External disturbances pose significant challenges to the attitude tracking and precision maintenance of high-speed flight vehicles. To enhance the tracking accuracy, this paper proposes a function-level adaptive model predictive control (FAMPC) approach. In contrast to conventional parameter-adaptive methods, the proposed functional adaptive law (FAL) estimates unknown disturbances directly without requiring auxiliary parameterized approximators. Furthermore, rather than relying on terminal invariant sets for stability guarantees, a Lyapunov-based model predictive control (MPC) framework is formulated. The core approach involves synthesizing an auxiliary controller, whose influence is incorporated as an additional constraint to enforce a specific decay rate for a properly selected Lyapunov function, thereby ensuring closed-loop stability and recursive feasibility. Rigorous theoretical analysis formally establishes the recursive feasibility and closed-loop stability under the proposed Lyapunov-based framework, while extensive simulation studies validate the effectiveness of the proposed control strategy.

This paper proposes a novel dynamic model and a locomotion framework based on it for nonholonomic wheeled mobile robots (NWMR) with a general configuration. The general configuration is established on a nonholonomically constrained wheeled mobile platform driven by two motors, into which a 3-DoFs waist-leg mechanism, a torso, and a 7-DoFs dual-arm system are integrated. Based on this configuration, a novel terrain-adaptive NWMR dynamics model (TAND) is formulated to operate on arbitrary inclined planes, which unifies the dynamic equations of NWMRs with those of conventional 6-DoFs floating-base robots. Consequently, traditional 3-DoFs floating-base NWMR dynamic models are encompassed as specific cases within the proposed TAND. Furthermore, by incorporating hierarchical optimization-based whole-body control (HOWBC) with TAND, a terrain-adaptive HOWBC (TAHC) is introduced, enabling the achievement of motion tracking, posture maintenance, and manipulability optimization (MTO) across varied terrains. For the MTO sub-task, a new whole-body manipulability index specifically designed for NWMR is proposed, and gradient optimization methods at the acceleration level are applied to enhance it. The proposed TAND, MTO, and TAHC are validated through comprehensive simulations, demonstrating their effectiveness in enabling coordinated locomotion across different terrains.

In this study, we analyze a multi-UAV target-attacker-defender (TAD) differential game framework in which multiple defenders is tasked with shielding a target from several attackers. The attackers are driven by the objective of seizing the target, while the defenders focus on intercepting their advances. Hierarchical reinforcement learning (HRL) framework offers a promising strategy for the TAD problem. This work introduces a two-level goal-conditioned HRL method. At the first level, defenders are dynamically paired with target attackers through an assignment mechanism guided by differential game theory, where optimal intercept trajectories are computed to inform the matching process. The second level employs a multi-agent deep deterministic policy gradient (MADDPG) algorithm to derive coordinated policies for both teams. Experimental validation demonstrates the framework’s effectiveness compared to baseline method.

This paper proposes a nonlinear tracking differentiator-based prescribed performance zeta-backstepping control method for uncertain nonlinear systems with unmodeled dynamics and external disturbances. Unlike traditional prescribed performance control methods, the proposed approach allows direct adjustment of the system damping ratio through parameter regulation while ensuring that the tracking error remains within prescribed performance boundaries. To address unmodeled dynamics and external disturbances, the developed feedforward-compensated nonlinear tracking differentiator provides uncertainty estimation. The analysis of a second-order linear differential equation inequality rigorously proves that the closed-loop system achieves adjustable damping characteristics and guaranteed error performance. Finally, this control method is applied to a quad-rotor hover system, and experimental results confirm its effectiveness and advantages.

This brief presents an adaptive neural zeta-backstepping control strategy for a class of uncertain nonlinear systems, which allows these systems to be practically stabilized with predefined damping ratios. By introducing the zeta-backstepping technique, system damping ratios can be predetermined based on specific parameter selection rules. To reduce the impact of unknown nonlinearities, neural networks (NNs) with gradient descent training are applied to compensate such nonlinearities online. A new filter, called dynamic command filter, is used to construct the gradient of the NNs. By resorting to second-order Lyapunov stability criteria, it is proved that the closed-loop system is practically stable and has predefined damping ratio. Finally, experiments on a perturbed direct current (DC) motor system demonstrate the advantages of the proposed method.

Presetting the transient and steady-state performance of the dynamic system holds significant engineering value. This article introduces a model predictive controller with predefined performance boundaries, distinguished by its ability to control the morphology of transient processes. The core feature reflects the construction of state boundary constraints guided by the damping ratio. The construction process of constraints relies on solving a class of second-order Lyapunov inequalities, which can preset the system's damping ratio, thereby influencing the shape of the transient process. In addition, a function-adaptive law is developed to account for the nonlinear dynamics of the system. This function-adaptive law integrates optimization-based command-filtering technology, which extracts differential information from unknown signals. The stability of the controller and function-adaptive law is established using the Lyapunov criterion. Finally, the effectiveness of the proposed model predictive controller and its predefined boundary constraints are validated through experiments conducted on a gyroscope platform.

