Because of their volume and power limitation, it is difficult for CubeSats to configure a traditional propulsion system. Atmospheric drag is one of the space environmental forces that low-orbit satellites can use to realize orbit adjustment. This paper presents an integrated control strategy to achieve the desired in-track formation through the atmospheric drag difference, which will be used on ZJUCubeSat, the next pico-satellite of Zhejiang University and one of the participants of the international QB50 project. The primary mission of the QB50 project is to explore the near-Earth thermosphere and ionosphere at the orbital height of 90–300 km. Atmospheric drag cannot be ignored and has a major impact on both attitude and orbit of the satellite at this low orbital height. We conduct aerodynamics analysis and design a multidimensional nonlinear constraint programming (MNLP) strategy to calculate different desired area–mass ratios and corresponding hold times for orbit adjustment, taking both the semimajor axis and eccentricity into account. In addition, area–mass ratio adjustment is achieved by pitch attitude maneuver without any deployable mechanism or corresponding control. Numerical simulation based on ZJUCubeSat verifies the feasibility and advantage of this design.
The flight dynamics model of air-breathing hypersonic vehicles (AHVs) is highly nonlinear and multivariable cou-pling, and includes inertial uncertainties and external disturbances that require strong, robust, and high-accuracy controllers. In this paper, we propose a linear-quadratic regulator (LQR) design method based on stochastic robustness analysis for the longitudinal dynamics of AHVs. First, input/output feedback linearization is used to design LQRs. Second, subject to various system parameter uncertainties, system robustness is characterized by the probability of stability and desired performance. Then, the mapping rela-tionship between system robustness and LQR parameters is established. Particularly, to maximize system robustness, a novel hybrid particle swarm optimization algorithm is proposed to search for the optimal LQR parameters. During the search iteration, a Chernoff bound algorithm is applied to determine the finite sample size of Monte Carlo evaluation with the given probability levels. Finally, simulation results show that the optimization algorithm can effectively find the optimal solution to the LQR parameters.
Steering control for an autonomous underwater glider (AUG) is very challenging due to its changing dynamic char-acteristics such as payload and shape. A good choice to solve this problem is online system identification via in-field trials to capture current dynamic characteristics for control law reconfiguration. Hence, an online polynomial estimator is designed to update the yaw dynamic model of the AUG, and an adaptive model predictive control (MPC) controller is used to calculate the optimal control command based on updated estimated parameters. The MPC controller uses a quadratic program (QP) to compute the optimal control command based on a user-defined cost function. The cost function has two terms, focusing on output reference tracking and move suppression of input, respectively. Move-suppression performance can, at some level, represent energy-saving performance of the MPC controller. Users can balance these two competitive control performances by tuning weights. We have compared the control performance using the second-order polynomial model to that using the fifth-order polynomial model, and found that the former cannot capture the main characteristics of yaw dynamics and may result in vibration during the flight. Both processor-in-loop (PIL) simulations and in-lake tests are presented to validate our steering control performance.
Hybrid signcryption is an important technique signcrypting bulk data using symmetric encryption. In this paper, we apply the technique of certificateless hybrid signcryption to an elliptic-curve cryptosystem, and construct a low-computation certificateless hybrid signcryption scheme. In the random oracle model, this scheme is proven to have indistinguishability against adaptive chosen-ciphertext attacks (IND-CCA2) under the elliptic-curve computation Diffie-Hellman assumption. Also, it has a strong existential unforgeability against adaptive chosen-message attacks (sUF-CMA) under the elliptic-curve discrete logarithm assumption. Analysis shows that the cryptographic algorithm does not rely on pairing operations and is much more efficient than other algorithms. In addition, it suits well to applications in environments where resources are constrained, such as wireless sensor networks and ad hoc networks.
A distributed fault-tolerant strategy for the controller area network based electric swing system of hybrid excavators is proposed to achieve good performance under communication errors based on the adaptive compensation of the delays and packet dropouts. The adverse impacts of communication errors are effectively reduced by a novel delay compensation scheme, where the feedback signal and the control command are compensated in each control period in the central controller and the swing motor driver, respectively, without requiring additional network bandwidth. The recursive least-squares algorithm with forgetting factor algorithm is employed to identify the time-varying model parameters due to pose variation, and a reverse correction law is embedded into the feedback compensation in consecutive packet dropout scenarios to overcome the impacts of the model error. Simulations and practical experiments are conducted. The results show that the proposed fault-tolerant strategy can effectively reduce the communication-error-induced overshoot and response time variation.
As a typical biometric cue with great diversities, smile is a fairly influential signal in social interaction, which reveals the emotional feeling and inner state of a person. Spontaneous and posed smiles initiated by different brain systems have differences in both morphology and dynamics. Distinguishing the two types of smiles remains challenging as discriminative subtle changes need to be captured, which are also uneasily observed by human eyes. Most previous related works about spontaneous versus posed smile recognition concentrate on extracting geometric features while appearance features are not fully used, leading to the loss of texture information. In this paper, we propose a region-specific texture descriptor to represent local pattern changes of different facial regions and compensate for limitations of geometric features. The temporal phase of each facial region is divided by calculating the intensity of the corresponding facial region rather than the intensity of only the mouth region. A mid-level fusion strategy of support vector machine is employed to combine the two feature types. Experimental results show that both our proposed appearance representation and its combination with geometry-based facial dynamics achieve favorable performances on four baseline databases: BBC, SPOS, MMI, and UvA-NEMO.
