• Flight stability control problems are fundamental controls that focus on maintaining the stability of a quadcopter by preventing the aircraft from pitching or rotating along its axis. In this context, the goal is to keep the rotation angles—specifically, the roll, pitch, and yaw angles—at zero or at predetermined values, ensuring that the quadcopter remains level and oriented in the desired direction. This type of control is important for stabilizing the system in flight and serves as a foundation for more complex control tasks. This control problem is particularly relevant in situations requiring manual control of a quadcopter, where the operator directs the vehicle to move in specific directions while maintaining the stability of the aircraft. For example, when hovering in a fixed position, flight stability control ensures that the quadcopter does not drift or pitch due to external disturbances such as wind.
Likewise, during forward, backward or sideways movement, it keeps the vehicle aligned and stable, allowing for precise trajectory tracking.
• Position control is an important control problem in which the objective is to guide a quadrotor to a specific target position in three-dimensional space. Unlike other control tasks that prioritize speed or trajectory optimization, position control focuses solely on ensuring that the quadrotor reaches the specified position
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without regard to the time taken or the path. This approach is particularly suitable for tasks where accuracy in final positioning is more important than efficiency in movement. A prominent application of position control is in flight missions that involve the transport of cargo, supplies, or equipment from one location to another, especially in open or unstructured environments. By continuously monitoring the current position and comparing it to the desired target, the quadrotor adjusts its motor speed and direction to move gradually toward the target. The control strategy must also account for external disturbances such as wind or load changes to maintain stability and accuracy throughout the flight.
Position control serves as a foundational capability for many advanced applications, enabling quadrotors to perform autonomous tasks with high precision and reliability in a wide range of operating scenarios.
Figure 3.1. Illustration of position control problem
• The path-following problem is a specialized control problem in which the objective is to ensure that a quadcopter moves along a predetermined trajectory or path in three-dimensional space. Unlike position control, which focuses on reaching a specific target location, path-following control maintains the coordinates of the quadcopter according to a mathematical equation or geometric description of a given path throughout the flight. This ensures that the quadcopter stays on the desired path, regardless of how long it takes to complete the journey. Path-following control is especially valuable in applications that require precise navigation through complex or obstacle- filled environments. For example, in cargo delivery missions in urban areas
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where buildings and structures form narrow corridors, quadcopters can be programmed to follow a predetermined safe path. This type of control problem highlights the importance of precision, adaptability, and safety in quadrotor navigation. By allowing the vehicle to reliably follow designated paths, it supports a wide range of applications, from automated delivery in crowded areas to systematic exploration of unknown terrain. Path following is therefore the foundation of autonomous drone operations, contributing to the efficiency and success of missions in a variety of environments.
Figure 3.2. Illustration of the path control problem
• The trajectory tracking control problem is a type of complex control challenge where the goal is to ensure that a quadrotor follows a predetermined trajectory in both space and time. Unlike trajectory tracking control, which focuses only on spatial compliance to a given trajectory, trajectory tracking requires the quadrotor to match the time component of its motion to the time component of the trajectory. In essence, the quadrotor must synchronize its position with a moving reference point along the trajectory, ensure that its coordinates converge to the desired reference point q(t) → 𝑞𝑟𝑒∫(𝑡) as closely as possible in time. This type of control problem is particularly relevant in dynamic and time-sensitive applications. For example, in target surveillance missions, a quadrotor may need to track a moving object, such as a vehicle or person, while maintaining a specific distance or angle for effective surveillance. The control algorithm must continuously compare the current state of the quadrotor with the desired trajectory and calculate the necessary adjustments to motor speed and thrust to minimize errors. In addition, the system must account for external disturbances,
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such as wind or changing payload dynamics, to maintain accurate tracking performance. Advanced control techniques, such as proportional–integral controllers (PIDs), model predictive control (MPCs), or neural network-based adaptive controllers, are often used to address the complexity of trajectory tracking. These approaches ensure that the quadrotor not only follows a spatial path but also maintains accurate timing, enabling highly accurate and dynamic
missions in many real-world scenarios.