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This study aims to obtain the dynamic behaviours of cracked rod-fastening rotor bearing system (RFBS), and experimental investigation was carried out to examine the dynamic characteristics of this kind of assembled rotor bearing system with a transverse crack passing through the critical speed.

An experimental test rig of cracked RFBS was established for examining the vibration behaviours between intact and cracked system. The crack on the surface of a fastening rod was simulated by wire-electrode cutting processing method. The comprehensive analysis method of vibration was used to obtain the dynamic characteristics such as vibration amplitude, acceleration and whirling orbits before and after the critical speed as well as the instantaneous response in the process of speed up.

Some experimental vibration datum is obtained for cracked RFBS. The appearance of a crack will introduce the initial bending and make the vibration amplitude, acceleration and instant response in the process of speed up increase greatly as well as the change of whirling orbits.

The actual vibration characteristics for this complex assembled rotor system with a transverse crack are given passing through the critical speed. It can provide some useful help for monitoring the vibration behaviours of this kind of assembled rotor system as well as the detection of the crack fault.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-07-2020-0260/

This paper aims to examine the dynamic behaviours of a three-dimensional (3D) rod-fastening rotor bearing system (RFBS) with a crack in a fastening rod.

Based on the 3D finite element method model and stress analysis of a cracked RFBS, a 3D dynamic model of the RFBS with a crack in a fastening rod is established with considering the initial bending and stress redistribution caused by the crack. A combined numerical simulation technology is used to investigate the dynamic behaviours of the system.

The distribution of contact stress between the two disks will be not uniform, and the initial bending of the system will occur due to the presence of a crack. This will lead to the change of system stiffness and the dynamic behaviours such as vibration amplitude, and motion orbits will change significantly.

A 3D finite element method dynamic model is proposed for the study of dynamic characteristics of complex combined rotor bearing system with cracks.

It is helpful and significant to master the dynamic behaviours of cracked RFBS. It is helpful to detect the presence of a crack of the rotor bearing system.

Some of the losses caused by crack failure may be reduced.

The proposed 3D method can provide a useful reference for the study of dynamic characteristics of complex combined rotor bearing system with cracks.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2020-0189

This paper aims to propose a novel model predictive control (MPC) with time varying weights to develop a lateral control law in an automatic carrier landing system (ACLS), which minimizes landing risk and improves flight quality.

First, a nonlinear mathematic model of an F/A-18 aircraft during lateral landing is established. Then the landing model is linearized in the form of state deviations on the equilibrium points. Second, landing risk windows are proposed and a high-dimensional landing risk model is addressed through a back propagation (BP) neural network. The trained samples are acquired based on a pilot behavior model. Third, time varying weights created from the lateral landing risk are introduced into the performance function of MPC. Optimal solution is solved quicker and some state deviations are focused on and eliminated. Fourth, the algebraic inequalities are substituted by the linear matrix inequalities (LMIs), which are easily calculated by the computers.

On a semi-physical platform, the proposed method compares with a traditional MPC algorithm and a modified MPC with an additional term. The test results indicate that the proposed algorithm brings about an excellent landing performance as well as an ability of eliminating landing risk.

The landing phase of a carrier-based aircraft is one of the most dangerous and complicated stages, and the algorithm proposed by this paper plays a vital role in the lateral landing.

This paper establishes a lateral landing risk model, which considers not only the current landing state but also the future touchdown point. This lateral landing risk is integrated into the time varying weights of the MPC algorithm so that the state deviations and landing risk can be both reduced in the rolling optimization.

Because of their large field of view, omnistereo vision systems have been widely used as primary vision sensors in autonomous mobile robot tasks. The purpose of this article is to achieve real-time and accurate tracking by the omnidirectional vision robot system.

The authors provide in this study the key techniques required to obtain an accurate omnistereo target tracking and location robot system, including stereo rectification and target tracking in complex environment. A simple rectification model is proposed, and a local image processing method is used to reduce the computation time in the localization process. A target tracking method is improved to make it suitable for omnidirectional vision system. Using the proposed methods and some existing methods, an omnistereo target tracking and location system is established.

