# Adaptive Recursive Sliding Mode Control (ARSMC)-Based UAV Control for Future Smart Cities
## 1 Introduction  
Unmanned aerial vehicle (UAV) path learning for optimizing age and power consumption in Internet of Things (IoT) applications with UAV battery recharge is an important task for future smart cities [1]. It is important to optimize UAVs' flight paths to minimize their age and power consumption while ensuring full data coverage. There are challenges and opportunities of using UAVs for environmental monitoring, such as the ability to collect data over remote or inaccessible areas. The effectiveness of UAV systems is demonstrated through several case studies in [2]. Helicopters (UAVs) are lifted and propelled by one or more rotors, making them suitable for operation in congested or remote areas where fixed-wing aircraft cannot operate. The key advantage of a helicopter is that its lift and direction can be controlled by adjusting the speed of its rotors through motors. As a result, helicopters are often used in areas where takeoff or landing is difficult. The importance of real-time applications for air vehicles has increased over time [3, 4, 5]. In order for a closed-loop system to exhibit the desired response, the controlling variables must be manipulated accordingly. To study this phenomenon, an experimental setup of a twin-rotor MIMOsystem (TRMS), a type of UAV with significant coupling effects, is developed and modeled in [6]. The setup for the UAV is a highly nonlinear system with limited degrees of freedom. It consists of two basic parts: a mechanical part and an electrical part. The objective involves addressing the challenges posed by the controller task, which include: (i) dealing with the dynamic changes of the UAV; (ii) handling the changing dynamics of the system, including mismatched disturbances and varying parametric dynamics; and (iii) managing the absence of the time-varying parameter variations states of the system. A comprehensive review of the literature will help enable us to understand the trajectory for robust optimization. A linear controller with all of its properties was utilized to regulate the TRMS and its coupling effect, whereby the main rotor, through its pitch angle, governs movement along the vertical axis, while the tail rotor, through its yaw angle, governs movement along the horizontal axis [7; 8; 9]. The use of PID control in different scenarios has been explored in several studies. In [10], PID control is discussed as a fractional controller based on the degree of freedom, while [11] explains its application for UAVs with a degree of freedom. In [12; 13], estimation control with nonlinear control is used for highly nonlinear systems. Additionally, [14] presents a decoupled system optimized via swarm optimization based on the PID control strategy. The efficiency of classical control is confirmed in [15; 16] through its effective output response of the TRMS. Hybrid control as classical and nonlinear control is presented in [17; 18]. The authors of [19] analyze the performance of a twin-rotor MIMO system using a linear quadratic regulator (LQR) and LQG control techniques and discuss the differences in the response of pitch and yaw angle control inputs. In [20], a comparative analysis is presented of different control techniques for a twin-rotor MIMO system and the reasons why pitch angle control input may show more variations compared to yaw angle control input are discussed. The authors of [21] undertook a comparative study of PID and fuzzy logic controllers for a twin-rotor MIMO system and considered the differences in the response of pitch and yaw angle control inputs. To eliminate the coupling effect, a decoupling method was employed, either through static decoupling or dynamic decoupling. Furthermore, a classical control strategy, specifically optimal control, was implemented to gain insights into the behavior of the system [22; 23]. An adaptive model for predictive control based on a tube was proposed in [24].