A full-link simulation of satellite single-photon LiDAR systems is important in laser parameter design, laser performance, and index analysis. Therefore, in this letter, a full-link simulation method for satellite single-photon LiDAR systems is proposed to simulate the whole process of satellite single-photon laser emission, transmission, and the generation of signal and noise photon events. The proposed method initially employs the LiDAR equation to simulate the energy and photon count of single-photon laser echoes, and it rapidly generates photon events using the Monte Carlo method. With the Advanced Topographic Laser Altimeter System (ATLAS) as the experimental object, echo photon events for three ideal terrains and complex natural surfaces are simulated. The simulated photon point cloud of the ideal terrain is consistent with the real terrain. Additionally, the signal photons observed by ATLAS (ATL03 data) are used to validate the accuracy of the simulated photon point cloud. The results indicate that the root mean square error (RMSE) of the distance between the ATL03 photons and the simulated 3-D photon point clouds is approximately 2.0 m, and the Chamfer distance (CD) is approximately 1.5 m. The coefficient of determination between the simulated photon elevation and ATL03 photon is 1.0. These results provide substantial evidence of the high similarity between the simulated and actual photon point clouds.

In this article, a practical finite-time command-filtered adaptive backstepping (PFTCFAB) control method is presented for a class of uncertain nonlinear systems with nonparametric unknown nonlinearities and external disturbances. Unlike PFTCFAB control techniques that use neural networks (NNs) or fuzzy-logic systems (FLSs) to deal with system uncertainties, the proposed method is capable of handling such uncertainties without the need for NNs or FLSs, thus reducing complexity and increasing reliability. In the proposed approach, novel function adaptive laws are designed to directly estimate unknown nonparametric nonlinearities and external disturbances by means of command filter techniques, and a type of practical finite-time command filters is proposed to obtain such laws. Moreover, the PFTCFAB controllers and finite-time command filters are designed with practical finite-time Lyapunov stability, which ensures finite-time stability of system tracking and filter estimation errors. Experimental results with a quadrotor hover system are presented and discussed to demonstrate the advantages and effectiveness of the proposed control strategy.

Full-waveform hyperspectral LiDAR (HSL) generates comprehensive hyperspectral waveforms for scenes to reveal the shape and spectral heterogeneity of multiple natural targets. Nevertheless, current waveform processing methods are primarily designed for single-wavelength LiDAR systems, resulting in a shortage of methods tailored for full-waveform HSL data processing and in a restriction to further quantitative applications for HSL. This study is designed to extract targets’ physical and spectral characteristics by integrating spectral-dimension features into the HSL waveform processing. The core idea of the method involves a rigorous processing technique consisting of parameter initialization, parameter optimization, and re-optimization over calculating the median (M) after ranking central locations of natural target echoes (Rclonte). The medians in the re-optimization step serve as the reference parameter sets for supplementing the hidden or weak components at some wavelengths for HSL. Two groups of datasets, the simulated and measured datasets, were utilized to evaluate the component detection ability of the proposed Rclonte-M method. The results suggest that the Rclonte-M method demonstrates excellent component detection performance on both simulated and measured data, outperforming the multispectral waveform decomposition (MSWD) method. The HSL system designed by us owns an overall ranging error of about 7 cm for adjacent components, with the relative neighbor distance error (RNDE) limited to 0.160. Besides, spectra retrieval results from HSL easily distinguish the natural targets along the laser path. This study enriches the full-waveform HSL data processing algorithm library and could be considered in other full-waveform HSL systems and the simulated airborne or space-borne HSL waveforms. Codes are freely available on https://github.com/Jie-Bai/Rclonte-M-TGRS.

3-D point cloud registration is a prerequisite for scene reconstruction and 3-D object recognition in computer vision and remote sensing. Numerous previous studies have presented a series of point cloud registration algorithms with diverse efficiencies and accuracies. However, registering point clouds with repetitive structures is still challenging. In this study, we propose the global planar structural constraint optimal (GPSCO)-based algorithm, which is specifically designed to handle the registration of repetitive structures. Its novelty lies in establishing the geometric constraint of multiple planes and registering based on the global optimal geometric constraint. The specific algorithm involves clustering the parallel planes into plane groups, estimating matching scores between plane groups, and selecting three corresponding pairs of nonparallel plane groups to form the plane structural constraint. The transformation matrix determined in the case of the optimal structural constraint is taken as the final result. The two terrestrial LiDAR datasets (HS1 and HS2) of real scenes with repetitive structures were collected to evaluate the GPSCO algorithm. Additionally, the GPSCO algorithm is validated on four public benchmark datasets, such as Whu-Park, Whu-Campus, ETH-Hauptgebaude, and ETH-Stairs. The registration results demonstrate that the GPSCO algorithm achieves 95.65%, 86.36%, 100%, 100%, 100%, and 88.89% successful registration rates (SRRs) on the six datasets, respectively, and significantly outperforms the existing methods on HS1 and HS2 with repetitive structures. The corresponding datasets and code are available at [https://github.com/fog223/GPSCO].

