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The satellite pointing accuracy plays a crucial role in ensuring a successful satellite mission itself. Therefore, this paper aims to enhance the attitude pointing accuracy of the combined energy and attitude control system (CEACS) in a satellite in the presence of external disturbance torques through a robust controller, which can produce high pointing accuracies with smaller control torques.

To improve the CEACS attitude pointing accuracy, a maiden fuzzy proportional derivative (PD)-based CEACS architecture is proposed. The mathematical models along with its numerical treatments of the fuzzy PD-based CEACS attitude control architecture are presented. In addition, a comparison between the PD and fuzzy PD controllers in terms of the CEACS pointing accuracies and control torques is provided.

Numerical results show that the fuzzy PD controller produces a considerable CEACS pointing accuracy improvement for a lower control torque compartment.

CEACS has gained a renew interest because of significant increase in the projected onboard power requirements for future space missions. Therefore, it is of paramount importance to improve the CEACS pointing accuracy itself with a minimum control torque compartment. In fact, this proposed fuzzy PD controller is shown to be a potential CEACS attitude controller.

The fuzzy PD-based CEACS architecture not only provides a better attitude pointing accuracy but also ensures a lower control torque compartment, which corresponds to a lower onboard power consumption.

Attitude determination and control subsystem (ADCS) is a vital part of earth observation satellites (EO-Satellites) that governs the satellite’s rotational motion and pointing. In designing such a complicated sub-system, many parameters including mission, system and performance requirements (PRs), as well as system design parameters (DPs), should be considered. Design cycles which prolong the time-duration and consequently increase the cost of the design process are due to the dependence of these parameters to each other. This paper aims to describe a rapid-sizing method based on the design for performance strategy, which could minimize the design cycles imposed by conventional methods.

The proposed technique is an adaptation from that used in the aircraft industries for aircraft design and provides a ball-park figure with little engineering man-hours. The authors have shown how such a design technique could be generalized to cover the EO-satellites platform ADCS. The authors divided the system requirements into five categories, including maneuverability, agility, accuracy, stability and durability. These requirements have been formulated as functions of spatial resolution that is the highest level of EO-missions PRs. To size, the ADCS main components, parametric characteristics of the matching diagram were determined by means of the design drivers.

Integrating the design boundaries based on the PRs in critical phases of the mission allowed selecting the best point in the design space as the baseline design with only two iterations. The ADCS of an operational agile EO-satellite is sized using the proposed method. The results show that the proposed method can significantly reduce the complexity and time duration of the performance sizing process of ADCS in EO-satellites with an acceptable level of accuracy.

Rapid performance sizing of EO-satellites ADCS using matching diagram technique and consequently, a drastic reduction in design time via minimization of design cycles makes this study novel and represents a valuable contribution in this field.

Detecting and tracking the position of a mobile user has become one of the important subjects in many mobile applications. Such applications use location based services (LBS) for learning and training user movements in different places (cities, markets, airports, stations) along different modes of transport (bus, car, cycle, walk). To date, GPS is the key solution to all LBS but repeated GPS querying is not economical in terms of the battery life of the mobile phone. The purpose of this paper is to study how cheap and energy‐efficient air pressure sensors measuring the altitude could be used, as a complement to the dominant GPS system. The location detection and route tracking task is then accomplished by matching the collected altitude traces with the altitude curves of stored data to find the best matching routes.

The cornerstone of the authors' approach is that a huge amount of route data, collected with GPS devices, is available in various cloud services. In order to evaluate the mechanism of matching routes with altitude data, the authors build a prototype system of crowd‐sourced database containing only altitude data of different routes along different modes of transport. How accurately this stored altitude data could be matched with the collected altitude traces is the key question of this study.

Results show that, within a certain level of accuracy, older repeated routes can be detected from newly tracked altitude traces. Further, the level of accuracy varies depending on the length of path traversed, route curvature, speed of travel and sensor used for tracking.

The new contribution in this paper is to propose an alternative route detection mechanism which minimizes the use of GPS query. This concept will help in retrieving the GPS coordinates of already traversed routes stored in a large database by matching them with currently tracked altitude curves.

