illustrate the effectiveness of the proposed learning law.

The efficient driving control of MEMS gyroscopes is an attractive way to improve the precision without hardware redesign. This paper investigates the sliding mode control (SMC) for the dynamics of MEMS gyroscopes using neural networks (NNs). Considering the existence of the dynamics uncertainty, the composite neural learning is constructed to obtain higher tracking precision using the serial-parallel estimation model (SPEM). Furthermore, the nonsingular terminal SMC (NTSMC) is proposed to achieve finite-time convergence. To obtain the prescribed performance, a time-varying barrier Lyapunov function (BLF) is introduced to the control scheme. Through simulation tests, it is observed that under the BLF-based NTSMC with composite learning design, the tracking precision of MEMS gyroscopes is highly improved.

Parameter uncertainty and unmodeled dynamics are inevitable for an actual nonlinear system, and are hard to deal with in control design, especially when the internal dynamics of this nonlinear system are unstable. The control design for a multiinput multioutput nonlinear nonminimum phase system with parameter uncertainty and unmodeled dynamics is discussed in this paper. The internal dynamics of nonminimum phase system are unstable, the control of this kind of system is challenging. Ideal internal dynamics (IID) based controller design method are utilized here, and a partially linearized model is constructed. A state tracking model is constructed on account of the partially linearized model and IID. Then the robust fuzzy controller design problem is discussed and a fuzzy logical system is utilized to identify the parameter uncertainty and unmodeled dynamics. A robust adaptive fuzzy controller is proposed for the stability of the nonlinear nonminimum phase system with parameter uncertainty and unmodeled dynamics. Finally, by simulations on vertical takeoff and landing aircraft, the availability of the presented adaptive fuzzy controller is validated.

This paper investigates the target tracking mission of an Unmanned Aerial Vehicle (UAV) equipped with a camera and a laser module. Firstly, utilizing Deep Neural Network (DNN) and Kernelized Correlation Filters (KCF), target recognition and location in the pixel coordinate system is achieved based on vision. Furthermore, by combining the laser ranging information and the distance estimation algorithm based on image, the distance between the UAV and the target is well estimated. To ensure the target tracking, a PID controller based on the distance error is applied to the UAV. The effectiveness of the system is verified on an actual UAV target tracking scenario.

This paper investigates a fault-tolerant control of the hypersonic flight vehicle using back-stepping and composite learning. With consideration of angle of attack (AOA) constraint caused by scramjet, the control laws are designed based on barrier Lyapunov function. To deal with the unknown actuator faults, a robust adaptive allocation law is proposed to provide the compensation. Meanwhile, to obtain good system uncertainty approximation, the composite learning is proposed for the update of neural weights by constructing the serial-parallel estimation model to obtain the prediction error which can dynamically indicate how the intelligent approximation is working. Simulation results show that the controller obtains good system tracking performance in the presence of AOA constraint and actuator faults.

This paper studies the compound learning control of disturbed uncertain strict-feedback systems. The design is using the dynamic surface control equipped with a novel learning scheme. This paper integrates the recently developed online recorded data-based neural learning with the nonlinear disturbance observer (DOB) to achieve good "understanding" of the system uncertainty including unknown dynamics and time-varying disturbance. With the proposed method to show how the neural networks and DOB are cooperating with each other, one indicator is constructed and included into the update law. The closed-loop system stability analysis is rigorously presented. Different kinds of disturbances are considered in a third-order system as simulation examples and the results confirm that the proposed method achieves higher tracking accuracy while the compound estimation is much more precise. The design is applied to the flexible hypersonic flight dynamics and a better tracking performance is obtained.

This paper addresses the dynamic surface control of uncertain nonlinear systems on the basis of composite intelligent learning and disturbance observer in presence of unknown system nonlinearity and time-varying disturbance. The serial-parallel estimation model with intelligent approximation and disturbance estimation is built to obtain the prediction error and in this way the composite law for weights updating is constructed. The nonlinear disturbance observer is developed using intelligent approximation information while the disturbance estimation is guaranteed to converge to a bounded compact set. The highlight is that different from previous work directly toward asymptotic stability, the transparency of the intelligent approximation and disturbance estimation is included in the control scheme. The uniformly ultimate boundedness stability is analyzed via Lyapunov method. Through simulation verification, the composite intelligent learning with disturbance observer can efficiently estimate the effect caused by system nonlinearity and disturbance while the proposed approach obtains better performance with higher accuracy.