Subspace predictive control (SPC) is a widely utilized data-driven control technique in various industrial applications. However, its static nature restricts its ability to effectively track nonlinear dynamic systems, resulting in diminished performance. To address this problem, an adaptive subspace predictive control approach is proposed, incorporating an adaptive mechanism to continuously update the subspace predictor. The designed adaptive mechanism mitigates the negative impact of historical data by sliding the data window. It simultaneously employs the addition and deletion of data vectors in the data matrix through recursive matrix transformation, simplifying computational complexity while maintaining accuracy. In addition, the developed subspace predictor enables online learning and effectively handles the dynamic nature of industrial processes, requiring little prior knowledge. The theoretical analysis of the proposed control approach includes recursive feasibility and stability, along with a discussion on determining relevant parameters. The effectiveness of the proposed control approach is demonstrated through its application to a continuous stirred tank heater benchmark. The results exhibit significant improvements in tracking control performance, leading to enhanced efficiency and cost reduction. Overall, this research presents a promising solution for addressing the challenges of predictive control in industrial processes.

The problem of high-precision attitude tracking is studied in this paper under model uncertainty and external disturbance risk. First of all, the kinematics and dynamics model of rigid body is derived based on the quaternion theory. Then, a standard logarithmic sliding-mode function is constructed using the attitude quaternion and its derivatives of the rigid spacecraft, and the corresponding logarithmic sliding-mode controller is also derived. Finally, the stability of the controlled closed-loop system is demonstrated. Consistenting with theoretical proof, the numerical simulation results showed that the controlled rigid body can quickly track its desired attitude command, even if there are model uncertainty and external disturbance risks.

Compared with the grasping of the manipulator on the fixed base, the dynamic grasping of the aerial manipulator on the UAV floating base is a more challenging task. The strong coupling disturbance caused by the manipulator and the load can seriously affect the position tracking performance of the UAV base, leading to the inability of the manipulator's end-effector to accurately reach the grasping position, resulting in the failure of dynamic grasping. To address the issue, this paper presents a coupling disturbance compensation method that comprehensively considers the motion of the manipulator and the load on the end-effector. It can effectively compensate the strong coupling disturbance caused by the manipulator and the load and greatly improve the position tracking performance of the UAV base. In addition, considering that the aerial manipulator is also affected by lumped disturbances such as various uncertainties and wind disturbances, we propose an end-effector position compensation method based on inverse kinematics, so that the end-effector can reach the target position more accurately during the dynamic grasping process. Finally, three sets of comparative simulation results under two scenarios demonstrate the effectiveness of the proposed method.

Style transfer techniques have found wide-ranging applications in diverse domains, including image enhancement, film and animation production, augmented reality, and social media, garnering significant attention across various disciplines. However, prevailing approaches mainly rely on data-driven and adaptive normalization techniques for learning transformation matrices, while overlooking critical aspects such as multi-scale feature extraction, local-global distributions, and spatial-channel dimensions information. In this paper, we present a novel Adaptive Information Fusion Network (AIFN), comprising an encoder, an information fusion module, and a decoder symmetric to the encoder. Specifically, the information fusion module receives multi-scale feature maps extracted from a pre-trained encoder, consisting of three parallel sub-modules: Adaptive Attention Normalization (AdaAttN), Spatial-channel correlation, and a Linear submodule. Through adaptive learning of sub-module weights, we seamlessly integrate style features to achieve a harmonious fusion. Furthermore, we introduce illumination loss, ink wash loss, and identity loss to enhance stylization performance concerning lighting variations, global hue and diffusion mode, while retaining accurate and rich content features. Through comparison and ablation experiments, the proposed method produces high-quality stylized images, demonstrating excellent performance in arbitrary style transfer tasks.

