## 1 Introduction
[PERSON], [PERSON] [30] presented an adaptive non-linear controller based on dynamic inversion and a neural network for trajectory tracking control of the quadrotor in the presence of uncertainties and actuator dynamics. This paper aimed to use a neural network to eliminate the inversion error due to disturbance and the dynamic inversion controller's parameter uncertainties. The performance of the proposed controller was compared with the conventional dynamic inversion controller and PID controller. The finding shows that the PID controller shows poor performance in the presence of uncertainty and disturbance. The dynamic inversion controller performed much better than PID in the presence of disturbance and noise. The neural network-based dynamic inversion controller showed the effectiveness of eliminating the model inversion error and improving its performance. Conventionally, the most common way to tune the controller's parameters is by using trial and error chosen arbitrarily. The controller's parameter needs to be tuned appropriately to achieve the best performance. The problem with this traditional method is that adjusting the controller's parameter is tedious since it can take a long time to find a suitable solution. Furthermore, the control designer can never tell which exact parameters are the optimal solution for the controller [16]. Therefore, to resolve this problem, many researchers have been adopting optimization tools in their work, especially in determining the controller's optimal parameters. With optimization, the optimal values of the controller's parameter can be obtained effortlessly [10]. One of the approaches is to use a nature-inspired optimization algorithm. The advantages of these optimizations are that they are independent problem structures [10]. There is extensive work on a nature-inspired optimization algorithms in the literature. Different algorithms have been used to obtain the optimal parameter of the controller. Among them we can find particle swarm optimization (PSO) [31], genetic algorithm (GA) [32], ant colony optimization (ACO) [33; 34], artificial bee colony (ABC) [35; 36], and cuckoo search (CS) [37; 38]. [PERSON] and [PERSON] [39] design an integral backstepping controller to fully control the quadrotor's position and orientation. The integral action used in this work is mainly to exploit the controller's advantages, which can improve the steady-state error in the presence of disturbance. A PSO was used to obtain the optimal values of the control parameters. In order to realize the performance of the proposed controller, a comparison with the PID controller optimized by the same optimization technique was made. Three simulation cases were conducted: first to move the quadrotor to a specific point in space while stabilizing the quadrotor's orientation angles, second, to track a linear and curve path, and third, to test the robustness against external disturbance. The first simulation results show that the proposed controller can quickly move to the desired point in just 0.26 s while the PID controller is slower. Still, both can stabilize the orientation angles of the quadrotor. They also reveal that the proposed controller improved the settling time by 13.3% and 30.5% in the X and Y positions. However, the PID controller gives a faster settling time by 40% in the Z position. Both controllersproduce no overshoot and steady-state error. The second test concluded that the proposed controller could successfully track linear and non-linear paths while the PID controller has failed. For the third simulation, the proposed controller shows a robust performance when the external disturbance was added since the quadrotor can track the desired position without compromise. [PERSON], [PERSON] [40] conducted a study on an optimization-based PID controller to obtain an optimal gain parameter for the controller. In this paper, the reference model (RM) method and genetic algorithm (GA) was used to optimize the PID controller's gain for altitude and attitude angle control of the quadrotor.