A Metaheuristic Optimization Using Explosion Method On A Hybrid Pd2-Lqr Quadcopter Controller

The popularity of the rotorcraft type UAV, the quadrotor, has grown rapidly in recent years due to its advantages and capability to perform various applications such as environment monitoring, surveillance, and inspection. However, the quadrotor’s dynamics are highly nonlinear and underactuated since it has 6 DOF that need to be controlled by only 4 actuators. Besides, it is also crucial that the controller’s gains are tuned appropriately since it can affect the quadrotor’s performance. This study aims to develop an effective optimal control technique to control and stabilize the quadrotor's altitude and attitude motion. A simulation-based experiment in MATLAB/Simulink environment was conducted to test and verify the proposed algorithm and controller performance. The mathematical model of the quadrotor was derived based on the Newton-Euler approach and linearized using a small angle approximation. In this study, a Hybrid PD2-LQR controller was proposed for quadrotor control and stabilization. Conventionally, the controller’s gains were tuned using the trial-anderror method. The problem with this method was that it very time-consuming, and the control designer could never tell which gains are the optimal solution for the controller. Therefore, an optimization algorithm based on the explosion method called REA was proposed and implemented on the proposed Hybrid PD2-LQR control structure. A comparative study with 8 well-known algorithms, PSO, ABC, GA, DE, MVO, MFO, FA, and STOA, was performed to evaluate the performance of the proposed algorithm. Similarly, the proposed controller was evaluated by a comparative study with 6 conventional controllers, PD, PID, LQR, Hybrid P-LQR, Hybrid PD-LQR, and Hybrid PD2-LQR. The findings show that the REA could perform well in exploiting the global optimum and exploring the search space. The convergence speed of the REA was also faster than other algorithms. Similarly, for the controller, the findings show that the REA-based Hybrid PD2-LQR controller has a faster rise time with a shorter settling time than the conventional controllers, while there was no overshoot and steady-state error produced. On average, the rise time, settling time, overshoot, steady-state error and RMSE was improved by 95%, 95.3%, 100%, 100%, and 43.5% respectively for roll and pitch motion, while 96.5%, 96.5%, 100%, 97.2%, and 47.3% respectively for yaw motion. For altitude motion, the rise time, settling time, overshoot, and steadystate error were improved by 84.5%, 85.5%, 100%, and 100%, respectively. The RMSE for altitude motion was not improved but still could be accepted since the difference with the conventional controllers was not too much. Therefore, based on these findings, it could be concluded that the proposed REA-based Hybrid PD2-LQR controller was the best among the tested controller and suited for controlling and stabilizing the quadrotor’s altitude and attitude motion since it could significantly improve the performance of the quadrotor’s response

The Developments Of Proportional-Double Derivative-Linear Quadratic Regulator Controller For Attitude And Altitude Motions Of A Quadcopter

Unmanned Aerial Vehicle (UAV), in this case, a quadcopter, is a small-scale UAV that has been widely used in the recent years due to its capability to perform a various application either in the military or civilian application such as environment monitoring, surveillance, and inspection. In order to guarantee a high performance of the quadcopter in the various mission applications, it needs reliable hardware and control systems. Therefore, it is important to developing an effective control algorithm for the controller for the performance and application of the quadcopter. In this thesis, studies of the attitude control and stabilization of the quadcopter through a simulation in Matlab/Simulink software has been done. First, several controllers, Proportional-Integral-Derivative (PID), Proportional-Derivative (PD), Linear Quadratic Regulator (LQR), Proportional-Linear Quadratic Regulator (P-LQR), and Proportional-Derivative-Linear Quadratic Regulator (PD-LQR) controller have been chosen to be studied and analyzed. After that, from the analysis obtained another controller was proposed to improve the performance of the quadcopter control. It is found that by adding another Derivative gain in the PD-LQR control system, the performance can be improved further. Thus, a Proportional-Double Derivative-Linear Quadratic Regulator (PD2-LQR) controller has been designed and developed. The mathematical model of the quadcopter using the Newton-Euler approach is applied to the controller system illuminate the attitude and altitude motions of the quadcopter. The simulation results of the proposed PD2-LQR controller have been compared with the PD, PID, LQR, P-LQR, PD-LQR controller. The comparative study of the response plots reveals that the proposed PD2-LQR controller significantly improves the performance of the control system in almost all responses. In pitch motion, the PD2-LQR controller can improve the rise time up to 82.9% in average compared to other controllers, settling time improved by 86.58% in average, overshoot improved by 39.16% in average, steady-state error improved by 39.2% in average, and RMSE improved by 28.32% in average. In roll motion, rise time improved by 63% in average, settling time improved by 65.5% in average, overshoot improved by 57.7% in average, steady-state error improved by 32.82% in average, and RMSE improved by 29.4% in average. In yaw motion, rise time improved by 41.8% in average, settling time improved by 41.5% in average, overshoot improved by 34.3% in average, the improvement of steady-state error in yaw motion is very small it can be approximately equal to zero, and RMSE improved by 19.4% in average. In altitude motion, rise time improved by 31.7% in average, settling time improved by 52.7% in average, overshoot improved by 75.7% in average, and RMSE improved by 10.2% in average. Therefore, the proposed PD2-LQR controller is best-suited for the modelled quadcopter in all four motions, pitch, roll, yaw, and altitude