Modelling and control of the Qball X4 quadrotor system based on pid and fuzzy logic structure

Multirotors have gained a high level of popularity during the last decade both in civilian, military and engineering applications because of the recent advances in sensing, communication, computing and control technologies. Quadcopters, one of the multirotors, are small aerial vehicles propelled by four rotors. This thesis focuses on a quadrocopter model, which is Qball X4. This quadrocopter model was developed by Quanser. In this work both linear and nonlinear models are described for use in to develop a controller. Axes of the Qball-X4 are denoted by x,y,z and these are defined with respect to the vehicle which is shown in Figure 1. Roll, pitch and yaw are defined as the angles of rotation about the x, y and z axes. First, the actuator dynamics, then respectively roll/pitch model, height model, x-y position model and yaw model are described. After the description of the models, a controller design method has been proposed. First, conventional PID control technique is presented. This technique has already been applied by the Quanser. The control gains for the PID are found using the LQR method. PID controller has been applied to both nonlinear and linear models of the Qball X4. Simulation results are shown for the position controls along x,y,z axis and roll, pitch yaw angles. Second, as an extension of the conventional PID control theory, a different fuzzy controller structure is applied. The proposed fuzzy controller structure is based on fuzzy logic. Fuzzy logic is a logic, in which the truth variables can take any real number between 0 and 1. It is different than the boolean logic, because in boolean structure, the truth variables can be in the only 0 or 1. Fuzzy logic has been extended to handle the concept of partial truth, so that the truth value can take range between completely true and comletely false. The name of the control structure is PID type fuzzy controller. Classical fuzzy PID controller requires three inputs and its rule base has three dimensions. On the other hand, the fuzzy type PID controller has just two inputs and its rule base has two dimensions. A PID type fuzzy controller structure includes both PD and PI type fuzzy controllers. Again PID type fuzzy controller has been applied to both nonlinear and linear model of the Qball X4. Simulation results are shown for the position controls along x,y,z axis and roll, pitch, yaw angles. Last, a different method which tunes the scaling factors of the PID type fuzzy controller is proposed. In this method, we cannot change the fuzzy rules and scaling factors, we can only set the membership function to improve the steady state response of the PID type fuzzy controller. Again, PID type fuzzy controller with self-scaling factors has been applied to both nonlinear and linear model of the Qball X4. Simulation results are shown for the position controls along x,y,z axis and roll, pitch, yaw angles, so that we can easily see the difference between the steady state response of the systems. As a result, in the simulation we can analyze six different cases.(3 cases belong to nonlinear, 3 cases belong to linear) These are both linear and nonlinear PID controller, both linear and nonlinear PID type fuzzy controllers and both linear and nonlinear PID type fuzzy controllers with self-scaling factors. The results are discussed in the last section.