Inverted pendulum self-adaptive iterative learning inversion control method

Abstract

An inverted pendulum self-adaptive iterative learning inversion control method is characterized in that: aiming at an inverted pendulum system containing unknown input saturation, a self-adaptive iterative learning inversion controller is designed through utilization of a neural network and an inversion control method in combination with self-adaptive iterative learning control; the construction of an integral lyapunov function solves the control problem caused by derivation of a unknown gain function; based on the median theorem, a hyperbolic tangent function is adopted to approximate an input saturation term; then, a radial basis function neural network is adopted to approximate and compensate uncertain unknown items of a system, and two combined self-adaptive laws are adopted to updatethe weight of the neural network and the bound of estimation errors. Under the condition that the system has input saturation, the invention provides the control method which can compensate the unknown uncertainty of the system, solve the control problem caused by derivation of the unknown gain function and realize two-norm convergence of a tracking error of the system to be near zero within limited iteration times.

Description

technical field [0001] The invention relates to an inverted pendulum adaptive iterative learning inversion control method, in particular to an inverted pendulum control method with unknown input saturation. Background technique [0002] The control problem of the inverted pendulum system is to control the pendulum to reach an equilibrium position quickly, so that it does not have obvious oscillation and excessive angle and speed. The inverted pendulum system has the characteristics of natural instability, strong coupling, strong nonlinearity, and external interference. The research on the inverted pendulum system can effectively reflect the typical problems of nonlinear control, such as nonlinear problems and robustness problems. Therefore, it is of great significance to study the control problem of the inverted pendulum system under the condition of input saturation. [0003] The iterative learning control method is a control method with strong learning ability, especially...

AI Technical Summary

Problems solved by technology

[0006] In order to overcome the unknown input saturation problem existing in the existing inverted pendulum, the present invention provides an inverted pendulum adaptive iterative learning inversion control method, which estimates and compensates the unknown uncertainty of the system in the case of input saturation in the system, and solves the problem caused by the unknown The control problem caused by the derivation of the gain function realizes the control method that the second norm of the system tracking error converges to near zero within a limited number of iterations

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