wangwanglianhe Goto Github PK

2-axis_bldc_camera_gimbal

A two axis camera gimbal stabillizer with the use of BLDC motors.

3-axis-camera-gimbal-stabilizer

A feedback control system for 3-axis camera gimbal stabilizer

500-ai-machine-learning-deep-learning-computer-vision-nlp-projects-with-code

500 AI Machine learning Deep learning Computer vision NLP Projects with code

adrc-visualization

ADRC(Active Disturbance Rejection Control) vs PID Simulation

adrc_quadrotor

ADRC uses an Extended state observer to linearize the Quadrotor's Nonlinear dynamics (similar to Feedback linearization). This makes it capable of eliminating disturbances (robustness).

artificial-intelligence-deep-learning-machine-learning-tutorials

A comprehensive list of Deep Learning / Artificial Intelligence and Machine Learning tutorials - rapidly expanding into areas of AI/Deep Learning / Machine Vision / NLP and industry specific areas such as Climate / Energy, Automotives, Retail, Pharma, Medicine, Healthcare, Policy, Ethics and more.

attitude-control-using-chebyshev-neural-network

Re-Implementation of paper "Active attitude fault-tolerant tracking control of flexible spacecraft via the Chebyshev neural Network"

attitude-estimation-of-quadcopter

Attitude estimation of Quadcopter using Kalman Filter, Extended Kalman Filter and Unscented Kalman Filter

attiude-orbitconunderdisturbances

Attitude & Orbit Control for Spacecraft under disturbances.

awesome-lane-detection

A paper list of lane detection.

benchmarks-attitude-smartphones

Benchmark on Attitude Estimation with Smartphones (datasets & scripts)

computer-vision-assisted-uav-detection-and-tracking

This System track the Flaying object and Detect the object using various Machine learning algoritm like Support vector maching,KNN,Random forest and convoluted Neural network.

coptersim

A high-fidelity simulation model developed in Simulink that compatible with different types of multicopters.

damper-defect-detection-using-machine-learning

dcm-imu

The DCM-IMU algorithm is designed for fusing low-cost triaxial MEMS gyroscope and accelerometer measurements. An extended Kalman filter is used to estimate attitude in direction cosine matrix (DCM) formation and gyroscope biases online.

deep-learning-drizzle

Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!

directional-singularity-escape-avoidance-for-gyroscopes

This code accompanies the paper: Valk, L., Berry, A., and Vallery, H., "Directional Singularity Escape and Avoidance for Single-Gimbal Control Moment Gyroscopes," Journal of Guidance, Control, and Dynamics. DOI: 10.2514/1.G003132

disturbance_observer

In this note, disturbance rejection control (DRC) based on unknown input observation (UIO), and disturbance-observer based control (DOBC) methods are revisited for a class of MIMO systems with mismatch disturbance conditions. In both of these methods, the estimated disturbance is considered to be in the feedback channel. The disturbance term could represent either unknown mismatched signals penetrating the states, or unknown dynamics not captured in the modeling process, or physical parameter variations not accounted for in the mathematical model of the plant. Unlike the high-gain approaches and variable structure methods, a systematic synthesis of the state/disturbance observer-based controller is carried out. For this purpose, first, using a series of singular value decompositions, the linearized plant is transformed into disturbance-free and disturbance-dependent subsystems. Then, functional state reconstruction based on generalized detectability concept is proposed for the disturbance-free part. Then, a DRC based on quadratic stability theorem is employed to guarantee the performance of the closed-loop system. An important contribution offered in this article is the independence of the estimated disturbance from the control input which seem to be missing in the literature for disturbance decoupling problems. In the second method, DOBC is reconsidered with the aim of achieving a high level of robustness against modeling uncertainties and matched/mismatched disturbances, while at the same time retaining performance. Accordingly, unlike the first method, DRC, full information state observation is developed independent of the disturbance estimation. An advantage of such a combination is that disturbance estimation does not involve output derivatives. Finally, the case of systems with matched disturbances is presented as a corollary of the main results.

do-dat.github.io

drl_uav

Deep reinforcement learning for UAV in Gazebo simulation environment

dynamicsf17-gimbal

ENGR2340 : Dynamics FA2017 Final Project : 2-Axis Gimbal

facialexpressionrecognition

人脸识别之表情识别项目相关源码

fashionstar-gimbal-2dof-stm32f103-openmv

STM32 OpenMV 云台

fast-and-robust-uav-to-uav-detection-and-tracking

Source code for an Computer Vision and Deep Learning based algorihtm to detect and tracking UAVs from camera mounted on a flying UAV.

fdd-in-uav-using-deep-learning

We propose a Fault Detection and Diagnosis (FDD) model using a Deep Neural Network based architecture to detect the UAV maloperation

fdir_uav

Fault detection and recovery project for the UAV (Matlab based)

fmt-model

Firmament Autopilot Model Framework

fqrouter

anti-GFW router

gimbal

Camera's gimbal stabilizer for BeagleBone using PWM signals

gimbal-1