A polynomial fuzzy fault detection scheme for sampled-output-measurements-based interval type-2 (IT2) polynomial-fuzzy-model-based (PFMB) systems is investigated in this paper, where the uncertainties in the premise variables (PVs) and membership functions (MFs) are described by IT2 fuzzy sets. Fully or partially unmeasurable PVs cause the parameter matrices of the polynomial fuzzy fault detection observer (PFFDO) to rely on the estimated states and the corresponding mismatching problems are further considered. Lyapunov stability theory is carried out with a novel multi-order homogenous polynomial Lyapunov functions (MHPLF) to introduce more information of the states when eliminating the partial derivatives, and the time-delays introduced by sampled-output measurements are handled by L-K functions. Unlike the membership-function-independent (MFI) approaches, the membership-function-dependent (MFD) approaches carry the information of the MFs for the relaxation of the stability constraints. Corresponding stable constraints in sum-of-squares (SOS) form are given to hold the asymptotic stability of the fault detection system with H ∞ performance γ . A numerical example with many cases illustrates the effectiveness of the proposed techniques in uncertainty handling and conservativeness reduction, while an inverted pendulum example verifies the feasibility of the method on physical systems.

As a typical mechatronics system, dual drive gantry stage has been widely used in high-end intelligent equipment. In this paper, an adaptive robust synchronization control scheme based on RBF neural network is presented to improve the synchronization accuracy and robust performance of dual drive gantry system. In order to overcome the limitation of system performance caused by ignoring high-frequency rotation mode in traditional modeling, a more reasonable rotational dynamic coupling model of gantry table was established. In addition, the adaptive robust control method with expected compensation is adopted to avoid the interference of measurement noise in the system and realize accurate compensation of the model. The advantages of RBF neural network infinite approximation are used to deal with the effects of model compensation residual, unmodeled dynamics and uncertain disturbances. The stability of the closed-loop system is proved by the Lyapunov theorem. Finally, different control strategies are used to conduct comparative experiments and the experimental results verify the superiority and effectiveness of the proposed control strategy. Note to Practitioners—The synchronization problem of dual drive gantry stage is a research hotspot in the industrial field and its control accuracy and robustness are important indexes that affect the system performance. In this paper, the coupled dynamics model of the gantry system is analyzed and established. In addition, an adaptive robust synchronous control strategy based on RBF neural network is presented to deal with various nonlinearities, mechanical strong coupling constraints and external unknown disturbances in the system, which improves the synchronization accuracy and anti-interference ability of the system. In practical industrial application, the designed controller can be applied to a class of dual-drive gantry systems to ensure the quality of product processing. At the same time, under the strong disturbance of complex working conditions, machine damage or more serious safety accidents caused by asynchronous movement can be avoided and the system reliability can be effectively improved, which is of great significance to industrial production.

The integrated fault estimation and fault-tolerant control scheme is developed in this paper for a series of interval type-2 polynomial fuzzy systems with both sensor faults and actuator faults, where the bi-directional influence between fault estimation unit and fault-tolerant control unit is investigated. Considering the existence of sensor faults, unmeasurable premise variables are investigated for more general situations and Class III state/fault estimation observers are established for the final fault estimation and fault-tolerant control purposes. To increase design flexibility and reduce physical implementation complexity, the proposed method allows the observer and original system to share asynchronous membership functions and a different number of fuzzy rules.$(\mathcal{Q,S,R}) \,-\, \alpha$dissipative performance index is also introduced to fulfill a wider vary of perfor-mance requirements. Membership-function-dependent stability constraints are given in the format of bi-linear polynomial matrix inequalities to obtain less conservative results, which are computed by a two-step path-following method. Superiority and validity are demonstrated by an inverted pendulum example in terms of estimation errors, fault-tolerant control performance and control inputs.

In this article, a combined gradient descent-Barzilai Borwein (GD-BB) algorithm and radial basis function neural network (RBFNN) output tracking control strategy was proposed for a family of nonlinear systems with unknown drift function and control input gain function. In such a method, a neural network (NN) is used to approximate the controller directly. The main merits of the proposed strategy are given as follows: first, not only the NN parameters, such as weights, centers, and widths but also the learning rates of NN parameter updating laws are updated online via the proposed learning algorithm based on Barzilai-Borwein technique; and second, the controller design process can be further simplified, the controller parameters that should be tuned can be greatly reduced. Theoretical analysis about the stability of the closed-loop system is manifested. In addition, simulations were conducted on a numerical discrete time system and an inverted pendulum system to validate the presented control strategy.

This article develops a novel cost function (performance index function) to overcome the obstacles in solving the optimal tracking control problem for a class of nonlinear systems with known system dynamics via adaptive dynamic programming (ADP) technique. For the traditional optimal control problems, the assumption that the controlled system has zero equilibrium is generally required to guarantee the finiteness of an infinite horizon cost function and a unique solution. In order to solve the optimal tracking control problem of nonlinear systems with nonzero equilibrium, a specific cost function related to tracking errors and their derivatives is designed in this article, in which the aforementioned assumption and related obstacles are removed and the controller design process is simplified. Finally, comparative simulations are conducted on an inverted pendulum system to illustrate the effectiveness and advantages of the proposed optimal tracking control strategy.

