Unmanned aerial vehicle (UAV) application in visual object tracking requires a gimbal to stabilize the camera in following the object’s movement. External disturbance and gimbal stability are the issues in this application. This study aims to analyze disturbance effects and study the stress and modal analysis on UAV gimbal using flexible body concepts. The three-dimensional (3D) gimbal is modeled using the RecurDyn software consisting of 3 arms and a camera. Each of the arms is connected using a revolute joint and a rotational force to represent a motor. The considered disturbances are step, pulse, ramp, and sine wave input. The PID controller is used to stabilize the gimbal arm from the gravity of the camera and external disturbance. The result shows that the PID controller is robust to step, pulse, and ramp disturbance, but not to the sin wave disturbance. In addition, the second arm of the gimbal is the most stressed component and is prone to vibration.

Stability Analysis of 3D UAV Gimbal using Flexible Body

In order to improve the quality of students' practical training, a real-time detection system experimental platform based on machine vision has been designed. The system has functions of real-time sampling, detection, defective product removal, result display and data storage, and its structure covers three parts: hardware platform, detection algorithm, and software function implementation. The hardware platform includes image acquisition module, material transportation module and defective product removal module, and the control circuit is based on the development of Arduino and STM32. The detection algorithm is developed based on OPENCV, and it is designed to detect three types of defects: cup rim concavity, cracking, and out-of-roundness. The software system is jointly developed using Visual Studio 2019 and Qt Creator 4.9.1, and it has good interactivity. With this experimental platform, teaching work such as image acquisition system construction, dynamic sampling problem analysis, image detection algorithm development, and interactive interface implementation can be carried out, so as to improve students' engineering design ability in many aspects.

An experimental teaching platform for machine vision-based real-time detection taking quality inspection of paper cups as instance

This paper discusses identification method of a class of Wiener nonlinear systems based on adaptive neural fuzzy networks. The Wiener system in current research is comprised of a dynamic linear block modelled by transfer function and a static nonlinear block approximated by a five-layer adaptive neural fuzzy networks. The special test signals involving separable signals and random signals are designed to achieve separation identification of the Wiener system. Firstly, we estimate the unknown parameters of the transfer function by applying the correlation analysis technique depending on separable signals input-output measurements. Secondly, employing the cluster method and the recursive least squares algorithm to estimate the adaptive neural fuzzy networks. The simulation results demonstrate that the developed identification approach can identify with effect the Wiener systems based on adaptive neural fuzzy networks and the adaptive neural fuzzy networks can well model the nonlinearity of the Wiener system.

Identification Algorithm for Wiener Nonlinear Systems Based on Adaptive Neural Fuzzy Networks

The emergence of COVID-19 has reduced the opportunities for offline meetings, making people's work and study more transfer to the internet platform. However, the viewing angle and distance of the camera cannot be considered both. Therefore, machine vision is used to identify and track the presenter, and the camera pan-tilt control function of automatically tracking the presenter is realized. In many tests, the target tracking function works normally and works well. The experimental design involves relatively comprehensive disciplines, with good functional scalability and high practicability. It is an innovative experiment integrating robotics teaching, machine learning practice and embedded systems.

Experimental Design of Self-Tracking Camera Gimbal Robot Based on OpenCV

This paper discusses a modeling and identification scheme of the Wiener model using long short term memory (LSTM) neural network. The Wiener model is composed of a dynamic linear block and a static nonlinear block, in which the linear block is modeled by ARX model, the nonlinear block is modeled by LSTM neural network, and special test signals composed of Gaussian signal and real wind power generation data are employed to realize parameter identification of each block for the Wiener model. Firstly, the input-output of Gaussian signal is used to estimate the dynamic linear block by adopting correlation analysis theory. Then, the input-output of random signal is used to identify the nonlinear block using stochastic optimization method of adaptive momentum. Simulation results show that the scheme proposed can effectively identify the Wiener model based on LSTM neural network and predict the power of wind power generation accurately.

Parameter Identification of Wiener Model Based on LSTM Neural Network

This paper discusses a scheme for identifying the Hammerstein output error system with time-delay state-space model. The Hammerstein system is composed of a static nonlinear block approximated by neural fuzzy model and a dynamical linear block modeled by time-delay state-space model, and the parameters learning separation of the Hammerstein system is realized by using special test signal composed of Gaussian signal and random signal. Firstly, according to the properties of unit backward operator, the state space system can be transformed into a transfer function, and the parameters of the linear block can be estimated by the virtue of correlation analysis method based on measurement input-output data of Gaussian signals. In addition, based on random signals, the parameters of static nonlinear block and noise model are learned by using the recursive extended least square algorithm. Simulation results show that the scheme proposed can effectively learn the time-delay-based Hammerstein state-space system and get high learning accuracy.

Parameter Learning for the Hammerstein Output Error System with Time-delay State-space Model

The hybrid signal is used to identify the multi-input single-output (MISO) Hammerstein model. The hybrid signal consists of Gaussian signal and random signal, and the identification process is divided into two stages, namely, the stage of dynamic linear part and the stage of static nonlinear part. Firstly, the correlation analysis method is used to identify the linear part parameters. Then, for the parameters of the nonlinear part and the output noise model, an extended stochastic gradient algorithm with forgetting factor (FF-ESG) is adopted to deal with the issue that the convergence of stochastic gradient algorithm is slow. Theoretical analysis and experiments show that the presented method can identify the MISO Hammerstein model with moving average noise and obtain good identification accuracy.

Identification of MISO Hammerstein Nonlinear Model with Moving Average Noise Based on Hybrid Signal

In this paper, the integrated energy system with the goal of system economy is studied and analyzed and the optimal scheduling strategy of the integrated energy system is proposed under the condition of minimum operation cost. Firstly, considering the characteristics of electric cooling and heat system when different energy sources are co-supplying energy, the system optimization model of gas internal combustion engine, gas turbine and other units is established. Secondly, the improved particle swarm optimization algorithm is used to solve the system. Finally, the scheduling strategy to minimize the operation cost of the integrated energy system is given. The simulation results show that the proposed scheduling strategy and optimization method can effectively reduce the system operation cost and provide an early basis for the future planning of power supply system.

Optimization scheduling strategy of integrated energy system based on improved particle swarm optimization algorithm

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