The purpose of this paper is to verify the correctness and feasibility of simultaneous localization and mapping (SLAM) algorithm based on red-green-blue depth (RGB-D) camera in high precision navigation and localization of celestial exploration rover.

First, a positioning algorithm based on depth camera is proposed. Second, the realization method is described from the five aspects of feature detection method, feature point matching, point cloud mapping, motion estimation and high precision optimization. Feature detection: taking the precision, real-time and motion basics as the comprehensive consideration, the ORB (oriented FAST and rotated BRIEF) features extraction method is adopted; feature point matching: solves the similarity measure of the feature descriptor vector and how to remove the mismatch point; point cloud mapping: the two-dimensional information on RGB and the depth information on D corresponding; motion estimation: the iterative closest point algorithm is used to solve point set registration; and high precision optimization: optimized by using the graph optimization method.

The proposed high-precision SLAM algorithm is very effective for solving high precision navigation and positioning of celestial exploration rover.

In this paper, the simulation validation is based on an open source data set for testing; the physical verification is based on the existing unmanned vehicle platform to simulate the celestial exploration rover.

This paper presents a RGB-D camera-based navigation algorithm, which can be obtained by simulation experiment and physical verification. The real-time and accuracy of the algorithm are well behaved and have strong applicability, which can support the tests and experiments on hardware platform and have a better environmental adaptability.

The proposed SLAM algorithm can deal with the high precision navigation and positioning of celestial exploration rover effectively. Taking into account the current wide application prospect of computer vision, the method in this paper can provide a study foundation for the deep space probe.

This paper aims to Test the capabilities/accuracies of four deep learning pre trained convolutional neural network (CNN) models to detect and classify types of highway cracks, as well as developing a new CNN model to maximize the accuracy at different learning rates.

A sample of 4,663 images of highway cracks were collected and classified into three categories of cracks, namely, “vertical cracks,” “horizontal and vertical cracks” and “diagonal cracks,” subsequently, using “Matlab” to classify the sample to training (70%) and testing (30%) to apply the four deep learning CNN models and compute their accuracies. After that, developing a new deep learning CNN model to maximize the accuracy of detecting and classifying highway cracks and testing the accuracy using three optimization algorithms at different learning rates.

The accuracies result of the four deep learning pre-trained models are above the averages between top-1 and top-5 and the accuracy of classifying and detecting the samples exceeded the top-5 accuracy for the pre-trained AlexNet model around 3% and by 0.2% for the GoogleNet model. The accurate model here is the GoogleNet model as the accuracy is 89.08% and it is higher than AlexNet by 1.26%. While the computed accuracy for the new created deep learning CNN model exceeded all pre-trained models by achieving 97.62% at a learning rate of 0.001 using Adam’s optimization algorithm.

The created deep learning CNN model will enable users (e.g. highway agencies) to scan a long highway and detect types of cracks accurately in a very short time compared to traditional approaches.

A new deep learning CNN-based highway cracks detection was developed based on testing four pre-trained CNN models and analyze the capabilities of each model to maximize the accuracy of the proposed CNN.

High order schemes, which are widely used in DNS and LES, received increasing attention in recent years with a number of variants being developed. However most of these schemes have difficulties in achieving high order accuracy near the boundary points. In order to solve this problem, the analytical discrete method (ADM) is proposed and presented in this paper. In addition, this method is convenient to construct the higher order WENO (weighted essentially non‐oscillatory) scheme. Application of the ADM‐WENO scheme to shock‐tube problems and compressible mixing flows has shown it is robust and accurate in both shock‐capturing and complex flow structures detection.

The utilisation of emerging technologies for the inspection of bridges has remarkably increased. In particular, non-destructive testing (NDT) technologies are deemed a potential alternative for costly, labour-intensive, subjective and unsafe conventional bridge inspection regimes. This paper aims to develop a framework to overcome conventional inspection regimes' limitations by deploying multiple NDT technologies to carry out digital visual inspections of masonry railway bridges.