The minimum error entropy (MEE) is an important and highly effective optimization criterion in information theoretic learning (ITL). For regression problems, MEE aims at minimizing the entropy of the prediction error such that the estimated model preserves the information of the data generating system as much as possible. In many real world applications, the MEE estimator can outperform significantly the well-known minimum mean square error (MMSE) estimator and show strong robustness to noises especially when data are contaminated by non-Gaussian (multimodal, heavy tailed, discrete valued, and so on) noises. In this brief, we present some theoretical results on the robustness of MEE. For a one-parameter linear errors-in-variables (EIV) model and under some conditions, we derive a region that contains the MEE solution, which suggests that the MEE estimate can be very close to the true value of the unknown parameter even in presence of arbitrarily large outliers in both input and output variables. Theoretical prediction is verified by an illustrative example.

This paper investigates the online recorded data-based composite neural control of uncertain strict-feedback systems using the backstepping framework. In each step of the virtual control design, neural network (NN) is employed for uncertainty approximation. In previous works, most designs are directly toward system stability ignoring the fact how the NN is working as an approximator. In this paper, to enhance the learning ability, a novel prediction error signal is constructed to provide additional correction information for NN weight update using online recorded data. In this way, the neural approximation precision is highly improved, and the convergence speed can be faster. Furthermore, the sliding mode differentiator is employed to approximate the derivative of the virtual control signal, and thus, the complex analysis of the backstepping design can be avoided. The closed-loop stability is rigorously established, and the boundedness of the tracking error can be guaranteed. Through simulation of hypersonic flight dynamics, the proposed approach exhibits better tracking performance.

This paper addresses two composite learning controller designs of quadrotor dynamics with unknown dynamics and time-varying disturbances using the terminal sliding mode. For unknown system dynamics, the single-hidden-layer feedforward network is employed for approximation which provides the information for the disturbance observer. Based on composite learning using neural approximation and disturbance estimation, the terminal sliding mode control (TSMC) is synthesized to obtain the finite-time convergence performance. To overcome the singularity problem, nonsingular TSMC is proposed. The closed-loop system stability under the two proposed controllers is presented via Lyapunov approach and the system trajectory will converge to the region caused by approximation error and disturbance estimation error. Simulation results demonstrate that the composite learning can efficiently estimate the system uncertainty and the tracking performance under the proposed controllers can be enhanced.

This paper investigates the singular perturbation (SP) theory-based composite learning control of a flexible-link manipulator using neural networks (NNs) and disturbance observer (DOB). For the dynamics, the system states are separated into fast and slow variables in terms of time scale. For the multi-input-multi-output slow dynamics, the intelligent control is designed where NNs are used for system uncertainty approximation and the DOB is used for compound disturbance estimation. The main contribution is that a novel controller using NN and DOB is constructed to deal with unknown dynamics and time-varying disturbances while the composite learning algorithm is proposed with prediction error. For the fast dynamics, sliding mode control is employed. The boundedness of the tracking error is proved via Lyapunov approach. The simulation results show that the DOB-based composite neural control can greatly improve the tracking precision.

This paper addresses a terminal sliding mode strategy to control the attitude of quadrotor while achieving the finite time convergence. To deal with system uncertainty and time-varying disturbance, a hybrid controller using single-hidden layer feedforward network (SLFN) and disturbance observer (DOB) is proposed. Fast terminal sliding mode surface is designed to construct the sliding mode control. To improve learning speed, the updating law of SLFN weight utilizes the information of the fast terminal sliding mode. The effectiveness of the proposed controller is demonstrated with simulation example.

This paper investigates the dynamic surface control of nonlinear transport aircraft model during the process of continuous heavy cargo airdrop in case of disturbance and actuator saturation. For the continuous airdrop process, the effects of moving process parameters include cargo mass and moving items upon the flight and the aircraft dynamics is with dramatic change. The dynamic surface related technique is designed for the attitude subsystem with unmatched disturbance and unknown dynamics so that in each step the virtual control is carefully considered using disturbance observer while the auxiliary signal is constructed in case of actuator saturation. The closed-loop stability is established via Lyapunov approach. In simulation test, it is interesting to find out that continuous airdrop process will deteriorate the system stability if additional control effort is not exerted. The proposed disturbance observer-based control can effectively enhance the reliability and robustness of the system, and can make sure of the smooth system performance at the instant of the heavy cargo separating from the aircraft.

