Kontrol Tracking Pada Quadrotor Menggunakan Nonlinear Quadratic Tracking Dengan Extended Kalman Filter

Quadrotor merupakan salah satu jenis Unmanned Aerial Vehicle (UAV) sebagai sistem MIMO dan bersifat nonlinear. Mekanisme gerak rotasi dan gerak translasi pada quadrotor memiliki sifat nonlinear yang tinggi dan input kontrol yang saling berinteraksi satu sama lain. Permasalahan interaksi antar input kontrol menyebabkan sistem tidak stabil. Karakteristik ini menyebabkan quadrotor mengalami kesulitan dalam melakukan tracking secara otomatis. Nonlinear Quadratic Tracking (NLQT) adalah metode kontrol nonlinear yang merupakan pengembangan dari metode kontrol Linear Quadratic Tracking (LQT). NQLT ini digunakan untuk mengatasi masalah tracking pada quadrotor dengan tetap mempertahankan sifat linear pada matrik B. Sedangkan Extended Kalman Filter (EKF) digunakan sebagai state estimator yang digunakan untuk mengatasi noise pengukuran. Berdasarkan hasil pengujian sebelum penambahan estimator EKF, metode NQLT menunjukkan performa yang baik dari quadrotor dalam melakukan tracking. Secara quantitatif, quadrotor dapat melakukan tracking sinyal referensi dengan error posisi untuk sumbu-x sebesar 0.0099m dan sumbu-y sebesar 0.0095m dengan noise pengukuran dengan mean nol dan varian 0.009. Setelah penambahan EKF pada sistem kontrol, dengan noise pengukuran yang sama sistem menghasilkan error posisi untuk masing-masing sumbu-x dan sumbu-y adalah 0.0062m ================================================================= Quadrotor is one of the Unmanned Aerial Vehicle (UAV) which is a MIMO system and has a non-linear dynamics. The nonlinearity properties of rotational motion and translational motion of quadrotor are very high and the control inputs interact each other. The interaction between the control inputs lead to system instability. This characteristic causes difficulties in tracking quadrotor automatically. Quadratic Nonlinear Tracking (NLQT) is used to overcome the problem of tracking in quadrotor with maintaining the linear nature of the matrix B. NQLT is developed from Linear Quadratic control method Tracking (LQT). The Extended Kalman Filter (EKF) is used as a state estimator to overcome the noise measurement. Based on the test results before the addition of the EKF, the proposed method provides the excellent performance of quadrotor in tracking. Quantitatively, the quadrotor can track the given reference signal with the position errors of quadrotor are 0.009 on the x-axis and 0.0099m on the y-axis 0.0095m for the measurement noise with zero mean and variance of 0.009. The addition of the EKF on the control system, and by using the same noise properties, the position error along the x-axis and y-axis respectively are 0.0062m and 0.0062

Outdoor altitude stabilization of QuadRotor based on type-2 fuzzy and fuzzy PID

This paper presents a design of altitude stabilization of QuadRotor based on type-2 fuzzy and fuzzy PID. This practical design is implemented outdoor. Barometric and sonar sensor were used in this experiment as an input for the controller YoHe. The throttle signal as a control input was provided by the controller to leveling QuadRotor in particular altitude and known well as altitude stabilization. The parameter of type-2 fuzzy and fuzzy PID was tuned in several heights to get the best control parameter for any height. Type-2 fuzzy produced better result than fuzzy PID but had a slow response in the beginning

Real Time Optimal Tuning of Quadcopter Attitude Controller Using Particle Swarm Optimization

A real-time novel algorithm for proportional, integral and derivative (PID) controller tuning for quadcopters is introduced. The particle swarm optimization (PSO) method is utilized to search the quadcopter solution space to find the best PID controller parameters. A fuzzy logic (FL) controller is used to provide proper velocity reference signals to serve as tracking set points to be achieved by the PID controller. This nested loop design is proposed for stabilizing the quadcopter, where the fuzzy logic controller (FL) is used in the stable loop (i.e. outer loop) to control the desired angle, while the PID controller is used for the rate loop (i.e. inner loop). Finally, the optimum generated PID parameters were achieved in real time using the PSO search algorithm. The generated parameters were tested successfully using an experimental quadcopter setup at the University of Jordan

