PID Controller Tuning Optimization with Genetic Algorithms for a Quadcopter

This paper is focused on the dynamic of mathematical modeling, stability, nonlinear gain control by using Genetic algorithm, utilizing MATLAB tool of a quadcopter. Previously many researchers have been work on several linear controllers such as LQ method; sliding mode and classical PID are used to stabilize the Linear Model. Quadcopter has a nonlinear dynamics and unstable system. In order to maintain their stability, we use nonlinear gain controllers; classical PID controller provides linear gain controller rather than nonlinear gain controller; here we are using modified PID control to improve stability and accuracy. The stability is the state of being resistant to any change. The task is to maintain the quadcopter stability by improving the performance of a PID controller in term of time domain specification. The goal of PID controller design is to determine a set of gains: Kp, Ki, and Kd, so as to improve the transient response and steady state response of a system as: by reducing the overshoot; by shortening the settling time; by decrease the rise time of the system. Modified PID is the combination of classical PID in addition to Genetic Algorithm. Genetic algorithm consists of three steps: selection, crossover, and mutation. By using Genetic algorithm we correct the behavior of quadcopter

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

Quadcopter stabilization with neural network

UAVs (Unmanned Aerial Vehicle), also known as drones, are becoming attractive in the consumer space due to their relatively low cost and their ability to operate autonomously with minimal human intervention. A user could program the drone with GPS coordinates, and the drone would comply with utmost precision. In order for the drone to operate a preprogrammed flight path, it requires a host of sensors for it to gather data and operate on that data in real time. For instance, a consumer drone typically has obstacle avoidance sensors, a GPS sensor for routing and navigation, and an IMU (Inertial Measurement Unit) for tracking position and orientation. These sensors play a crucial role in both stabilization and navigation of the drone. This report aims to investigate, analyze and understand the complexity involved in designing and implementing an autonomous quadcopter; specifically, the stabilization algorithms. In general, stabilization is achieved using some form of control algorithm. The report covers a popular approach for stabilization (PID Control) found with many open source libraries and contrasts it with an alternative machine learning approach (Neural Networks). Finally, a machine learning based algorithm is implemented and evaluated on a prototype quadcopter, and its results are presented.Electrical and Computer Engineerin