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This paper uses the adaptive dynamic programming (ADP) method to achieve optimal trajectory tracking control for quadrotors. Relying on an established mathematical model of a quadrotor, the approximate optimal trajectory tracking control, which consists of the steady-state control input and the approximate optimal feedback control input, is designed for a nominal system. Considering the compound disturbances in position and attitude dynamic models, disturbance observers are introduced. The estimated values are used to design robust compensation inputs to suppress the effect of the compound disturbances for good trajectory tracking performance. Theoretically, the Lyapunov theorem demonstrates the stability of a closed-loop system. The robustness and effectiveness of the proposed controller are confirmed by the simulation results. [ABSTRACT FROM AUTHOR]

This paper presents a practical study on how to improve the ℋ ∞ performance and meet the input–output constraints of the two-degrees-of-freedom (DOF) flexible-joint manipulator system (FJMS) with parameter uncertainties and external disturbances. For this reason, a robust constrained moving-horizon ℋ ∞ controller is designed to improve the system ℋ ∞ performance while still satisfying the input–output constraints of the uncertain system. First, the uncertain controlled system model of the two-DOF FJMS is established via the Lagrange equation method, Spong's assumption, and the linear fractional transformation (LFT) technique. Then, the control requirements and input–output constraints of the uncertain system are transformed into the linear matrix inequality (LMI) via the theory of ℋ ∞ control and the full-block multiplier technique. Next, the LMI optimization problem refreshed by the current state is addressed at each sample moment with the idea of the moving-horizon control of the model predictive control (MPC), and the calculated gain is implemented to the nonlinear closed-loop system under the state feedback structure. The validity and feasibility of the designed control scheme is finally verified via the results of simulation experiments. [ABSTRACT FROM AUTHOR]

Data-driven soft sensor technology has been widely used in process monitoring, quality prediction, etc. However, there are dynamic time-varying and concept drift problems in industrial processes, making the accurate modeling of the soft sensor is still a challenging task. To solve the above problems, this paper proposes a concept drift adaptive dynamic partial least squares method. The method maps high-dimensional process data into a low-dimensional latent variable subspace. Dynamic information is introduced by establishing the dynamic regression relationship between process latent variables and quality latent variables. At the same time, transfer learning is used to align second-order statistics between latent variables of data from different distributions to solve the concept drift problem. The experiments on multiple industrial datasets show that the method proposed in this paper can effectively reduce prediction errors and improve the generalization ability of the model. [Display omitted] • Distribution alignment and dynamic system modeling are realized under NIPALS. • The objective function is organized into the form of Rayleigh quotient. • There are few hyperparameters and they have better interpretability. • Experiments show that our method can adapt to the change of data distribution well. [ABSTRACT FROM AUTHOR]

The production of sapphire single crystals with high quality and low cost is an important goal of the sapphire industry. The quality of seeding has a direct impact on the final growth quality of sapphire crystals when sapphire is prepared using the Kyropoulos method. Keeping the crystal/melt contact stable is essential for lowering the number of seeding defects. In order to reduce the preparation cost of sapphire, an optimization strategy integrating computational fluid dynamics, artificial neural networks, and genetic algorithm is proposed to optimize the growth parameters of crystal seeding. The optimization goals are to stabilize the crystal/melt growth interface and increase seeding effectiveness. Three growth parameters, namely, melt temperature, seed crystal rotation speed and pulling speed, are selected as optimization variables. The samples produced by numerical calculation are combined with the actual production data to train and test the neural networks. The genetic algorithm is coupled with artificial neural networks to solve the multi-objective conflict between improving seeding efficiency and reducing the formation of crystal defects in the seeding process. The influence of the mechanism of different combinations of seeding parameters on seeding quality is discussed, and the optimization of seeding growth process is realized. The results show that the optimized combination of parameters gained in this study can improve the seeding speed and ensure the stability of the solid–liquid interface. This work is helpful to reduce the formation of seeding defects, improve the seeding quality, and shorten the seeding time. [ABSTRACT FROM AUTHOR]

With the increasing proportion of solar grid-connected, the establishment of an accurate photovoltaic (PV) power prediction model is very important for safe operation and efficient dispatching of a power grid. Considering the multi-level periodicity of PV power caused by many factors, such as seasons and weather, a short-term PV power prediction model based on transfer component analysis is designed by introducing the idea of transfer learning. In order to measure the uncertainty of numerical weather prediction (NWP) and power sequence, a novel algorithm considering weather similarity and power trend similarity is proposed. First, the intrinsic trend is measured by extracting permutation entropy, variance, and mean from the historical PV power sequence. Second, weighting of NWP is accomplished based on the Pearson correlation coefficient. PV power data are divided into different clusters by K-medoids clustering. At the same time, the transfer component analysis alleviates the time-varying problem of data distribution caused by multi-level time periodicity and effectively improves the prediction accuracy of the model. Finally, simulation experiments are carried out on the PV power output dataset (PVOD). The results show that the prediction accuracy of the proposed method is better than the traditional methods, and the accuracy and applicability of the proposed method are verified. [ABSTRACT FROM AUTHOR]

