Trajectory tracking of a quadrotor using TID controller Kranthi Kumar Deveerasetty 1,2,3 , Yimin Zhou* 1,2,3 and Bo Han 1,2,3 Abstract—In this paper, a TID controller is applied for the trajectory tracking of a quadrotor. The mathematical model of the control system is derived and compared with the traditional PD (Proportional-Derivative) controller. In order to improve the control precision of the UAV, the controller is designed by selecting the proper tuning parameters. To explore the effectiveness of the proposed controller, dynamic responses of a UAV obtained by using the TID controller and the validation of the results compared with the PD controller. The control performances are analysed by using MATLAB/Simulink model. I. INTRODUCTION Micro UAVs (Unmanned Aerial Vehicles) is getting faster based on the rapid progress of computer technology. Quadro- tors can be operated on a wide area regardless of the effect of ground configuration. The practical application of UAVs gains its popularity because it is easy to operate in the places where they are unsafe and challenging to approach. UAVs can be classified into two types. One is the fixed-wing and the other has the rotary-wings. In this paper, we use the rotary- wing type, which has more advantages compared to the fixed-wing quadrotors in the sense of Vertical Take-off and Landing (VTOL), hovering performance and omnidirectional flying. Rotary wing type UAVs are sorted out as helicopters, co-axial helicopters and quadrotors, etc. Compared to the above rotary-wing types, the quadrotor has a simple dynamic structure and cheaper cost, which is safer in hazardous tasks than piloted aircraft. Quadrotors are available with various shapes and sizes to have been developed for research and commercial purpose. A four-rotor helicopter dynamic model derived and a controller based on dynamic feedback was developed by the Mistler et al. [1]. Dragon flyer theoretical study and its dynamic model developed by the McKerrow [2]. The dynamic feedback con- trol applied to the nonlinear dynamic model of the quadrotor and also wind parameters are estimated by considering the Euler angles [3]. The dynamic model of the mini rotorcraft was achieved using the Lagrange approach using the Lya- punov study. A nonlinear control structure developed based on nested saturation and compared the results with LQR linear controller to shows its performance ability [4]. A new indoor micro VTOL system developed and implemented the mechanical design, dynamic modelling, and sensing and also *This work was not supported by any organization 1 Center for Intelligent Bionic, Shenzhen Institutes of Advanced Technol- ogy, Chinese Academy of Sciences, Shenzhen, China 2 Guangdong Provincial Key Lab of Robotics and Intelligent System, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences 3 Key Laboratory of Human-Machine Intelligent Syner- gic System, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences kranthi@siat.ac.cn; ym.zhou@siat.ac.cn;bo.han@siat.ac.cn developed control techniques for OS4 autonomous robot [5]. Two famous nonlinear controllers such as back-stepping and a sliding mode control are demonstrated and performance verified on the same OS4 autonomous robot [6]. Visual feedback control developed using the camera as a sensor for the estimation of the altitude and the camera-equipped on the X4-flyer [7]. To perform the trajectory tracking tasks, a vision based visual servo controller is developed for the underactuated quadrotor [8]. To achieve the quasi-stationary flight, an image-based strategy used for the visual servo control is proposed. The desired task is achieved by locating the camera to an unmoving target [9]. A new controller is developed based on the nested saturation to obtain the hovering [10]. The VTOL of an ”X4-flyer” achieved by an intuitive control strategy considering the gyroscopic effects with a quasi-stationary flight dynamic modelling is proposed [11]. To obtain the attitude stabilization of a VTOL quadrotor a PD 2 feedback controller was proposed. The PD 2 feedback structure is established by compensation of the gyroscopic torques and Coriolis [12]. Especially, for the outdoor attitude control, a unique control design has been developed and applied on the Stanford Testbed of Autonomous Rotorcraft for Multi-Agent Control (STARMAC), is also known as X4- flyer. Two control design techniques are applied based on the integral sliding mode and reinforcement learning control for adapting nonlinear disturbance [13], [17]. Considering the disturbance and model uncertainties, reduced state observer and disturbance observer are proposed [14]. The quadrotor hovering problem addressed by using nonlinear programming (NLP) method instead of Pontryagin’s Minimum Principle (PMP). The advantage of using NLP method does not need to solve a set of highly nonlinear differential equations [15]. The flying platforms which are controlled at low frequencies have many limitations due to software and hardware. To overcome this problem, a 1 kHz control frequency used in the flying platform with an updated rate of motor configuration is used to improve the reliability and efficiency of the performance [16]. Recently, the position and attitude tracking of the quadrotor was designed by using PID with derivative filter and integral sliding mode controller [20], [21]. A brief review dedicated to the subject of control techniques and also for the obstacle avoidance methods for the UAVs [22], [23]. The rest of the paper is summarised as follows. Modelling of the quadrotor is presented in Section 2. In section 3, the TID controller is discussed. Results and discussion are presented in Section 4 and the conclusion is discussed in Section 5. 2019 Australian & New Zealand Control Conference (ANZCC) Auckland, New Zealand. November 27-29, 2019 978-1-7281-1786-7/19/$31.00 ©2019 IEEE 139