# A Survey: Future Smart Cities Based on Advance Control of Unmanned Aerial Vehicles (UAVs)
## 5 Why Nonlinear Control Strategies
### Contribution of Nonlinear Control Strategies for UAVs in Smart Cities  
Nonlinear control strategies have made significant contributions to the development of UAVs for smart cities. These strategies offer advanced control techniques that can handle the complex dynamics and uncertainties associated with UAVs operating in urban environments. Here is a detailed analysis of the technical and research aspects of nonlinear control methods in the context of UAV development for smart cities:  
1. Enhanced Stability and Performance: Nonlinear control methods provide improved stability and performance compared to traditional linear control approaches. They can handle the highly nonlinear dynamics of UAVs, allowing for precise control and maneuverability in challenging urban environments [107].
2. Robustness to Uncertainties: Smart cities present various uncertainties, such as wind gusts, sensor noise, and unpredictable obstacles. Nonlinear control strategies, such as adaptive control and robust control, can effectively handle these uncertainties, ensuring the stability and safety of UAV operations [108].
3. Trajectory Tracking and Path Planning: Nonlinear control methods enable accurate trajectory tracking and path planning for UAVs in smart cities. By considering the nonlinear dynamics and constraints of the UAV, these strategies can optimize the trajectory to achieve desired objectives, such as efficient package delivery or surveillance missions [109].
4. Obstacle Avoidance and Collision Prevention: Nonlinear control techniques facilitate obstacle avoidance and collision prevention in complex urban environments. By incorporating sensor data and environment mapping, these methods can generate control actions that steer the UAV away from obstacles, ensuring safe navigation [110].
5. Energy Efficiency: Nonlinear control strategies can optimize the energy consumption of UAVs, which is crucial for prolonged flight times and efficient operations in smart cities. By considering the nonlinear dynamics and energy constraints, these methods can minimize energy usage while maintaining desired performance [111].  
Research Aspects of Nonlinear Control Methods for UAVs in Smart Cities Include  
* Model Identification: Accurate modeling of UAV dynamics is essential for designing effective nonlinear control strategies. Research focuses on developing identification techniques to estimate the UAV's nonlinear dynamics from flight data or simulation models [112].
* Adaptive Control: Adaptive control methods aim to adapt the control laws based on the UAV's changing dynamics or uncertainties. Research focuses on developing adaptive algorithms that can handle varying conditions in real-time, ensuring robust and reliable control [113].
* Machine Learning Integration: Machine learning techniques, such as neural networks and reinforcement learning, can be integrated with nonlinear control methods to enhance UAV performance. Research explores the integration of these techniques to improve control accuracy, obstacle detection, and decision-making capabilities [114].
* Human-UAV Interaction: As UAVs become more prevalent in smart cities, research focuses on developing nonlinear control strategies that enable seamless interaction between humans and UAVs. This includes intuitive control interfaces, human-in-the-loop control, and shared autonomy, allowing for safe and efficient collaboration [115].  
Nonlinear control strategies have significantly contributed to the development of UAVs for smart cities. These methods offer enhanced stability, robustness, trajectory tracking, obstacle avoidance, energy efficiency, and more. Ongoing research explores various aspects, including model identification, adaptive control, machine learning integration, and human-UAV interaction, to further advance the capabilities of nonlinear control methods in the context of UAV development for smart cities.