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The purpose of this paper is to solve the ground verification and test method for space robot system capturing the target satellite based on visual servoing with time-delay in 3-dimensional space prior to space robot being launched.

To implement the approaching and capturing task, a motion planning method for visual servoing the space manipulator to capture a moving target is presented. This is mainly used to solve the time-delay problem of the visual servoing control system and the motion uncertainty of the target satellite. To verify and test the feasibility and reliability of the method in three-dimensional (3D) operating space, a set of ground hardware-in-the-loop simulation verification systems is developed, which adopts the end-tip kinematics equivalence and dynamics simulation method.

The results of the ground hardware-in-the-loop simulation experiment validate the reliability of the eye-in-hand visual system in the 3D operating space and prove the validity of the visual servoing motion planning method with time-delay compensation. At the same time, owing to the dynamics simulator of the space robot added in the ground hardware-in-the-loop verification system, the base disturbance can be considered during the approaching and capturing procedure, which makes the ground verification system realistic and credible.

The ground verification experiment system includes the real controller of space manipulator, the eye-in-hand camera and the dynamics simulator, which can veritably simulate the capturing process based on the visual servoing in space and consider the effect of time delay and the free-floating base disturbance.

The purpose of this paper is to develop an easily implemented and practical stabilizing strategy for the hardware-in-the-loop (HIL) system. As the status of HIL system in the ground verification experiment for space equipment keeps rising, the stability problems introduced by high stiffness of industrial robot and discretization of the system need to be solved ungently. Thus, the study of the system stability is essential and significant.

To study the system stability, a mathematical model is built on the basis of control circle. And root-locus and 3D root-locus method are applied to the model to figure out the relationship between system stability and system parameters.

The mathematical model works well in describing the HIL system in the process of capturing free-floating targets, and the stabilizing strategy can be adopted to improve the system dynamic characteristic which meets the needs of the practical application.

A method named 3D root-locus is extended from traditional root-locus method. And the improved method graphically displays the stability of the system under the influence of multivariable. And the strategy that stabilize the system with elastic component has a strong feasible and promotional value.

The aim of this paper is the implementation and validation of control and guidance algorithms for unmanned aerial vehicle (UAV) autopilots.

The path-following control of the UAV can be separated into different layers: inner loop for pitch and roll attitude control, outer loop on heading, altitude and airspeed control for the waypoints tracking and waypoint navigation. Two control laws are defined: one based on proportional integrative derivative (PID) controllers both for inner and outer loops and one based on the combination of PIDs and an adaptive controller.

Good results can be obtained in terms of trajectory tracking (based on waypoints) and of parameter variations. The adaptive control law guarantees smoothing responses and less oscillations and glitches on the control deflections.

The proposed controllers are easily implementable on-board and are computationally efficient.

The algorithm validation via hardware in the loop simulations can be used to reduce the platform set-up time and the risk of losing the prototype during the flight tests.

The paper presents conception and practical realization of laboratory testing of control system for small aircraft. The stand used in these tests is prepared in Matlab and Simulink environment. Usage of xPC and RTW modules enables the system to perform “Hardware in the loop” real time tests including tests with pilots. As a hardware PC computer equipped with specialized boards, including A/C, C/A, converters and RS232, LPT, CAN 2B data lines was used. The main task of presented stand is to make possible tests of control system introducing new conception of aircraft controlling (e.g. smart control and decoupled control techniques).

The purpose of this paper is to introduce a new danger‐aware Operational Flight Program (OFP) for the unmanned helicopter's auto‐navigation based on the well‐known time‐triggered message‐triggered object (TMO) model.

In this design with the TMO, the danger‐awareness means two things. First, an unmanned helicopter maneuvers on safe altitudes to avoid buildings or mountains when navigating to the target position. It is assumed that minimum safe altitudes are given on evenly spaced grids and on the center points of every four adjacent grids. A three‐dimensional (3D) path‐finding algorithm using this safe‐altitude information is proposed. Second, a helicopter automatically avoids a zone with very high temperature caused by a fire.

Since the auto‐flight control system requires componentized real‐time processing of sensors and controllers, the TMO model that has periodic and sporadic threads as members, has been used in designing the OFP. It has been found that using the TMO scheme is a way to construct a very flexible, well‐componentized and timeliness‐guaranteed OFP.

As the RTOS, RT‐eCos has been used. It was developed a few years ago based on the eCos3.0 to support the real‐time thread model of the TMO scheme. To verify this navigation system, a hardware‐in‐the‐loop simulation (HILS) system also has been developed.

Designing an OFP by using the real‐time object model TMO and the proposed 3D safe path finding algorithm is a whole new effective deadline‐based approach. And the developed OFP can be used intensively in the phase of disaster response and recovery.

It would take billions of miles’ field road testing to demonstrate that the safety of automated vehicle is statistically significantly higher than the safety of human driving because that the accident of vehicle is rare event.