This research adopts an exploratory case study approach, and the empirical data is collected through exploratory workshops, interviews and document reviews. The framework is implemented and refined in five masonry bridges as part of the UK railway infrastructure. Four NDT technologies, namely, terrestrial laser scanner, infrared thermography, 360-degree imaging and unmanned aerial vehicles, are used in this study.

A digitally enhanced visual inspection framework is developed by using complementary optical methods. Compared to the conventional inspection regimes, the new approach requires fewer subjective interpretations due to the additional qualitative and quantitative analysis. Also, it is safer and needs fewer operators on site, as the actual inspection can be carried out remotely.

This research is a step towards digitalising the inspection of bridges, and it is of particular interest to transport agencies and bridge inspectors and can potentially result in revolutionising the bridge inspection regimes and guidelines.

In multidisciplinary design optimization (MDO), if the relationships between design variables and some output parameters, which are important performance constraints, are complex implicit problems, plenty of time should be spent on computationally expensive simulations to identify whether the implicit constraints are satisfied with the given design variables during the optimization iteration process. The purpose of this paper is to propose an ensemble of surrogates-based analytical target cascading (ESATC) method to tackle such MDO engineering design problems with reduced computational cost and high optimization accuracy.

Different surrogate models are constructed based on the sample point sets obtained by Latin hypercube sampling (LHS) method. Then, according to the error metric of each surrogate model, the repeated ensemble of surrogates is constructed to approximate the implicit objective functions and constraints. Under the framework of analytical target cascading (ATC), the MDO problem is decomposed into several optimization subproblems and the function of analysis module of each subproblem is simulated by repeated ensemble of surrogates, working together to find the optimum solution.

The proposed method shows better modeling accuracy and robustness than other individual surrogate model-based ATC method. A numerical benchmark problem and an industrial case study of the structural design of a super heavy vertical lathe machine tool are utilized to demonstrate the accuracy and efficiency of the proposed method.

This paper integrates a repeated ensemble method with ATC strategy to construct the ESATC framework which is an effective method to solve MDO problems with implicit constraints and black-box objectives.

This paper aims to address the spacecraft attitude control problem using hybrid actuators in the presence of actuator saturation, uncertainties and faults, inertia uncertainties and external disturbances.

A hybrid actuator configuration is used where thrusters are engaged for rapid attitude maneuvers, while reaction wheels (RWs) are used for fine pointing.

The key advantages are two-fold: a finite-time high-level controller is designed to produce the three-axis virtual control torques; an online robust control allocation (RobCA) scheme is proposed to redistribute virtual control signals to the actuators with taking into account the actuator saturation, uncertainties and faults; and the RobCA scheme allows a smooth switch between thrusters and RWs, which handles the inaccuracy problem of thrusters and saturation problem of RWs.

An online RobCA algorithm is designed that maps the total control demands onto individual actuator settings and allows a smooth switch between thrusters and RWs. Simulation results show the effectiveness of the proposed control strategy.

This work may be used on modern space missions, which impose higher requirements on smooth switching of spacecraft thrusters and RWs.

The purpose of this paper is to solve the problem that the analytic solution model of spatial three-dimensional coordinate measuring system based on dual-position sensitive detector (PSD) is complex and its precision is not high.

A new three-dimensional coordinate measurement algorithm by optimizing back propagation (BP) neural network based on genetic algorithm (GA) is proposed. The mapping relation between three-dimensional coordinates of space points in the world coordinate system and light spot coordinates formed on dual-PSD has been built and applied to the prediction of three-dimensional coordinates of space points.

The average measurement error of three-dimensional coordinates of space points at three-dimensional coordinate measuring system based on dual-PSD based on GA-BP neural network is relatively small. This method does not require considering the lens distortion and the non-linearity of PSD. It has simple structure and high precision and is suitable for three-dimensional coordinate measurement of space points.

A new three-dimensional coordinate measurement algorithm by optimizing BP neural network based on GA is proposed to predict three-dimensional coordinates of space points formed on three-dimensional coordinate measuring system based on dual-PSD.