This paper investigates the disturbance observer-based composite fuzzy control of a class of uncertain nonlinear systems with unknown dead zone. With fuzzy logic system approximating the unknown nonlinearities, composite learning is constructed on the basis of a serial–parallel identifier. By introducing the intermediate signal, the disturbance observer is developed to provide efficient learning of the compounded disturbance which includes the effect of time-varying disturbance, fuzzy approximation error, and unknown dead zone. Based on the disturbance estimation and fuzzy approximation, the adaptive fuzzy controller is synthesized with novel updating law. The stability analysis of the closed-loop system is rigorously established via Lyapunov approach. The performance of the proposed controller is verified via simulation that faster convergence and higher precision are obtained.

This paper studies both indirect and direct global neural control of strict-feedback systems in the presence of unknown dynamics, using the dynamic surface control (DSC) technique in a novel manner. A new switching mechanism is designed to combine an adaptive neural controller in the neural approximation domain, together with the robust controller that pulls the transient states back into the neural approximation domain from the outside. In comparison with the conventional control techniques, which could only achieve semiglobally uniformly ultimately bounded stability, the proposed control scheme guarantees all the signals in the closed-loop system are globally uniformly ultimately bounded, such that the conventional constraints on initial conditions of the neural control system can be relaxed. The simulation studies of hypersonic flight vehicle (HFV) are performed to demonstrate the effectiveness of the proposed global neural DSC design.

This paper studies the composite adaptive tracking control for a class of uncertain nonlinear systems in strict-feedback form. Dynamic surface control technique is incorporated into radial-basis-function neural networks (NNs)-based control framework to eliminate the problem of explosion of complexity. To avoid the analytic computation, the command filter is employed to produce the command signals and their derivatives. Different from directly toward the asymptotic tracking, the accuracy of the identified neural models is taken into consideration. The prediction error between system state and serial-parallel estimation model is combined with compensated tracking error to construct the composite laws for NN weights updating. The uniformly ultimate boundedness stability is established using Lyapunov method. Simulation results are presented to demonstrate that the proposed method achieves smoother parameter adaption, better accuracy, and improved performance.

In this brief, a novel adaptive-critic-based neural network (NN) controller is investigated for nonlinear pure-feedback systems. The controller design is based on the transformed predictor form, and the actor-critic NN control architecture includes two NNs, whereas the critic NN is used to approximate the strategic utility function, and the action NN is employed to minimize both the strategic utility function and the tracking error. A deterministic learning technique has been employed to guarantee that the partial persistent excitation condition of internal states is satisfied during tracking control to a periodic reference orbit. The uniformly ultimate boundedness of closed-loop signals is shown via Lyapunov stability analysis. Simulation results are presented to demonstrate the effectiveness of the proposed control.

This paper examines the importance of non-topological node attributes for link prediction in social networks. Rank method and supervised learning method were introduced to show the role of the node attributes in link prediction respectively. A rule for choosing the appropriate node attributes was discussed and a method for aggregating two node attributes was proposed. The result of the experiments on a blog dataset showed that using non-topological node attributes make a better performance in link prediction.

Terrainability is mostly dependant on the suspension mechanism and the control of a space rover. For the six wheeled CRAB rover, this paper presents the composite control design with torque control and adaptive Kriging control to improve the terrainability, somewhat related to minimizing wheel slip. As CRAB is moving slowly, the torque control is processed by minimizing the variance of the required friction coefficient based on the static model. Adaptive Kriging control is used to track the commanded velocity. The system uncertainty is compensated by Kriging estimation based on the velocity dynamics. Experiment results with two different tires show the effectiveness of the control scheme.

This paper investigates the adaptive Kriging controller for the longitudinal dynamics of a generic hypersonic flight vehicle (HFV). For the altitude subsystem, the dynamics are transformed into the strict-feedback form where the back-stepping scheme is employed. Considering the nonlinearity of the dynamics, the nominal feedback is included in the controller while Kriging system is designed to estimate the uncertainty. With the proposed controller, the almost surely bounded stability is guaranteed. The simulation study is presented to show the effectiveness of the proposed control approach.