The traditional soft sensor models are based on the independent and identical distribution assumption, which are difficult to adapt to changes in data distribution under multiple operating conditions, resulting in model performance deterioration. The domain adaptive transfer learning methods learn knowledge in different domains by means of distribution alignment, which can reduce the impact of data distribution differences, and effectively improve the generalization ability of the model. However, most of the existing models established by domain adaptation methods are static models, which cannot reflect the dynamic characteristics of the system, and have limited prediction accuracy when applied to dynamic system modeling under multiple operating conditions. The dynamic system modeling methods can effectively extract the dynamic characteristics of the data, but they cannot deal with the concept drift problem caused by the change of data distribution. This paper proposes a new dynamic transfer partial least squares method, which maps the high-dimensional process data into the low-dimensional latent variable subspace, establishes the dynamic regression relationship between the latent variables and the labels, and realizes the systematic dynamic modeling, at the same time, the model adds regular terms for distribution alignment and structure preservation, which realizes dynamic alignment of data distribution difference. The effectiveness of the proposed method is validated on three publicly available industrial process datasets. [Display omitted] • This paper proposes a novel dynamic transfer partial least squares algorithm (DTPLS). • DTPLS can solve the concept drift problem under the multiple working conditions. • Distribution alignment regularization is designed based on distribution differences. • Structure preservation regularization is designed based on smoothness assumptions. [ABSTRACT FROM AUTHOR]

Nuclear safety has always been a focal point in the field of nuclear applications. Mobile robots carrying specific sensors for nuclear-radiation detection have become an alternative to manual detection. This work presents an autonomous α/β radiation mapping framework, using a mobile robot carrying a light detection and ranging (LiDAR) and a nuclear-radiation-detection sensor. The method employs simultaneous localization and mapping (SLAM) techniques and radiation-detection sensors. Cartographer is adopted as a demonstration example to map the unknown environment. Radiation data are obtained through the radiation detection sensor and projected onto the environment map after coordinate alignment. The color-coded radiation map is shown on the environment map according to the dose rate. The simulation and real-environment experiments in a robot-operating system (ROS) validate the effectiveness of the proposed method in different radiation scenarios for both indoor and outdoor environments. [ABSTRACT FROM AUTHOR]

Aiming at the problem of mismatch between real-time data distribution and modeling data distribution caused by the change of working conditions in industrial process, which leads to the performance deterioration of the soft sensor model, a multi-source unsupervised soft sensor method based on joint distribution alignment and mapping structure preservation is proposed. Firstly, the method uses the hypergraph to establish the complex structure of feature and label, and clusters the hypergraph matrix in multiple views to completely construct the class pseudo label; then dynamic distribution alignment is used to adapt marginal distribution and conditional distribution between the data of historical working conditions and the current working conditions, and the hypergraph Laplacian operator is introduced for manifold regularization to prevent the mapping relationship between feature and label from being destroyed; finally, similar working conditions are introduced to further enhance the robustness of the model. The experimental results show that compared with the traditional unsupervised soft sensor methods, the method used in this paper can effectively improve the prediction accuracy of the model. • DASP constructs pseudo label to dynamically distribute adapt the continuous regression data. • The new label can retain the mapping relationship between the original data feature and label. • Several experimental results verify the effectiveness of the method. [ABSTRACT FROM AUTHOR]

For high-precision positioning and navigation systems, the positioning precision of receiver is jeopardized by multipath interference. Multipath suppression methods based on data processing have drawn much attention recently. The critical step of data processing-based method is to estimate multipath parameters. However, most multipath suppression methods falling into the category of data processing-based methods are limited to Gaussian noises, which means the performance of these methods may be degraded in non-Gaussian noises which are encountered quite often in reality. Besides, only static multipath case is studied in most existing literature, which is not sufficient for potential applications since the occurrence and the disappearance of multipath are always changeable along the movement of receiver. To address these problems, the maximum correntropy criterion(MCC) and the generalized maximum correntropy criterion(GMCC) are integrated into the traditional adaptive multipath estimation(AME) algorithm, named as MCC-AME and GMCC-AME, to handle the dynamic multipath estimation problem in non-Gaussian noises. Furthermore, MCC-AME and GMCC-AME are further improved by adopting forgetting factor, named as RMCC-AME and RGMCC-AME, to improve estimation accuracy and reduce time consumption in a recursive way. The four proposed algorithms also address the problems that the formerly proposed entropy-based multipath estimation algorithms are sensitive to the initial estimation and that the assumption of fixed number of multipath is required. The performance of the four proposed algorithms are analyzed and compared. The analytical results show that GMCC-AME outperforms MCC-AME regarding convergent speed, estimation accuracy and robustness, and RGMCC-AME performs even better than RMCC-AME in the same regard. [ABSTRACT FROM AUTHOR]

