Fixed-time adaptive neural network sliding mode control method for nonlinear systems

Abstract

The invention discloses a fixed-time self-adaptive neural network sliding mode control method of a nonlinear system, comprising the following steps: step S1, establishing a dynamic model of the quadcopter according to the dynamic motion law of the quadcopter; step S2, based on The non-singular fast terminal sliding mode surface proposes a dual power fixed-time control law to achieve high robustness and fast sliding rate; step S3, simulation verification, to verify the proposed fixed-time adaptive neural system for uncertain nonlinear systems Effectiveness of network adaptive control; Step S4, numerical example verification, using Matlab simulation to verify the effectiveness of fixed-time adaptive neural network control. A dual-power fixed-time control is designed based on a non-singular fast terminal sliding surface to achieve high robustness and fast sliding rate, and there is no negative exponential term in the control law to effectively avoid singularity. The algorithm proposed by the invention can greatly improve the robust performance of the system and has high practical value.

Description

technical field [0001] The invention relates to a fixed-time self-adaptive neural network sliding mode control method of a nonlinear system, and belongs to the technical field of intelligent control of unmanned aerial vehicles. Background technique [0002] For decades, quadrotors have received widespread attention from military, civil and engineering scholars due to their inherent characteristics. Small unmanned aerial vehicles (such as quadrotors) have a wide range of applications, such as military reconnaissance in harsh environments, civil logistics, aerial photography, and pesticide spraying. In order to improve the stability and reliability of the aircraft in various situations, scholars have achieved a lot of research results in the research on the intelligent control of quadrotor UAVs. There are many control strategies for the quadrotor, but because the quadrotor system is a complex underactuated system, the traditional PID algorithm and sliding mode control cannot ...

AI Technical Summary

Problems solved by technology

There are many control strategies for quadrotors, but because the quadrotor system is a complex underactuated system, the traditional PID algorithm and sliding mode control cannot meet the actual control requirements

[0004] (1) The method of error generation is unreasonable. The control target v can "jump" in the process, but the change of the output Y of the controlled object has inertia, and it is impossible to jump. It is required to let the slowly changing variable y track to be able to jump. Variable variable v, the initial error is very large, easy to cause overshoot, very unreasonable

[0005] (2) There is no good way to generate the differential signal of the error. Because the differentiator cannot be realized physically, it can only be realized approximately. The commonly used form of the approximate differentiator is

[0006] (3) The introduction of error integral feedback has many negative effects In PID control, the function of error integral feedback is to eliminate static error and improve the accuracy of system response, but at the same time, the introduction of error integral feedback makes the closed loop dull and prone to Oscillation, easy to produce control volume saturation caused by integral saturation

[0007] (4) The linear combination is not necessarily the best combination. The control quantity given by the PID controller is a linear combination of the present, past and future errors.

[0008] Disadvantages of sliding mode control: when the state trajectory reaches the sliding mode surface, it is difficult to slide strictly along the sliding mode towards the equilibrium point, but approaching the equilibrium point by crossing back and forth on both sides, resulting in chattering——sliding mode Control the main obstacles in practical application

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