arXiv subjects

Xidong Wang

Publications and source records attributed to Xidong Wang.

43 records · Page 3Linked to original sources

arXiv2022

Prescribed Performance Adaptive Fixed-Time Attitude Tracking Control of a 3-DOF Helicopter with Small Overshoot

In this article, a novel prescribed performance adaptive fixed-time backstepping control strategy is investigated for the attitude tracking of a 3-DOF helicopter. First, a new unified barrier function (UBF) is designed to convert the prescribed performance constrained system into an unconstrained one. Then, a fixed-time (FxT) backstepping control framework is established to achieve the attitude tracking. By virtual of a newly proposed inequality, a non-singular virtual control law is constructed. In addition, a FxT differentiator with a compensation mechanism is employed to overcome the matter of "explosion of complexity". Moreover, a modified adaptive law is developed to approximate the upper bound of the disturbances. To obtain a less conservative and more accurate approximation of the settling time, an improved FxT stability theorem is proposed. Based on this theorem, it is proved that all signals of the system are FxT bounded, and the tracking error converges to a preset domain with small overshoot in a user-defined time. Finally, the feasibility and effectiveness of the presented control strategy are confirmed by numerical simulations.

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arXiv2022

Adaptive Smooth Disturbance Observer-Based Fast Finite-Time Attitude Tracking Control of a Small Unmanned Helicopter

In this paper, a novel adaptive smooth disturbance observer-based fast finite-time adaptive backstepping control scheme is presented for the attitude tracking of the 3-DOF helicopter system subject to compound disturbances. First, an adaptive smooth disturbance observer (ASDO) is proposed to estimate the composite disturbance, which owns the characteristics of smooth output, fast finite-time convergence, and adaptability to the disturbance of unknown derivative boundary. Then, a finite-time backstepping control protocol is construct to drive the elevation and pitch angles to track reference trajectories. To tackle the "explosion of complexity" and "singularity" problems in the conventional backstepping design framework, a fast finite-time command filter (FFTCF) is utilized to estimate the virtual control signal and its derivative. Moreover, a fractional power-based auxiliary dynamic system is introduced to compensate the error caused by the FFTCF estimation. Furthermore, an improved fractional power-based adaptive law with the $σ$-modification term is designed to attenuate the observer approximation error, such that the tracking performance is further enhanced. In terms of the fast finite-time stability theory, the signals of the closed-loop system are all fast finite-time bounded while the attitude tracking errors can fast converge to a sufficiently small region of the origin in finite time. Finally, a contrastive numerical simulation is carried out to validate the effectiveness and superiority of the designed control scheme.

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arXiv2021

Finite-Time Gradient Descent-Based Adaptive Neural Network Finite-Time Control Design for Attitude Tracking of a 3-DOF Helicopter

This paper investigates a novel finite-time gradient descent-based adaptive neural network finite-time control strategy for the attitude tracking of a 3-DOF lab helicopter platform subject to composite disturbances. First, the radial basis function neural network (RBFNN) is applied to estimate lumped disturbances, where the weights, centers and widths of the RBFNN are trained online via finite-time gradient descent algorithm. Then, a finite-time backstepping control scheme is constructed to fulfill the tracking control of the elevation and pitch angles. In addition, a hybrid finite-time differentiator (HFTD) is introduced for approximating the intermediate control signal and its derivative to avoid the problem of "explosion of complexity" in the traditional backstepping design protocol. Moreover, the errors caused by the HFTD can be attenuated by the combination of compensation signals. With the aid of the stability theorem, it is proved that the closed-loop system is semi-globally uniformly ultimately boundedness in finite time. Finally, a comparison result is provided to illustrate the effectiveness and advantages of the designed control strategy.