Dual-drive gantry stage has been widely applied to various industrial manufacturing fields with its unique structural advantages, and the synchronous control accuracy of the platform is crucial to the performance of the whole motion system. Therefore, an adaptive robust synchronous control scheme based on an improved master-slave structure is proposed, which is not only simple in structure but also easy to implement in engineering. The error dynamics model established in this article makes up for the lag of response of traditional master-slave control and improves the stability of closed-loop system. Online parameter adaptive algorithms deal with parameter uncertainties in the system, while robust control deals with unmodeled dynamics and external disturbances. In addition, nonlinear cogging force compensation is applied to the gantry biaxial system to further improve the control accuracy of tracking and synchronization. Finally, a dual-drive gantry stage system with good tracking and synchronization performance is obtained. The effectiveness and superiority of the proposed control strategy are verified by the comparison of several groups of experiments.

The transfer of artistic style is a major and demanding task in computer vision. Compared with western paintings, Chinese ink wash paintings have unique characteristics that prevent existing methods from yielding satisfactory results, such as voids, brush strokes, and ink wash tone and diffusion. The main problems include: 1) the generator does not concentrate on key global features; 2) the generated paintings lose the color of the original content image; 3) the generated paintings do not have bright edges and smooth shading. In this paper, we propose Chip-SAGAN, an innovative approach to transforming real-world pictures into Chinese ink wash paintings, which is trained with unpaired photos and Chinese ink wash paintings. We introduce a self-attention module into the generator to capture global dependencies between features. Furthermore, we introduce an edge-promoting adversarial loss and a color reconstruction loss to ensure that the generated painting matches the content image’s edges and colors. The experimental results show that our method can transform real-world pictures into high-quality Chinese ink wash paintings, and surpass state-of-the-art algorithms.

Leaf area index (LAI) is a significant parameter controlling a lot of physical and biological processes related to vegetation on the Earth's surface. Previously, an LAI estimation model of ICESat-2 (Ice, Cloud, and Land Elevation Satellite-2)/Atlas (Advanced Topographic Laser Altimeter System) has been established and the accuracy of ICESat-2 LAI has been evaluated using optical images. However, this model hasn't been tested with airborne data. To demonstrate the effectiveness of ICESat-2 LAI, this study applied the model to airborne LiDAR data in the Saihanba National Nature Reserve in the same season. Results showed that the coefficient of determination $(R^{2})$ of ICESat-2 LAI was 0.63 and the root mean square error (RMSE) is $1.03(n=26,\ p < 0.001)$ , ICESat-2 has the inversion capability of LAI comparable to airborne lidar. These findings may help in promoting the LAI estimation model and broadening the application fields of the photon-counting LiDAR data.

As an essential parameter in forestry, crown base height (CBH) faces many tasks. The methods are still developing for estimating it. Unmanned aerial vehicles (UAVs) light detection and ranging (LiDAR) supplies new, massive, and high-density data for estimating CBH. Many methods had been generated to compute CBH indirectly using regression-based ways or directly using geometric/statistical LiDAR-based ways. However, there were few methods to deal with the problem of understory, trunk, and noise points caused by high-density UAV data. A robust method was first proposed in this study to directly estimate CBH from LiDAR data, which contained two significant skills: 1) understory vegetation removal for each tree using a polynomial curve and 2) computing CBH by kernel densification of the elevation frequency histogram of LiDAR data. It could tolerate the understory and trunk points better through kernel convolution. The method proposed in this study and a previous simple model were applied in a crabapple plot in the Huailai Remote Sensing Comprehensive Experimental Station, Hebei, China, and verified by field-measured data. It was inspiring that our method is slightly better, and the mean CBH of LiDAR-derived trees was only 1.60 cm higher than that of field-measured trees. The mean absolute error (MAE) of CBH was 4.91 cm,${R}^{2}$was 0.73, the root-mean-squared error (RMSE) was 8.29 cm, and the bias was 2.68% for these trees. Generally, this method showed strong usability for high-density UAV LiDAR data and high precision for measuring CBH of low trees.

Because of various problems in the production of ceramics, such as material and transportation, there exists many defects in ceramics products. Traditional methods use pixel-level segmentation to locate defects which only achieve pixel-level accuracy. In order to realize higher-precision defect localization, we propose a novel subpixel interpolation method based on the blurred edge model. Based on the analysis to the gray histogram of the defect image, the threshold segmentation method is first used to roughly locate the defect. The formula for calculation of subpixel position using interpolation method is then derived, which is faster than the traditional fitting method. Finally, the 2-D subpixel edge localization problem is disassembled 1-D calculation problems in the X-direction and Y-direction according to the direction of the defect edge gradient. The subpixel location calculation method is further given. Experiments on different defects show that the proposed method can precisely segment the defects.