Conveyor belt longitudinal tear is one of the most serious problems in coal mining. It is very important to detect it in real‐time before the length of the tear is too long. In this study, a method of longitudinal tear detection based on visible light charge coupled device (CCD) and infrared CCD is proposed. A visible light and infrared vision real‐time detection system for conveyor belt longitudinal tear is designed based on this method. In this method, the infrared CCD uses adaptive histogram equalisation coordinating with Hough transform line detection. The visible light CCD, coordinating with a laser line source, uses the Hough transform algorithm and the features from accelerated segment test algorithm. These two kinds of CCDs work together to make detection results reliable. Experimental results show that the proposed method is effective and adaptive, and meets the requirements for reliable, real‐time and online longitudinal tear detection. Compared to several current methods, the proposed method has a better performance on efficiency of detection. [ABSTRACT FROM AUTHOR]

• Experiments are conducted to investigate the frequency spectrum characteristics. • Multiple channel mechanical signals are interpreted for mill load parameter (MLP). • A measurement method for the contribution rate of the mechanical signals is proposed. • Mechanical channels must be selected to construct an effective MLP prediction model. Online monitoring load parameters inside the ball mill is the key to improving the production quality and quantity of the mineral grinding process. In this paper, the experimental analysis of wet mill load parameter (MLPs) based on multiple channel mechanical signals is presented. A series of experiments is conducted to investigate the mechanical frequency spectrum characteristics in terms of different grinding conditions, such as only-ball, -mineral, or –water load change. Based on power spectra density (PowSD), multiple channel mechanical signals are interpreted for different MLPs, i.e., mineral-to-ball volume ratio (MBVR), pulp density (PD), and charge volume ratio (CVR), in detail. Experimental results show that the PowSDs of these mechanical signals are positively correlated with CVR and negatively correlated with MBVR and PD. Further, the generation mechanism of these mechanical signals is qualitatively analyzed, and a new measurement method for the contribution rate of multiple channel mechanical signals, i.e., combination estimation index, is proposed. The results show the different contribution rates of these signals to various MLPs under varied grinding conditions. Appropriate mechanical channels for different MLPs must be selected to construct an effective MLP forecasting model. [ABSTRACT FROM AUTHOR]

Existing key-frame extraction methods are basically video summary oriented; yet the index task of key-frames is ignored. This paper presents a novel key-frame extraction approach which can be available for both video summary and video index. First a dynamic distance separability algorithm is advanced to divide a shot into subshots based on semantic structure, and then appropriate key-frames are extracted in each subshot by SVD decomposition. Finally, three evaluation indicators are proposed to evaluate the performance of the new approach. Experimental results show that the proposed approach achieves good semantic structure for semantics-based video index and meanwhile produces video summary consistent with human perception. [ABSTRACT FROM AUTHOR]

Highlights • The OCS method abandons the template-based method of locking region of interest. • The OCS method establishes the convergence model of spoke pattern center. • The OCS method improves the effect of seeding operation. Abstract The seeding process is vital in the preparation of large-diameter sapphire single crystal. It is the key to detect the seeding point during the seeding process. The OCS method is proposed in the paper to detect the seeding point. The OCS method improves the detection method of spoke pattern center proposed by Churl Min Kim, based on this, and the convergence model of spoke pattern center is fitted, so that the real seeding point is detected. In the experiment, the OCS method is verified by comparing with the traditional manual seeding method (operated by skilled seeding technologists) and the method proposed by Churl Min Kim. The OCS method has the same effect as the traditional artificial seeding method and can reduce the number of attempts in a single seeding experiment. Compared with the method proposed by Churl Min Kim, the OCS method can meet the needs of actual industrial production in terms of the number of successful seeding, the number of seeding attempts and the average seeding time. [ABSTRACT FROM AUTHOR]

Impurity in transparent-bottled liquid is a serious production accident in the field of beverage and medicine industry. However, the existing detection systems are difficult to distinguish impurities with dynamic interference (bubbles and stains) and detect impurities located at the edge of the bottle. In order to solve the problems stated above, a new machine vision system for detecting tiny and dynamic impurities is proposed in this paper. In the system, circularity calculation, longitudinal frame-difference method, orthogonal-axis inspection and K-Nearest Neighbor (KNN) machine learning algorithm are combined together to realize the automatic and real-time detection. Experimental results demonstrate that, after completing machine learning, the weighted error of the proposed system for detecting impurities can be effectively controlled at about 0.9% even in dynamic interference environment, which is great significance to safety production in beverage and medicine industry. [ABSTRACT FROM AUTHOR]

In mining operation, the longitudinal tear detection of conveyor belt is of great significance to safety production. A novel method, named Dual Band Infrared Detection (DBID), is proposed to detect the longitudinal tear of conveyor belts in this paper. The DBID method is based on the collaboration of mid-infrared and long infrared vision. The DBID sensor device captures the lower surface images of conveyor belt. The feature of tear or scratch and tearing precursor can be extracted by analyzation of captured images. The DBID method is verified by experiments and practice, including the capturing of dual band infrared images (mid-infrared and long infrared images), image preprocessing, and tear detection. The DBID method is not only unlimited by visual condition, but also much more accurate and reliable compared with the Integrative Binocular Vision Detection (IBVD) method presented before which meets the requirement of real-time online detection. [ABSTRACT FROM AUTHOR]