# Fuzzy Model Predictive Control for Unmanned Helicopter
### Methods of Designing Control Systems for Unmanned Rotorcrafts  
Knowing that the aerodynamic phenomena describing helicopter dynamics strongly depend on the conditions in which the machine operates, it is necessary to use this knowledge to synthesize control laws that enable controlling the rotorcraft's state in various distinct flight scenarios. There are many alternative approaches for designing automatic flight control systems for rotorcraft. A literature study regarding this topic is presented below. A reliable method of designing an automatic flight control system for an unmanned rotorcraft is to perform system identification of the plant in hover conditions and design PID regulators to achieve the desired position-tracking capabilities. This approach was precisely described in reference [8] and enabled designers to achieve satisfactory performance in conditions akin to hovering. An alternative approach to synthesizing a control law for an unmanned rotorcraft can rely on \(H_{\infty}\) control loop design, which aims to minimize the impact of system disturbances on the helicopter's performance. This task is mathematically described as the Bounded \(L_{2}\) Gain Design Problem. This method not only guarantees the stability of the designed control system but also provides an efficient solution for controlling Multiple Input-MultipleOutput (MIMO) plants, where dynamics decoupling is not possible. A description of designing a static \(H_{\infty}\) controller for an unmanned rotorcraft is presented in reference [9]. Another possible method for designing an automatic flight control system for an unmanned rotorcraft is based on Linear-Quadratic Regulator (LQR) theory, which aims to provide a static control law for MIMO plants [10]. Based on a linear dynamics model of the helicopter, the LQR regulator provides optimal control for an infinite time horizon [11]. The existence of an analytical solution for the LQR problem and its great efficiency in controlling MIMO dynamics make this method a popular choice in the aerospace industry [12]. An example of using the LQR framework in unmanned helicopter control is described in reference [13]. The previously mentioned methods for designing automatic flight control systems heavily rely on the mathematical representation of helicopter flight dynamics. An alternative approach to synthesizing control laws that do not necessarily rely on a mathematical model of the plant is the utilization of fuzzy inference systems. These systems can be designed based on expert knowledge and provide a suitable framework for designing nonlinear control laws. Reference [14] presents a process for designing a fuzzy inference system to control an unmanned rotorcraft. Another example of using fuzzy systems in unmanned helicopter control is reference [15], which describes the integration of a Mamdani regulator with Takagi-Sugeno inference for controlling the horizontal velocity of an unmanned rotorcraft. In recent years, data-driven methods have gained significant popularity in the synthesis of control laws for autonomous systems. As one of them, reinforcement learning (RL) has emerged as a particularly promising framework for defining control policies in scenarios, where explicit modeling of complex dynamics is difficult or impractical. By interacting with the environment and learning through trial-and-error, RL agents can adapt to a wide range of flight conditions and disturbances. The application of RL to control tasks under strict constraints and safety requirements has led to the development of algorithms such as FHCPL, which enables fixed-horizon constrained policy learning in risk-sensitive industrial scenarios [16]. In the context of autonomous driving, similar principles have been successfully applied to motion planning using fixed-horizon constrained reinforcement learning techniques [17]. For aerial vehicles, hybrid approaches combining RL with trajectory optimization have been shown to significantly enhance path planning efficiency, as described in [18].