UAV flight control is one of the main problems UAV research. During the flight, there will be various disturbances, such as noise and drift of the sensor, strong winds and chaotic airflow, changes in load capacity and model changes caused by excessive dip angles. These will seriously affect the flight quality of the aircraft, so the control technology of the drone is particularly important. Traditional control methods focus on attitude and altitude control, in addition to controlling speed, position, heading, and 3D trajectory tracking control. The control method of the multi-rotor UAV can be summarized into the following three main aspects.
1 Linear flight control method
Conventional aircraft control methods and early attempts to control aircraft are based on linear flight control theory, such as PID, H∞, LQR, and gain scheduling.
1. PID PID control belongs to the traditional control method and is one of the most successful and widely used control methods. The control method is simple, no need for pre-modeling work, the physical meaning of the parameters is clear, and it is suitable for the control with low flight precision requirements.
2. H∞ H∞ is a robust control method. The classical control theory does not require an accurate mathematical model of the controlled object to solve the problem of multi-input and multi-output nonlinear systems. Modern control theory can solve the problem of multi-input and multi-output nonlinear systems quantitatively, but it completely depends on the mathematical model describing the dynamic characteristics of the controlled object. Robust control can solve the modeling error caused by interference and other factors, but its computational complexity is very large, relying on high-performance processors. At the same time, it is relatively difficult to adjust parameters due to the frequency domain design method.
3. LQR LQR is one of the more successful methods used to control drones. It is a linear system that can be represented by a state space expression. The objective function is the integral of a quadratic function of a state variable or a control variable. Moreover, the use of Matlab software provides a good simulation condition for the LQR control method, which provides convenience for engineering implementation.
4. Gain scheduling Gain scheduling means that when the system is running, the change of the scheduling variables causes the parameters of the controller to change. According to the scheduling variables, the system is operated in different regions with different control rules to solve the system. Nonlinear problem. The algorithm consists of two major parts. The first part mainly completes event-driven and realizes parameter adjustment. If the operating conditions of the system change, the modality can be identified and switched through this part; the second part is error driven, and its control function is implemented by the selected modality. The control method has excellent performance in the control of vertical takeoff and landing, fixed point hovering and path tracking of the rotorcraft.
2 Learning-based flight control method
The learning-based flight control method is characterized by the need to understand the dynamics of the aircraft, as long as some flight tests and flight data. Among the most popular research are fuzzy control methods, human learning based methods, and neural network methods.
1. Fuzzy logic (Fuzzy logic) Fuzzy control is one of the methods to solve the uncertainty of the model. The control of the UAV is realized when the model is unknown.
2. Human-ba sed learning MIT researchers in the United States collect data from aircraft participating in military exercises for aerobatics in order to find a control method that can better control small unmanned aerial vehicles. The operation of the aircraft in different situations to better understand the input sequence and feedback mechanism of the drone. This method has been applied to the autonomous flight of small drones.
3. Neural networks The classical PID control structure is simple, easy to use and easy to implement. However, when the controlled object has complex nonlinear characteristics and it is difficult to establish an accurate mathematical model, it is often difficult to achieve satisfactory control effects. The neural network adaptive control technology can effectively realize the control of a variety of uncertain and difficult to describe nonlinear complex processes, improve the robustness and fault tolerance of the control system, and the control parameters have adaptive and self-learning capabilities .
3 Model-based nonlinear control method
In order to overcome the limitations of some linear control methods, some non-linear control methods have been proposed and applied to the control of the aircraft. These nonlinear control methods can usually be classified as model-based nonlinear control methods. Among them are feedback linearization, model predictive control, multi-saturation control, backstepping, and adaptive control.
1. Feedback linearization Feedback linearization is a commonly used method for nonlinear systems. It uses the methods of mathematical transformation and the knowledge of differential geometry. First, the state and control variables are transformed into a linear form. Then, the design is performed by the conventional linear design method. Finally, the design result is transformed into the original by inverse transformation. State and form of control. There are two important branches of feedback linearization theory: differential geometry and dynamic inverse. The dynamic inverse method has simple estimation characteristics compared with differential geometry, so it is more suitable for the design of flight control systems . However, the dynamic inverse method requires a fairly accurate model of the aircraft, which is very difficult in practice. In addition, due to system modeling errors, plus various external interferences, the design should focus on the robustness factor. The dynamic inverse method has certain engineering application prospects and has become a hot topic in the field of flight control research.
2. Model predictive control Model predictive control is a special type of control method. It obtains the current control action by solving the optimal control problem of a finite time domain open loop at each sampling instant. The initial state of the optimal control problem is the current state of the process, and the solved optimal control sequence is only applied to the first control action, which is the biggest difference between it and those algorithms that pre-calculate the control law. In essence, model predictive control is the problem of solving an open-loop optimal control. It is independent of the specific model, but the implementation is related to the model.
3. Nested saturation is a very common physical phenomenon that exists in a large number of engineering problems. Designing a multi-rotor UAV using a multi-saturation control method can solve many practical problems that cannot be solved by other control methods. Especially for micro-mini drones, the actuators frequently become saturated due to the action of large dip angles and external disturbances. Actuator saturation limits the range of operation and impairs the stability of the control system. Many methods have been used to solve the problem of saturated input, but have not achieved the desired results. Multi-saturation control has good global stability in controlling saturation input, so this method is often used to control the stability of micro-UAVs.
Backstepping Backstepping is one of the most commonly used methods for nonlinear system controller design. It is more suitable for online control and can reduce the time for online calculation. The basic idea of ​​the Backstepping-based controller design method is to decompose the complex system into multiple subsystems that do not exceed the system order, and then design part of the Lyapunov function and the intermediate virtual control amount for each subsystem through reverse recursion. Until the design completes the entire controller. The design of the back-step method applied to the flight control system controller can deal with the effects of a class of nonlinear and uncertain factors, and has been proved to have better stability and error convergence.
5. Adaptive control adaptive control is also a mathematical model-based control method. Its biggest feature is that there is less information dependence on the internal model and external disturbance of the system. The information related to the model is in the running system. The process is continuously acquired, and the model is gradually improved. As the model continues to improve, the control effect obtained by the model will also improve, so the control system has a certain adaptability. At the same time, however, adaptive control is more complex and costly than conventional feedback control, so adaptive methods are only considered when conventional feedback is not used to achieve the desired performance.
Reference books:
Kenzo NONAMI, Wei WANG, et al. Auto nomous Flying Robots: Unmanned Aerial Vehicles and Micro Aerial Vehicles [M]. Berlin: Springer, 2010.
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