PEMODELAN DAN ANALISA YAW-LOCK INPUT MIXER TERHADAP FREE-RUDDER HOVERING MULTICOPTER

The main purpose of this research is to compare the response of a modeling and quadcopter implementation when it’s in  normal position and given a yaw-lock mixer input with some PID control parameters. Yaw is one of the movable axis parallel to the axis of the earth magnet. The yaw interference comes from the rotor imbalance as the main inertia generator, thus causing a drift effect on yaw. The yaw-lock mixer input system is installed to keep the yaw in one quadcopter heading position as well as the setpoint tracking. One of the advantages of keeping yaw stability is when mounted dolphin 2 fpv camera axis (roll-pitch), with free-rudder image control generated will consist stable. The method used in this research are; the quadcopter is dynamically modeled so that hypotheses can be drawn about the quadcopter response. After obtained the quadcopter response formulation, then do the sampling of magnetometer data. Based on the formulation and the sampling data, a decision was made regarding the control technique that became the yaw-lock mixer input to the quadcopter board. The graphic result of reading control response then analyzed the efficiency and the level of "disturbance cancellation"

Fuzzy logic based pid control of quadcopter altitude and attitude stabilization

This paper presents the development and implementation fuzzy logic based PID control algorithm for a quadcopter system. The quadcopter consists four motors with four propellers placed on the ends. The rotors are directed upwards and they are placed in a square formation with equal distance from the center of mass of the quadcopter. Four different scenarios are presented: altitude movement, pitch, roll and yaw angle. For the all cases 6-DOF model is derived and used. The quadcopter can be perceived as a challenging control problem due to its high nonlinearity, even with four motors it is underactuated and cannot move translative without rotating about one of its axes. The main objective of the controller is to propose a suitable solution for the problem associated with the control of quadcopter. A fuzzy controller was designed according to the process characteristics. The simulation results were carried out in MATLAB/SIMULINK. The corresponding figures and simulation results are presented. The performance of suggested fuzzy controllers is discussed and analysed. Comparing the performance of the proportional and derivative (PD) controller tuned by Zeiger-Nichols method and proportional, integral and derivative (PID) tuned by partial swarm optimization (PSO) results depict that fuzzy logic based PID controller give a better performance in terms of transient responses, steady state responses and overshoot error

Identifikasi Sistem Plant Kontrol Ketinggian Quadcopter Dengan Metode RLS

Untuk mendapatkan model matematis suatu sistem adalah dengan menggunakan proses identifikasi. Dalam hal ini metode yang digunakan adalah menggunakan metode RLS (Recursive Least Square).  Sistem kontrol quadcopter digunakan dalam identifikasi sistem RLS tersebut. Sinyal uji untuk identifikasi sistem berasal dari PRBS (Pseudo Random Binary Sequence)  yang dibangkitkan dari mikrokontroler ATMega328P. Sedangkan plant yang dikontrol adalah quadcopter yang memiliki board IMU (Inertial Measurement Unit) dengan tipe KK Board 2.1. Hasil identifikasi sistem ini adalah berupa fungsi alih diskrit dengan validasi berupa whiteness test dan uncorrelation test. Parameter fungsi alih diskrit yang didapatkan pada identifikasi sistem ini yaitu denominator A1 = -1.2595, A2 = 0.4422, numerator B1 = 0.0134, B2 = -0.0098, pada time sampling 0.5s. Sedangkan hasil validasi sistem menggunakan whiteness test yaitu RN(0) = 0.1338 , RN(1) = 0.1247, RN(2) = 0.0122, RN(3) = 0.0495 dan  |RN(i)|≤ 0.13563 dimana batas validasi praktikalnya adalah sebesar |RN(i)|≤ 0.15

Pengendalian Kestabilan Ketinggian pada Penerbangan Quadrotor dengan Metode PID Fuzzy

