# Fuzzy Model Predictive Control for Unmanned Helicopter
### Methods of Designing Control Systems for Unmanned Rotorcrafts
To improve learning performance and safety, frameworks like TAG incorporate teacher-advice mechanisms and Gaussian processes [19], while algorithms such as those presented in [20] aim to ensure almost-sure safe exploration by incorporating state safety estimation mechanisms. These advances demonstrate the growing viability of RL-based methods in automatic flight control, particularly for highly dynamic and uncertain environments where conventional controllers face limitations. Apart from the presented approaches, an automatic flight control system for an unmanned helicopter can rely on Model Predictive Control, which is an advanced method of controlling dynamical systems. In its early applications, Model Predictive Control was widely utilized in controlling refinery processes, but nowadays, it has applications in industries such as metallurgy, aviation, robotics, nuclear engineering, and many more. There are many applications of Model Predictive Control in unmanned rotorcraft systems. This technique strongly depends on the quality of the mathematical representation of the plant's dynamics, but on the other hand, it has many formulations and provides the designer with a wide variety of weighting factors that shape the response of the designed system. Reference [21] serves as an example of utilizing the Model Predictive Control framework for designing an automatic flight control system for an unmanned rotorcraft. Itdiscusses the usage of Laguerre functions to define control signals produced by the regulator and compares the usage of unconstrained optimization in the algorithm with constrained optimization. A possible enhancement of the standard MPC algorithm, known in the literature as robust MPC, is presented in reference [22], where the optimization task performed by the controller is defined as the minimization of the worst-case deviation along the predicted trajectory. The paper describes the synthesis of an automatic flight control system for an unmanned rotorcraft using robust MPC and experimentally evaluates its performance. Since Model Predictive Control is defined as an optimization task, it is possible to formulate it using a nonlinear mathematical model of the controlled plant. An approach known as Nonlinear MPC produces a control signal in the process of solving a nonlinear and non-convex optimization problem instead of a quadratic programming task, as in the conventional variant of this method. Reference [23] shows an application of this approach to controlling an unmanned rotorcraft, where the optimization problem in the MPC framework is solved using the Proximal Averaged Newton for Optimal Control algorithm, presented in [24]. Precise control of unmanned helicopters enables their effective operation in complex and dynamic environments, where integration with other networks such as energy and transportation is essential to ensure system reliability and resilience [25]. Moreover, precise control allows unmanned systems to operate in swarms, where advanced multi-agent cooperation methods, such as asymmetric Nash games, enable efficient task and resource allocation under varying environmental conditions [26]. As a result, these approaches enhance the adaptability and scalability of unmanned helicopter control systems. The research presented in this paper intends to show that Model Predictive Control, enhanced with Takagi-Sugeno inference, can be an efficient method for controlling an unmanned helicopter in changing environmental conditions, such as varying forward-flight velocities. In the aerospace industry, most commercial automatic flight control systems rely on PID controllers, which require designing a complex system architecture. The utilization of Model Predictive Control with Takagi-Sugeno inference provides a centralized regulator that can ensure sufficient control without the need for a complicated system design. The novelty of this research lies in the development of an automatic flight control system for a small unmanned rotorcraft that employs a Parallel Distributed Compensation approach.