# A Survey: Future Smart Cities Based on Advance Control of Unmanned Aerial Vehicles (UAVs)
## 5 Why Nonlinear Control Strategies
### History of Nonlinear Control
This theory examines controllability and observability in nonlinear control systems using differential geometry, demonstrating tremendous potential in this area. Reviewing the early history of nonlinear control critically reveals that the descriptive ideas of optimality, stability, and uncertainty predominated. The introduction of the \"activation process\", however, during the 1990s resulted in a substantial shift. During this decade, descriptive research underwent a metamorphosis into formal design methodologies through constructive techniques [90]. As a result, attention turned away from merely descriptive methods and towards the creation of systematic, usable methods for creating nonlinear control systems. Table 3, provides a prominent historical advance in nonlinear control theory. In recent years, there have been significant advancements in the research of nonlinear control strategies, leading to a deeper understanding and improved application of these techniques. One noteworthy development is the utilization of modern mathematical tools, such as nonlinear system analysis, to analyze and design control algorithms for complex nonlinear systems. These tools provide a more rigorous framework for studying the stability and performance of nonlinear control systems, enabling researchers to develop control strategies that are robust and reliable [91]. Another important development is the emergence of adaptive control techniques, which allow control systems to adapt and adjust their parameters in real-time based on the changing dynamics of the system. Adaptive control strategies have been particularly valuable in dealing with uncertainties and disturbances in nonlinear systems, enabling improved tracking, stability, and disturbance rejection capabilities. These techniques have found applications in diverse fields, including aerospace, robotics, and process control [92]. Furthermore, the integration of machine learning and artificial intelligence (AI) with nonlinear control has opened up new possibilities for advanced control strategies. Machine learning algorithms, such as neural networks and reinforcement learning, can be used to learn the dynamics of complex nonlinear systems and develop control policies that optimize performance objectives. This combination of nonlinear control and AI has shown promising results in various domains, including autonomous vehicles, robotics, and power systems [48]. Additionally, researchers have made significant progress in the field of optimal control for nonlinear systems. Optimal control strategies aim to find control actions that minimize a specified cost function while satisfying system constraints. Nonlinear optimal control techniques, such as model predictive control (MPC) and nonlinear programming, have been extensively studied and applied to various nonlinear systems. These approaches offer improved control performance and the ability to handle complex nonlinear dynamics and constraints [93]. Moreover, advancements in the area of hybrid control systems have contributed to the development of nonlinear control strategies. Hybrid systems involve the interaction of continuous dynamics with discrete events, leading to challenging control problems. Researchers have developed innovative approaches, including hybrid control frameworks, supervisory control, and event-triggered control, to tackle these complex systems. These strategies have been successfully applied to applications like autonomous systems, power grids, and manufacturing processes [94]. This study examines various nonlinear, adaptive, and intelligent control approaches employed in UAV flight control systems, as provided in Table 5. The performance of each control technique is evaluated based on its capability to handle nonlinearities, coupled dynamics, actuator failures, modeling errors, parametric uncertainties, aerodynamic effects, and unknown perturbations in unstructured environments.