 Quadrotor is a kind of unmanned aerial vehicle that have the ability to take of vertically and maintaining its position while flying mid-air. Flying a quadrotor sometimes needs a stable altitude to perform a specific mission. A stable altitude will make easier for pilot to control the movement of the quadrotor to certain direction.This study designed and implemented a system that can stabilises the altitude of a quadrotor by using Fuzzy-PID method. Altitude control system needed to help pilot controls the altitude stability without adjusting the throttle. Control with PID method is a common control system to be implemented on a quadrotor. This control system has a constant that can be tuned with fuzzy logic with linguistic approach to improve the response time when compensating an error.  The result of this study shows that Fuzzy PID control method generate a better response time compared with the PID-only method. The implementation of PID control generate an altitude stabilisation with a mean value steady state error of ±1,86 cm, whereas the PID Fuzzy generate a mean value of steady state error of ±1,22 cm

Reinforcement Learning for UAV Attitude Control

Autopilot systems are typically composed of an "inner loop" providing stability and control, while an "outer loop" is responsible for mission-level objectives, e.g. way-point navigation. Autopilot systems for UAVs are predominately implemented using Proportional, Integral Derivative (PID) control systems, which have demonstrated exceptional performance in stable environments. However more sophisticated control is required to operate in unpredictable, and harsh environments. Intelligent flight control systems is an active area of research addressing limitations of PID control most recently through the use of reinforcement learning (RL) which has had success in other applications such as robotics. However previous work has focused primarily on using RL at the mission-level controller. In this work, we investigate the performance and accuracy of the inner control loop providing attitude control when using intelligent flight control systems trained with the state-of-the-art RL algorithms, Deep Deterministic Gradient Policy (DDGP), Trust Region Policy Optimization (TRPO) and Proximal Policy Optimization (PPO). To investigate these unknowns we first developed an open-source high-fidelity simulation environment to train a flight controller attitude control of a quadrotor through RL. We then use our environment to compare their performance to that of a PID controller to identify if using RL is appropriate in high-precision, time-critical flight control.Comment: 13 pages, 9 figure

Optimized Neural Networks-PID Controller with Wind Rejection Strategy for a Quad-Rotor

In this paper a full approach of modeling and intelligent control of a four rotor unmanned air vehicle (UAV) known as quad-rotor aircraft is presented. In fact, a PID on-line optimized Neural Networks Approach (PID-NN) is developed to be applied to angular trajectories control of a quad-rotor. Whereas, PID classical controllers are dedicated for the positions, altitude and speed control. The goal of this work is to concept a smart Self-Tuning PID controller, for attitude angles control, based on neural networks able to supervise the quad-rotor for an optimized behavior while tracking a desired trajectory.  Many challenges could arise if the quad-rotor is navigating in hostile environments presenting irregular disturbances in the form of wind modeled and applied to the overall system. The quad-rotor has to quickly perform tasks while ensuring stability and accuracy and must behave rapidly with regards to decision making facing disturbances. This technique offers some advantages over conventional control methods such as PID controller. Simulation results are founded on a comparative study between PID and PID-NN controllers based on wind disturbances. These later are applied with several degrees of strength to test the quad-rotor behavior and stability. These simulation results are satisfactory and have demonstrated the effectiveness of the proposed PD-NN approach. In fact, the proposed controller has relatively smaller errors than the PD controller and has a better capability to reject disturbances. In addition, it has proven to be highly robust and efficient face to turbulences in the form of wind disturbances

Desarrollo de prototipo de quadrotor con sistema de comunicación inalámbrica bidireccional

Este artículo presenta el diseño, implementación, construcción y evaluación de un prototipo quadrotor. Este prototipo cuenta con una amplia variedad de componentes. En el desarrollo, se da a conocer cada uno de ellos, junto con una metodología de trabajo paso a paso del proceso, la cual fue fundamental para la construcción del prototipo.  El sistema de comunicación requerido para llevar a cabo un vuelo eficiente utiliza un equipo emisor comercial, el cual permite controlar el quadrotor, sus variadores de velocidad, los cuales son regulados simultáneamente de acuerdo a la señal de vuelo. Gracias al comportamiento del prototipo en sus pruebas iniciales, fue posible su re-diseño y ajuste, incrementando su estabilidad y desempeño en vuelo. Los resultados finales se detallan en la evaluación de desempeño, describiendo los comportamientos del prototipo apoyados con imágenes