Description of flight control method for multi-rotor UAV

Description of flight control method for multi-rotor UAV

In order to overcome the limitations of some linear control methods, some nonlinear control methods are proposed and applied to the control of aircraft. The following is my explanation of the flight control method of shared multi-rotor UAV. Welcome to read and browse.

1 linear flight control method

Conventional aircraft control methods and early attempts to control aircraft are based on linear flight control theory, including PID, H? LQR and gain scheduling method.

1.PID PID control is a traditional control method, and it is one of the most successful and widely used control methods at present. Its control method is simple, no modeling work is needed, and the physical meaning of parameters is clear, so it is suitable for control with low flight accuracy requirements.

2.h？ h？ Belonging to a robust control method. Classical control theory does not need the precise mathematical model of the controlled object to solve the multi-input and multi-output nonlinear system problems. Modern control theory can quantitatively solve the problem of multi-input multi-output nonlinear system, 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 calculation is very large and depends on high-performance processors. At the same time, because it is a frequency domain design method, parameter adjustment is relatively difficult.

3.LQR LQR is one of the successful methods to control UAV. Its object is a linear system that can be expressed by state space expression, and its objective function is the integral of quadratic function of state variable or control variable. Moreover, the use of Matlab software provides a good simulation condition for the control method of LQR, which is more convenient for engineering implementation.

4. Gain scheduling method Gain scheduling means that when the system is running, the parameters of the controller will change with the change of scheduling variables, and the system operates with different control laws in different regions according to the scheduling variables to solve the problem of system nonlinearity. The algorithm consists of two parts. The first part mainly completes event-driven and realizes parameter adjustment. If the system operation changes, you can identify and switch modes through this part; The second part is error drive, and its control function is realized by the selected mode. This control method has excellent performance in vertical takeoff and landing, fixed-point hovering and path tracking control of rotary-wing UAV.

2 learning-based flight control method

The characteristic of learning-based flight control method is that it does not need to know the dynamic model of the aircraft, but only needs some flight tests and flight data. Among them, the most popular research methods are fuzzy control method, method based on human learning and neural network method.

1. Fuzzy logic fuzzy control is one of the methods to solve the uncertainty of the model, which realizes the control of the UAV when the model is unknown.

2. Based on human learning, in order to find a better control method for small unmanned aerial vehicles, researchers from Massachusetts Institute of Technology collected data of aerobatics aircraft participating in military exercises, and analyzed the pilot's operation on the aircraft in different situations, so as to better understand the input sequence and feedback mechanism of unmanned aerial vehicles. This method has been applied to the autonomous flight of small UAV.

3. The classical neural network PID control structure is simple and easy to use and realize, but when the controlled object has complex nonlinear characteristics and it is difficult to establish an accurate mathematical model, it is often difficult to obtain satisfactory control results. Neural network adaptive control technology can effectively control all kinds 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 ability.

3 Model-based nonlinear control method

In order to overcome the limitations of some linear control methods, some nonlinear control methods are proposed and applied to the control of aircraft. These nonlinear control methods can usually be classified as model-based nonlinear control methods. There are feedback linearization, model predictive control, multi-saturation control, backstepping and adaptive control.

1. feedback linearization feedback linearization is a common method for nonlinear systems. It uses the method of mathematical transformation and the knowledge of differential geometry. First, it transforms state and control variables into linear forms. Then, the conventional linear design method is used for design. Finally, the design results are transformed into the original state and control form by inverse transformation. Feedback linearization theory has two important branches: differential geometry method and dynamic inverse method, among which the dynamic inverse method is more suitable for the design of flight control system because of its simple calculation compared with differential geometry method. However, the dynamic inverse method needs a fairly accurate aircraft model, which is very difficult in practical situations. In addition, due to system modeling errors and various external disturbances, the design should focus on the factors of robustness. Dynamic inverse method has a certain engineering application prospect and has become a hot spot in the field of flight control.

2. Model predictive control Model predictive control is a special control method. It obtains the current control action by solving a finite time-domain open-loop optimal control problem at each sampling time. 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 function, which is the biggest difference from those algorithms that calculate the control law in advance. Model predictive control is essentially to solve an open-loop optimal control problem, which has nothing to do with the specific model, but its realization is related to the model.

3. Nested saturation is a very common physical phenomenon, which exists in a large number of engineering problems. Using multi-saturation control method to design multi-rotor UAV can solve many practical problems that other control methods can not solve. Especially for micro-UAV, the actuator will be saturated frequently due to the large inclination angle and external interference. Actuator saturation will limit the operating range and weaken the stability of the control system. There are many methods to solve the problem of input saturation, but none of them have achieved ideal results. Multi-saturation control has good global stability when controlling saturated input, so this method is often used to control the stability of micro UAV.

4. Backstepping and backstepping control is one of the most commonly used methods for controller design of nonlinear systems, which is more suitable for online control and can reduce the time of online calculation. The basic idea of the controller design method based on backstepping is to decompose the complex system into several subsystems that do not exceed the order of the system, and then design a partial Lyapunov function and an intermediate virtual control quantity for each subsystem through backward recursion until the whole controller is designed. The backstepping method is applied to the design of flight control system controller, which can deal with the influence of a class of nonlinear and uncertain factors and has been proved to have good stability and error convergence.

5. Adaptive control Adaptive control is also a control method based on mathematical model. Its biggest feature is that it relies less on the information of the internal model and external disturbance of the system, and constantly obtains the information related to the model during the operation of the system, so that the model is gradually improved. With the continuous improvement of the model, the control function obtained from the model will also be improved, so the control system has certain adaptability. However, at the same time, the adaptive control is more complicated and expensive than the conventional feedback control, so the adaptive method will be considered only when the conventional feedback cannot achieve the expected performance.

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