Adaptive neural network tracking control method with preset tracking precision

Abstract

The invention belongs to the technical field of switching systems, and discloses an adaptive neural network tracking control method and system with preset tracking precision. In order to approach an unknown nonlinear function and an unknown periodic time-varying parameter, a radial basis function neural network and Fourier series expansion are respectively introduced, and an upper bound of an approximation error is processed for the first time. Two bilateral smooth switching functions are introduced, and a common Lyapunov function meeting all subsystems is constructed. A new adaptive neural network control scheme is constructed by using a backstepping method and a common Lyapunov function theory. According to the method, the tracking error is converged to the neighborhood of the preset zero point, the preset performance of the tracking error is ensured, and all signals of a closed-loop system are semi-globally consistent and finally bounded. According to the method, the actual problem is combined, the model is established, the model obtaining result is solved, and a new thought and a solution method are provided for cross research of mathematics and engineering problems.

Description

technical field [0001] The invention belongs to the technical field of switching systems, and in particular relates to a preset tracking accuracy adaptive neural network tracking control method and system. Background technique [0002] At present: the switching system is an extremely important type of hybrid system, which consists of a group of continuous or discrete dynamic subsystems and a set of switching rules (switching laws or switching signals) that determine how to switch between subsystems. The entire switching system Switch between subsystems according to this set of switching rules. The switching system organically combines the discrete event dynamic system with the continuous variable dynamic system, which can effectively describe the parameters or structural mutations, adopts a multi-controller switching mechanism for a single object, has large uncertainties in the model, and is difficult to identify online. Certain systems that cannot be stabilized with smooth...

AI Technical Summary

Problems solved by technology

The existing technology has the following defects: 1) For the uncertain switching nonlinear system, the system function is unknown and subject to periodic disturbances, and how to design the corresponding approximation algorithm needs further research; 2) The approximation performance cannot be displayed intuitively

[0005] (1) For the uncertain switching nonlinear system without considering the unknown system function and the periodic disturbance, how to design the corresponding approximation algorithm still needs further research

[0006] (2) It is impossible to display the approximation performance intuitively

In most approximation algorithms, the upper bound of the approximation error is ignored as an unknown constant, and the corresponding approximation performance cannot be obtained through the tracking error

[0007] (3) For uncertain switching nonlinear systems with periodic parameters, it is difficult to improve the approximation algorithm and deal with the approximation error. At the same time, it is more technically difficult to ensure that the tracking error converges to the preset small neighborhood of the origin

Due to the complexity of the geographical location and the environment, in practical applications, these controlled systems are often disturbed, considering the general disturbance situation, periodic disturbances, which lead to the failure of the designed controller and the deterioration of the stability of the system

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