Model Predictive Control Approach to Autonomous Formation Flight
(2312.01907)Abstract
Formation flight is when multiple objects fly together in a coordination. Various automatic control methods have been used for the autonomous execution of formation flight of aerial vehicles. In this paper, the capacity of the model predictive control (MPC) approach in the autonomous execution of formation flight is examined. The MPC is a controller that capable of performing formation flight, maintaining tracking desired trajectory while avoiding collisions between aerial vehicles, and obstacles faced. Through this approach, aerial vehicle models with six degrees of freedom in a three-dimensional environment are performed formation flight autonomously, mostly in a triangle order. Not only the trajectory for the formation flight can be tracked through the MPC architecture, also the collision avoidance strategies of the aerial vehicles can be performed by this architecture. Simulation studies show that MPC has sufficient capability in both cases. Therefore, it is concluded that this method can deal with constraints, avoid obstacles as well as collisions between aerial vehicles. However, implementation of MPC to aerial vehicles in real time holds challenges.
Overview
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The paper discusses Model Predictive Control (MPC) as a control strategy for autonomous formation flight, emphasizing its capacity to handle complex dynamics and avoid collisions.
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MPC has been applied to aerial vehicles, allowing them to fly in coordinated formations without human intervention, and has succeeded in simulation environments.
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Challenges in autonomous formation flight include ensuring safety and cooperation among Unmanned Aerial Vehicles (UAVs) under disruptive factors and uncertainties.
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MPC's optimization-based approach makes it suitable for managing multivariable systems and constraints, making it effective for controlling UAVs in formation flight.
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There is a need for advancements in computational resources, like using GPUs over CPUs, for real-time implementation of MPC in autonomous aerial vehicles.
Introduction to Model Predictive Control in Autonomous Formation Flight
Model Predictive Control (MPC) is a robust method in the field of automatic control systems, particularly notable in its application to autonomous formation flight of aerial vehicles. It enables vehicles to fly in a coordinated formation, following a predefined trajectory, and ensures that collisions, both between the aerial vehicles and with any potential obstacles, are avoided. This control strategy, based on a systematic approach to predictive and corrective actions, makes it immensely suitable for managing the complex dynamics of formation flight.
Formation Flight and Control Challenges
Formation flight, typically seen in military aviation and air shows, demands precise control and attention. Pilots need to execute these flights with a high degree of skill, which poses both a challenge and a risk. To reduce the pilot workload, engineers have worked towards creating advanced autonomous systems, leading to the development of Unmanned Aerial Vehicles (UAVs) capable of performing tasks without direct human intervention.
However, the autonomous execution of formation flight poses significant challenges. It requires the integration of the vehicles’ dynamics through a common control application while ensuring cooperation and safety among the UAVs. An effective control system must assure coordination, collaboration, and safety, even in the presence of various disruptive factors, failures, and uncertainties.
Model Predictive Control (MPC) Efficacy
MPC, although originally developed for complex chemical processes, has swiftly found its way into controlling multivariable systems including aerial vehicles due to its ability to deal with constraints and predict optimal inputs. It's an optimization-based method, found to be particularly successful in handling constraints in control problems. Several studies have demonstrated the capability of the MPC approach to autonomously execute formation flight, affirming its adequacy in maintaining a predefined trajectory, avoiding collisions, and managing constraints confidently.
Real-time Implementation and Future Directions
Most studies to date have tested MPC for aerial vehicles in simulation environments. One of the core reasons for this has been the high computational demand for real-time implementation, which exceeds the capabilities of many contemporary systems. To overcome this challenge in real-time applications outside of aviation, graphical processing units (GPUs) have been used instead of central processing units (CPUs) due to their higher computing capacity. Given that computational demand increases with the complexity of the vehicle dynamics, there's a need to explore high-capacity processors capable of accommodating the advanced calculations required by MPC for it to be viable in real-time applications.
In conclusion, the use of Model Predictive Control in formation flight presents a substantial potential for autonomy in aerial vehicles. While the technology has proven effective in simulations, the quest towards real-world application continues, calling for advancements in computational resources and techniques that could handle the intricate dynamics and real-time decision-making needed for autonomous formation flight.
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