We propose a formation control strategy for multiple unmanned aerial vehicles (multi-UAV) based on second-order consensus, by introducing position and velocity coordination variables through neighbor-to-neighbor interaction to generate steering commands. A cooperative guidance algorithm and a cooperative control algorithm are proposed together to maintain a specified geometric configuration, managing the position and attitude respectively. With the whole system composed of the six-degree-of-freedom UAV model, the cooperative guidance algorithm, and the cooperative control algorithm, the formation control strategy is a closed-loop one and with full states. The cooperative guidance law is a second-order consensus algorithm, providing the desired acceleration, pitch rate, and heading rate. Longitudinal and lateral motions are jointly considered, and the cooperative control law is designed by deducing state equations. Closed-loop stability of the formation is analyzed, and a necessary and sufficient condition is provided. Measurement errors in position data are suppressed by synchronization technology to improve the control precision. In the simulation, three-dimensional formation flight demonstrates the feasibility and effectiveness of the formation control strategy.
Unconstrained offline handwriting recognition is a challenging task in the areas of document analysis and pattern recognition. In recent years, to sufficiently exploit the supervisory information hidden in document images, much effort has been made to integrate multi-layer perceptrons (MLPs) in either a hybrid or a tandem fashion into hidden Markov models (HMMs). However, due to the weak learnability of MLPs, the learnt features are not necessarily optimal for subsequent recognition tasks. In this paper, we propose a deep architecture-based tandem approach for unconstrained offline handwriting recognition. In the proposed model, deep belief networks are adopted to learn the compact representations of sequential data, while HMMs are applied for (sub-)word recognition. We evaluate the proposed model on two publicly available datasets, i.e., RIMES and IFN/ENIT, which are based on Latin and Arabic languages respectively, and one dataset collected by ourselves called Devanagari (an Indian script). Extensive experiments show the advantage of the proposed model, especially over the MLP-HMMs tandem approaches.
Robust object tracking has been an important and challenging research area in the field of computer vision for decades. With the increasing popularity of affordable depth sensors, range data is widely used in visual tracking for its ability to provide robustness to varying illumination and occlusions. In this paper, a novel RGBD and sparse learning based tracker is proposed. The range data is integrated into the sparse learning framework in three respects. First, an extra depth view is added to the color image based visual features as an independent view for robust appearance modeling. Then, a special occlusion template set is designed to replenish the existing dictionary for handling various occlusion conditions. Finally, a depth-based occlusion detection method is proposed to efficiently determine an accurate time for the template update. Extensive experiments on both KITTI and Princeton data sets demonstrate that the proposed tracker outperforms the state-of-the-art tracking algorithms, including both sparse learning and RGBD based methods.
To achieve fine segmentation of complex natural images, people often resort to an interactive segmentation paradigm, since fully automatic methods often fail to obtain a result consistent with the ground truth. However, when the foreground and background share some similar areas in color, the fine segmentation result of conventional interactive methods usually relies on the increase of manual labels. This paper presents a novel interactive image segmentation method via a regression-based ensemble model with semi-supervised learning. The task is formulated as a non-linear problem integrating two complementary spline regressors and strengthening the robustness of each regressor via semi-supervised learning. First, two spline regressors with a complementary nature are constructed based on multivariate adaptive regression splines (MARS) and smooth thin plate spline regression (TPSR). Then, a regressor boosting method based on a clustering hypothesis and semi-supervised learning is proposed to assist the training of MARS and TPSR by using the region segmentation information contained in unlabeled pixels. Next, a support vector regression (SVR) based decision fusion model is adopted to integrate the results of MARS and TPSR. Finally, the GraphCut is introduced and combined with the SVR ensemble results to achieve image segmentation. Extensive experimental results on benchmark datasets of BSDS500 and Pascal VOC have demonstrated the effectiveness of our method, and the com-parison with experiment results has validated that the proposed method is comparable with the state-of-the-art methods for in-teractive natural image segmentation.
We propose an efficient colocated multiple-input multiple-output radar waveform-design method based on two-step optimizations in the spatial and spectral domains. First, a minimum integrated side-lobe level strategy is adopted to obtain the desired beam pattern with spatial nulling. By recovering the hidden convexity of the resulting fractional quadratically constrained quadratic programming non-convex problem, the global optimal solution can be achieved in polynomial time through a semi- definite relaxation followed by spectral factorization. Second, with the transmit waveforms obtained via spatial optimization, a phase changing diagonal matrix is introduced and optimized via power method-like iterations. Without influencing the shape of the optimized beam pattern, the transmit waveforms are further optimized in the spectral domain, and the desired spectral nulling is formed to avoid radar interference on the overlaid licensed radiators. Finally, the superior performance of the proposed method is demonstrated via numerical results and comparisons with other approaches to waveform design.