The experiments are conducted with all the necessary stages involved in obtaining a high-performance omnistereo vision system. The proposed correction algorithm can process the image in real time. The experimental results of the improved tracking algorithm are better than the original algorithm. The statistical analysis of the experimental results demonstrates the effectiveness of the system.

A simple rectification model is proposed, and a local image processing method is used to reduce the computation time in the localization process. A target tracking method is improved to make it suitable for omnidirectional vision system. Using the proposed methods and some existing methods, an omnistereo target tracking and location system is established.

A switching depth controller based on a variable buoyancy system (VBS) is proposed to improve the performance of small autonomous underwater vehicles (AUVs). First, the requirements of VBS for small AUVs are analyzed. Second, a modular VBS with high extensibility and easy integration is proposed based on the concepts of generality and interchangeability. Subsequently, a depth-switching controller is proposed based on the modular VBS, which combines the best features of the linear active disturbance rejection controller and the nonlinear active disturbance rejection controller.

The controller design and endurance of tiny AUVs are challenging because of their low environmental adaptation, limited energy resources and nonlinear dynamics. Traditional and single linear controllers cannot solve these problems efficiently. Although the VBS can improve the endurance of AUVs, the current VBS is not extensible for small AUVs in terms of the differences in individuals and operating environments.

The switching controller’s performance was examined using simulation with water flow and external disturbances, and the controller’s performance was compared in pool experiments. The results show that switching controllers have greater effectiveness, disturbance rejection capability and robustness even in the face of various disturbances.

A high degree of standardization and integration of VBS significantly enhances the performance of small AUVs. This will help expand the market for small AUV applications.

This solution improves the extensibility of the VBS, making it easier to integrate into different models of small AUVs. The device enhances the endurance and maneuverability of the small AUVs by adjusting buoyancy and center of gravity for low-power hovering and pitch angle control.

Robot localization in dynamic, cluttered environments is a challenging problem because it is impractical to have enough knowledge to be able to accurately model the robot’s environment in such a manner. This study aims to develop a novel probabilistic method equipped with function approximation techniques which is able to appropriately model the data distribution in Markov localization by using the maximum statistical power, thereby making a sensibly accurate estimation of robot’s pose in extremely dynamic, cluttered indoors environments.

The parameter vector of the statistical model is in the form of positions of easily detectable artificial landmarks in omnidirectional images. First, using probabilistic principal component analysis, the most likely set of parameters of the environmental model are extracted from the sensor data set consisting of missing values. Next, we use these parameters to approximate a probability density function, using support vector regression that is able to calculate the robot’s pose vector in each state of the Markov localization. At the end, using this density function, a good approximation of conditional density associated with the observation model is made which leads to a sensibly accurate estimation of robot’s pose in extremely dynamic, cluttered indoors environment.

The authors validate their method in an indoor office environment with 34 unique artificial landmarks. Further, they show that the accuracy remains high, even when they significantly increase the dynamics of the environment. They also show that compared to those appearance-based localization methods that rely on image pixels, the proposed localization strategy is superior in terms of accuracy and speed of convergence to a global minima.

By using easily detectable, and rotation, scale invariant artificial landmarks and the maximum statistical power which is provided through the concept of missing data, the authors have succeeded in determining precise pose updates without requiring too many computational resources to analyze the omnidirectional images. In addition, the proposed approach significantly reduces the risk of getting stuck in a local minimum by eliminating the possibility of having similar states.

When it comes to the high accuracy autonomous motion of the mobile robot, it is challenging to effectively control the robot to follow the desired trajectory and transport the payload simultaneously, especially for the cloud robot system. In this paper, a flexible trajectory tracking control scheme is developed via iterative learning control to manage a distributed cloud robot (BIT-6NAZA) under the payload delivery scenarios.

Considering the relationship of six-wheeled independent steering in the BIT-6NAZA robot, an iterative learning controller is implemented for reliable trajectory tracking with the payload transportation. Meanwhile, the stability analysis of the system ensures the effective convergence of the algorithm.

Finally, to evaluate the developed method, some demonstrations, including the different motion models and tracking control, are presented both in simulation and experiment. It can achieve flexible tracking performance of the designed composite algorithm.

This paper provides a feasible method for the trajectory tracking control in the cloud robot system and simultaneously promotes the robot application in practical engineering.