This paper proposes an accelerated testing method for automated vehicles safety evaluation based on improved importance sampling (IS) techniques. Taking the typical cut-in scenario as example, the proposed method extracts the critical variables of the scenario. Then, the distributions of critical variables are statistically fitted. The genetic algorithm is used to calculate the optimal IS parameters by solving an optimization problem. Considering the error of distribution fitting, the result is modified so that it can accurately reveal the safety benefits of automated vehicles in the real world.

Based on the naturalistic driving data in Shanghai, the proposed method is validated by simulation. The result shows that compared with the existing methods, the proposed method improves the test efficiency by 35 per cent, and the accuracy of accelerated test result is increased by 23 per cent.

This paper has three contributions. First, the genetic algorithm is used to calculate IS parameters, which improves the efficiency of test. Second, the result of test is modified by the error correction parameter, which improves the accuracy of test result. Third, typical high-risk cut-in scenarios in China are analyzed, and the proposed method is validated by simulation.

This paper seeks to present a fully digital, real‐time (RT) hardware‐in‐the‐loop (HIL) simulator on PC‐cluster, of electric systems and drives for research and education purposes; to use the developed system to conduct several motor drives implementation and to evaluate the motor and the control algorithm performance in RT.

This simulator was developed with the aim of meeting the simulation needs of electromechanical drives and power electronics systems while solving the limitations of traditional RT simulators. This simulator has two main subsystems, software and hardware. The two subsystems were coordinated together to achieve the RT simulation. The software subsystem includes MATLAB/Simulink environment, a C++ compiler and RT shell. The hardware subsystem includes FPGA data acquisition card, the control board, the sensors, and the controlled motor.

The complexity of RT implementation of motor drives is greatly reduced by utilizing this simulator. The detailed operation and implementation of this simulator are presented, together with test results and comparisons with simulated virtual environment for a permanent magnet dc and induction motors (IM). The simulator performance is adequate for both open and closed loops motor drives. The simulation time step is limited by the system Master/Target CPU's speed, the communication network type, and the complexity of the control algorithm.

A typical application for this system is to select and evaluate the performance of electric motors for a hybrid electric vehicle in a real vehicle environment without actually installing that component in the real vehicle.

The use of the developed RT simulator to achieve HIL simulation allows rapid prototyping, converter‐inverter topologies testing, motors testing, and control strategies evaluation. The transition from simulated virtual environment to the HIL mode can be performed by replacing the model of the physical system (e.g. motor) with the DAQ blocks to represent the channels connected to the physical system sensors. The use of a single environment for both simulation and HIL control provides a quick experimentation and performance comparison between the real and simulated systems.

The purpose of this paper is to present the development of hardware‐in‐the‐loop simulation (HILS) for visual target tracking of an octorotor unmanned aerial vehicle (UAV) with onboard computer vision.

HILS for visual target tracking of an octorotor UAV is developed by integrating real embedded computer vision hardware and camera to software simulation of the UAV dynamics, flight control and navigation systems run on Simulink. Visualization of the visual target tracking is developed using FlightGear. The computer vision system is used to recognize and track a moving target using feature correlation between captured scene images and object images stored in the database. Features of the captured images are extracted using speed‐up robust feature (SURF) algorithm, and subsequently matched with features extracted from object image using fast library for approximate nearest neighbor (FLANN) algorithm. Kalman filter is applied to predict the position of the moving target on image plane. The integrated HILS environment is developed to allow real‐time testing and evaluation of onboard embedded computer vision for UAV's visual target tracking.

Utilization of HILS is found to be useful in evaluating functionality and performance of the real machine vision software and hardware prior to its operation in a flight test. Integrating computer vision with UAV enables the construction of an unmanned system with the capability of tracking a moving object.

HILS for visual target tracking of UAV described in this paper could be applied in practice to minimize trial and error in various parameters tuning of the machine vision algorithm as well as of the autopilot and navigation system. It also could reduce development costs, in addition to reducing the risk of crashing the UAV in a flight test.

A HILS integrated environment for octorotor UAV's visual target tracking for real‐time testing and evaluation of onboard computer vision is proposed. Another contribution involves implementation of SURF, FLANN, and Kalman filter algorithms on an onboard embedded PC and its integration with navigation and flight control systems which enables the UAV to track a moving object.

A high-fidelity simulation platform helps to verify the feasibility of the controller and reduce the cost of subsequent experiments. Therefore, this paper aims to design a high-fidelity hardware-in-the-loop (HIL) simulation platform for the tail-sitter vehicles.

The component breakdown approach is used to develop a more reliable model. Thruster dynamics and ground contact force are also modeled. Accurate aerodynamic coefficients are obtained through wind tunnel tests. This simulation system adopts a mode transition method to achieve continuous simulation for all flight modes.

Simulation results are in good agreement with the flight log and successfully predict the state of the vehicle.

First, the effects of the propeller slipstream are considered. Second, most researchers ignore the parasitic drag caused by the landing gear and other appendages, which is discussed in this study. Third, a ground contact model is implemented to allow a realistic simulation of the takeoff and landing phases. Fourth, complete wind tunnel tests are conducted to obtain more accurate aerodynamic coefficients. Finally, a mode transition method is deployed in the HIL simulation system to achieve continuous simulation for all flight modes.