LiDAR footprint, defined as the illumination area of LiDAR sensor on the ground, is the fundamental unit that the sensor collects information from. The design of footprint size crucially influences the acquired LiDAR signals. For large-footprint full-waveform LiDAR, a well-designed footprint size is indispensable to acquire accurate and complete vertical profiles of scene targets. The methods that design the footprint size are increasingly needed to satisfy various application requirements. In this study, an analytical method to designing the footprint size is proposed for forest and topography applications. It is established based on a mixture Gaussian model and the designed footprint size ensures the signals of vegetation and ground can be completely extracted. Experiment results with our method show that the footprint size is preferably in the range of 10.6–25.0 m for forest application, while it is less than 32.3 m for topography application. The intersection of the two sets satisfies both applications. Furthermore, a series of sensibility studies were performed to analyze the influence of multiple key parameters to the optimal footprint size, including the scene characteristics, instrumental configurations, and application requirements. This study provides a theoretical basis for the design of future large-footprint full-waveform laser altimeters.

This paper investigates the position tracking control problem for an induction motor with completely unknown nonlinearities. A novel control scheme is presented by using the gradient descent algorithm, adaptive backstepping technique, neural networks (NNs), and extended differentiators. Differing from some existing results which only designed the adaption of weights of NNs, our proposed control strategy provides training for all the parameters of NNs, including the basis functions' widths and centers. With the help of the gradient descent algorithm and Lyapunov stability criterion, the convergence of both the NN approximation error and the system tracking error can be guaranteed. Finally, a simulation example shows the advantages of our proposed method compared with direct adaptive NN control strategy.

Aerial manipulator is a kind of robot with broad application prospects, which is suitable for high altitude operation and other dangerous application scenarios. This paper presents a trajectory tracking control algorithm for the aerial parallel manipulator based on Stewart platform. By modeling the overall dynamics of the aerial parallel manipulator, the expression of the influence of Stewart platform is given, and it is proved that this type of influence can be combined with the unmodeled error and external disturbance into the comprehensive disturbance of the flight platform. The flight platform trajectory tracking control is carried out by using the backstepping method, and the nonlinear disturbance observer is used for disturbance estimation and compensation in the control output. Numerical experiments show that the proposed control method can realize the trajectory tracking control of the flight platform of the aerial parallel manipulator.

Physical models simulating the radiative budget (RB) and remote sensing (RS) observation of three-dimensional (3D) landscapes are critical to better understand human and natural components of the Earth system and further develop RS technology. DART is one of the most comprehensive 3D models of Earth-atmosphere optical radiative transfer (RT), from ultraviolet (UV) to thermal infrared (TIR). It simulates the optical signal of proximal, aerial and satellite imaging spectrometers and laser scanners, the 3D RB and solar induced chlorophyll fluorescence (SIF) signal, for any urban or natural landscape and any experimental or instrument configuration. It is freely available for research and teaching activities (https://dart.omp.eu). Here, five recent advances are presented. 1) Atmosphere RT. 2) RT in non repetitive topography. 3) Monte Carlo modelling for fast RS image simulation of large landscapes. 4) SIF modelling for vegetation simulated as facets and turbid cells. 5) RS image inversion for mapping the optical properties of urban material and the urban radiative budget.

This study aims to understand the effect of surface topography on vegetation high-order backscatterings of large-footprint full-waveform LiDAR. Most previous studies in exploring LiDAR scattering mechanisms were carried out over relatively flat areas. To explore the canopy scattering mechanism on slope terrain, this study implements the Discrete Anisotropic Radiative Transfer (DART) model to simulate the LiDAR single- and multiple-scattering waveforms in mountainous forest scene. Results show that 1) terrain slope changes the LiDAR scattering components at different time delays by changing spatial distribution of scene elements; 2) the ratio of multiple scattering to total intensity changes little with the terrain slope (less than 2%); 3) multiple scattering contributes most when the ground vertical extent is close to the canopy vertical extent. These findings may help in better understanding the canopy scattering processes on the slope terrain.

A novel data-driven closed-loop subspace-aided approach to predictive controller design is proposed in this paper. The basic idea of the proposed method is to construct an instrumental variable which contributes to extending the standard LQ factorization to the closed-loop situation. Moreover, the predictive controller can be directly obtained from the input/output data with the subspace-aided technique by minimizing the normal predictive objective function. The efficiency of the proposed method is eventually verified and demonstrated through a numerical study on a randomly generated three-order discrete-time linear time invariant (LTI) system.