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arXiv2021

Smooth Attitude Tracking Control of a 3-DOF Helicopter with Guaranteed Performance

This paper presents a new prescribed performance control scheme for the attitude tracking of the three degree-of-freedom (3-DOF) helicopter system with lumped disturbances under mechanical constraints. First, a novel prescribed performance function is defined to guarantee that the tracking error performance has a small overshoot in the transient process and converges to an arbitrary small region within a predetermined time in the steady-state process without knowing the initial tracking error in advance. Then, based on the novel prescribed performance function, an error transformation combined with the smooth finite-time control method we proposed before is employed to drive the elevation and pitch angles to track given desired trajectories with guaranteed tracking performance. The theoretical analysis of finite-time Lyapunov stability indicates that the closed-loop system is fast finite-time uniformly ultimately boundedness. Finally, comparative experiment results illustrate the effectiveness and superiority of the proposed control scheme.

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arXiv2020

An Adaptive Multivariable Smooth Second-Order Sliding Mode Approach

This paper presents a novel adaptive multivariable smooth second-order sliding mode approach with the features of fast finite-time convergence, adaptation to disturbances and smooth. This approach can be directly applied to the controller design of multi-input and multi-output (MIMO) systems. In addition, a novel adaptive multivariable smooth disturbance observer is proposed based on this structure. In terms of the types of disturbances, the fast finite-time convergence and the fast finite-time uniformly ultimately boundedness of the systems are proved with the corresponding fast finite-time Lyapunov stability theory. Finally, the effectiveness of the proposed approach is validated by comparative numerical simulations.

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arXiv2020

Adaptive Fast Smooth Second-Order Sliding Mode Control for Attitude Tracking of a 3-DOF Helicopter

This paper presents a novel adaptive fast smooth second-order sliding mode control for the attitude tracking of the three degree-of-freedom (3-DOF) helicopter system with lumped disturbances. Combining with a non-singular integral sliding mode surface, we propose a novel adaptive fast smooth second-order sliding mode control method to enable elevation and pitch angles to track given desired trajectories respectively with the features of non-singularity, adaptation to disturbances, chattering suppression and fast finite-time convergence. In addition, a novel adaptive-gain smooth second-order sliding mode observer is proposed to compensate time-varying lumped disturbances with the smoother output compared with the adaptive-gain second-order sliding mode observer. The fast finite-time convergence of the closed-loop system with constant disturbances and the fast finite-time uniformly ultimately boundedness of the closed-loop system with the time-varying lumped disturbances are proved with the finite-time Lyapunov stability theory. Finally, the effectiveness and superiority of the proposed control methods are verified by comparative simulation experiments.

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arXiv2020

Multimodal fusion for sea level anomaly forecasting

The accumulated remote sensing data of altimeters and scatterometers have provided a new opportunity to forecast the ocean states and improve the knowledge in ocean/atmosphere exchanges. Few previous studies have focused on sea level anomaly (SLA) multi-step forecasting by multivariate deep learning for different modalities. For this paper, a novel multimodal fusion approach named MMFnet is used for SLA multi-step forecasting in South China Sea (SCS). First, a grid forecasting network is trained by an improved Convolutional Long Short-Term Memory (ConvLSTM) network on daily multiple remote sensing data from 1993 to 2016. Then, an in-situ forecasting network is trained by an improved LSTM network, which is decomposed by the ensemble empirical mode decomposition (EEMD-LSTM), on real-time, in-situ and remote sensing data. Finally, the two single-modal networks are fused by an ocean data assimilation scheme. During the test period from 2017 to 2019, the average RMSE of the MMFnet (single-modal ConvLSTM) is 4.03 cm (4.51 cm), the 15th-day anomaly correlation coefficient is 0.78 (0.67), the performance of MMFnet is much higher than those of current state-of-the-art dynamical (HYCOM) and statistical (ConvLSTM, Persistence and daily Climatology) forecasting systems. Sensitivity experiments analysis indicates that, the MMFnet, which added CCMP SCAT products and OISST for SLA forecasting, has improved the forecast range over a week and can effectively produce 15-day SLA forecasting with reasonable accuracies.In an extension of the validation over the North Pacific Ocean, MMFnet can calculate the forecasting results in a few minutes, and we find good agreement in amplitude and distribution of SLA variability between MMFnet and other classical operational model products.

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