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 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.

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 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.

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.

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 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 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.

Our work addresses the problem of analyzing and understanding dynamic video scenes. A two-level motion pattern mining approach is proposed. At the first level, activities are modeled as distributions over patch-based features, including spatial location, moving direction, and speed. At the second level, traffic states are modeled as distributions over activities. Both patterns are shared among video clips. Compared to other works, one advantage of our method is that moving speed is considered to describe visual word. The other advantage is that traffic states are detected and assigned to every video frame. These enable finer semantic interpretation, more precise video segmentation, and anomaly detection. Specifically, every video frame is labeled by a certain traffic state, and the video is segmented frame by frame accordingly. Moving pixels in each frame, which do not belong to any activity or cannot exist in the corresponding traffic state, are detected as anomalies. We have successfully tested our approach on some challenging traffic surveillance sequences containing both pedestrian and vehicle motions.

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.

A high-resolution relaxed scheme which requires little information of the eigenstructure is presented for the multiclass Lighthill-Whitham-Richards (LWR) model on an inhomogeneous highway. The scheme needs only an estimate of the upper boundary of the maximum of absolute eigenvalues. It is based on incorporating an improved fifth-order weighted essentially non-oscillatory (WENO) reconstruction with relaxation approximation. The scheme benefits from the simplicity of relaxed schemes in that it requires no exact or approximate Riemann solvers and no projection along characteristic directions. The effectiveness of our method is demonstrated in several numerical examples.

Our work addresses the problem of analyzing and understanding dynamic video scenes. A two-level motion pattern mining approach is proposed. At the first level, single-agent motion patterns are modeled as distributions over pixel-based features. At the second level, interaction patterns are modeled as distributions over single-agent motion patterns. Both patterns are shared among video clips. Compared to other works, the advantage of our method is that interaction patterns are detected and assigned to every video frame. This enables a finer semantic interpretation and more precise anomaly detection. Specifically, every video frame is labeled by a certain interaction pattern and moving pixels in each frame which do not belong to any singleagent pattern or cannot exist in the corresponding interaction pattern are detected as anomalies. We have tested our approach on a challenging traffic surveillance sequence containing both pedestrian and vehicular motions and obtained promising results.

The second-order oscillating particle swarm optimization(SO-PSO) algorithm, which introduced the second-order oscillating evolutionary equation to the evolutionary equation of PSO, could adjust the particles’ global and local search capability and avoid the local optimization. It was proposed to solve a mathematical model which was built for aircraft departure sequencing problem in this paper. The correlative implementation techniques and detailed design process of the algorithm were presented Then the simulation was performed to solve this sequencing problem using the SO-PSO algorithm. The results showed that the global optimal solution was obtained, so the SO-PSO algorithm was rational and feasible and curtailed the consumption of aircraft departure effectively.

Based on least squares wavelet support vector machines (LS-WSVM) with quantum-inspired evolutionary algorithm (QEA), the prediction model of urban passenger transport is proposed , that can provide the theoretical foundation of forecasting passenger volume of urban transport accurately. The prediction model of urban passenger transport is established by using LS-WSVM, whose regularization parameter and kernel parameter are adjusted using quantum-inspired evolutionary algorithm. QEA with quantum chromosome and quantum mutation has better global search capacity. The parameters of LS-WSVM can be adjusted using quantum-inspired evolutionary optimization. Combining with the data of the urban volume of passenger transport of Xipsilaan over years, the prediction model of urban passenger transport is validated, the simulation results indicate that the prediction model is effective, and based on LS-WSVM has more improvement than LS-SVM with Gaussian kernel in predicting precision, and then the improved LS-WSVM with QEA is efficient than with cross-validation method for tuning parameters.