Multispectral LiDAR systems have been proved to have potential for retrieving forest structural and physiological parameters from waveforms of various wavelengths. However, multiple scattering occurring in complex forest canopy directly affects the waveform shape. This study combined a leaf optical model, a forest structure model, and a LiDAR process model to simulate multispectral LiDAR waveforms in single and multiple scattering cases and then assessed the effect of multiple scattering on forest physiological indicators. Results showed that 1) multiple scattering increases the waveform intensity, especially at near-infrared wavelength; 2) multiple scattering greatly affects the retrieval of physiological parameters of the understory; 3) when leaf chlorophyll content is low, forest physiological indicator was relative-seriously overestimated due to the multiple scattering effect. These findings may shed some light on our understanding of multiple scattering mechanism and accurately retrieving forest physiological parameters from multi-spectral LiDAR waveforms.

Although the Ice, Cloud, and land Elevation Satellite/Geoscience Laser Altimetry System (ICESat/GLAS) has been used for urban monitoring, however, previous studies focused on extracting the maximum building height within the footprint. In fact, the full-waveform recording of GLAS data makes it possible to extract multiple building heights. However, the uncertainty of the spatial distribution and reflectance creates considerable challenges for the fine inversion of multiple building heights within the footprint. In this letter, we proposed an inversion method of multiple building heights using GLAS data assisted by QuickBird imagery. First, the GLAS waveform and the auxiliary optical imagery were processed to extract some spectral, horizontal, and vertical information as the prior knowledge of the inversion model. Then, the multiple building heights were inversed from the optimal simulated waveform based on the 3-D geometric optical and radiative transfer (GORT) model. The building heights measured by airborne LiDAR were used to validate the inversed building heights. The results demonstrated that the proposed inversion method achieved the building height estimation accurately and precisely ($R^{2} = 0.971$, rRMSE = 13.2%, and$n = 430$). This letter may shed some light on extracting multiple-level heights within the footprint using satellite LiDAR full-waveform data.

In this paper, the problem of robust backstepping control for a three-degree-of-freedom experimental helicopter is investigated by using output feedback. The proposed control strategy can estimate the angular velocity through a state observer and achieve the attitude tracking of the elevation and pitch angles respectively in the case of using only the angular position sensor. The system unmodeled dynamics, parameter uncertainties, and external perturbation are addressed via robust backstepping technique. It's shown by Lyapunov stability analysis that the closed-loop system can be stabilized by the proposed controller with high control accuracy. The experimental results are provided to verify the effectiveness and advantage of the proposed control methodology.

The accurate estimation of terrain slope is very important for accurately monitoring the elevation and mass changes of glacier using laser altimeter. In this paper, a novel physical model was proposed for accurately estimating withinfootprint terrain slope. The new proposed model was built based on overlapping footprints of the geoscience laser altimeter system (GLAS) data, namely, using altitude angle, footprint size, shape, orientation, terrain aspect, and ground extent. Ground extent estimation models were established on the basis of linear regression analyses between: 1) GLAS-derived waveform extent and airborne topographic mapper (ATM)-derived ground extent and 2) GLAS-derived waveform width and ATM-derived ground extent, respectively. In addition, the terrain slopes estimated from the overlapping footprints were validated by ATM data and compared with the slopes calculated from surface elevations, i.e., from ASTER global digital elevation model (DEM) (GDEM) and GLAS elevation. Results showed that the accuracy of waveform width-predicted ground extents (R2= 0.868, RMSE = 0.686 m, n = 20, and p-value2= 0.776, RMSE = 0.824 m, n = 20, and p-value <; 0.0001), which indicated that waveform width is more suitable for estimating ground extent. Slopes estimated from the new proposed model have a strong consistency with those calculated from ATM data (Corrcoef = 0.786, bias = 0.654°, SD = 1.368°, and RMSE = 1.452°). Additionally, results also indicated that the new proposed model performs much better than the methods based on ASTER GDEM and the GLAS surface elevation in estimating within-footprint terrain slope due to higher correlation, lower bias, standard deviation, and RMSE.

The accurate estimation of within-footprint slope is very important for measuring earth’s surface characteristics using satellite light detection and ranging (LiDAR) data. Several models have previously been proposed for slope estimation; however, these models have limitations in either accuracy or applicability. Therefore, the main purpose of this paper is to explore the influence of various factors (e.g., ground vertical extent, footprint size, footprint shape, and footprint orientation) on slope estimation to better estimate the within-footprint slope using large-footprint waveform LiDAR data. The results indicated that the absolute slope error due to the coupling effect of ground vertical extent and footprint size increased with an increase in the ratio of ground vertical extent and footprint size, while the relative slope error had an opposite trend. The slope error caused by footprint shape was relatively low when the footprint eccentricity was small. However, the slope error due to footprint shape grew rapidly when the footprint eccentricity became larger; thus, it is essential to fully take into account the influence of footprint shape on within-footprint slope estimation. In addition, the results suggest that the slope error changed regularly based on the intersection angle between footprint orientation and terrain aspect. This paper also provided guidance for the determination of an easy and practical model for within-footprint slope estimation. The determination of best model is dependent on the value of intersection angle. Once the intersection value is given, the best model can easily be determined. Using the best model, the within-footprint terrain slope can be estimated with high accuracy.

An intelligent data-driven predictive control strategy is proposed in this paper. The predictive controller is designed by combining predictive control and local weighted projection regression. The presented control strategy needs less prior knowledge and has fewer parameters that are hard to determine compared to other data-driven predictive controller, e.g., the one in dynamic partial least square (PLS) framework. Furthermore, the proposed predictive controller performs better in the control of nonlinear processes and is able to update its parameters based on the online data. The predictive model validity and intelligence of the control strategy are guaranteed by the online updating strategy to a certain degree. The control performance of the proposed predictive controller against the model predictive control (MPC) in dynamic PLS framework is illustrated through the simulation of a typical numerical example and the benchmark of a continuous stirred tank heater system. It can be observed from the simulation that the proposed MPC strategy has higher prediction precision and stronger ability in coping with nonlinear dynamic processes which are quite common in practical applications, for instance, the industrial process.

In this paper, a stochastic gradient descent (SGD)-based adaptive neural network (NN) control scheme is presented for a class of uncertain nonlinear systems. The introduction of the SGD algorithm results in a better tracking performance compared with some other adaptive NN methods without using SGD. This is because the proposed SGD-based adaptive NN control strategy provides optimization algorithms for the weights, the widths, and the centers of the NNs, which can achieve a good function approximation performance. In order to implement the proposed method, extended differentiators are introduced to get the differential estimations of error signals, such that the loss function of the optimization algorithm can be constructed approximatively. Moreover, adaptive laws are designed to reduce the overall approximation errors, such that the tracking performance is further improved. By using the Lyapunov stability theory, it can be proved that the target signal is tracked by the system output within a small error. Finally, simulation and comparison results are given to show the effectiveness and advantages of the proposed method.

This paper is concerned with the design of fault detection for sampled-data systems, which are based on multirate sampling, with frequency specifications. A general multirate system is considered in this paper, where not only the inputs and outputs but also their different channels have different sampling rates. The purpose of this paper is to make this residual system with multirate sampling satisfy a given disturbance attenuation level over a restricted frequency range. With the use of the lifting technique, this paper reformulates a single-rate linear time-invariant system, which is equivalent to the multirate time-varying system. For a given restricted frequency range, convex conditions are obtained in designing a required fault detection filter. Then, the restricted frequency ranges problem are also solved specifically via the generalized Kalman-Yakubovic̆-Popov lemma. Finally, this paper uses a continuous-stirred tank reactor system to illustrate the effectiveness and advantages of the fault detection filter design method.

In this paper, based on the traditional Partial Least Squares(PLS) algorithm, a new way to select the most effective data sets for the PLS regression is proposed. The reason why we apply this new approach is that it could maintain or even surpass the original performance of control and diagnosis of a certain process while keep the data sets as less as possible to enhance the conciseness. The most significant advantage of the proposed data set selection method is that it identifies the data sets with the most typical characteristics of the group of data, excluding less informative data. Based on the ordinary PLS algorithm and with the improvement of conciseness, a better performance on prediction and fitting could be achieved. To achieve these goals, the ordinary and the improved PLS algorithm are introduced, after which a new method of data set selection is proposed. Specifically, the enhanced effectiveness of the proposed approach could be revealed by the simulation results of a numerical case.

This paper aims to discuss the H∞control problem of nonlinear stochastic switched systems in case where both global asymptotically stable in the mean (GASiM) subsystems and unstable subsystems coexist. An average dwell time (ADT) scheme is established to show us that the system is GASiM, if the activation time of GASiM subsystems is comparatively longer than that of unstable ones. Further, some conditions upon the H∞performance of the stochastic switched system are provided. The effectiveness of the proposed result is illustrated by a simulation example.

This paper puts forward an actuators and sensors allocation approach to the design of the adjacent buildings vibration attenuation under seismic excitation. A full order model of an adjacent buildings system with the location information of actuators and sensors is considered and by retaining the modes which make the largest contributions to the model with the Modal Cost Analysis (MCA), a reduced order model is established so that the controller can be designed conveniently. In view of the fact that not all the states of the system can be measured by the sensors, a dynamic output feedback H∞controller is designed for the adjacent buildings system. By considering that the output powers of the actuators are limited, a mixed H∞=GH2control is employed. Genetic algorithm (GA) is brought forward to design the dynamic output feedback controller and obtain the locations of the actuators and sensors. With the proposed approach, the allocation problem is solved and corresponding controller is obtained, which attenuates the building vibration at a sufficiently low level with constrained acting forces. Simulations demonstrate the effectiveness of the proposed approach in attenuating building vibration under earthquake excitation and some comparisons are made among the building systems with different quantities of actuators.