A Model-Free Adaptive Sliding Mode Constraint Control Method Based on Improved Preset Performance

Abstract

The invention relates to the technical field of automatic control, and specifically discloses a model-free adaptive sliding mode constraint control method based on improved preset performance. The method includes the steps of: converting a discrete nonlinear system with an external disturbance into an affine system; Define the tracking error including the compensation vector, and design the convergence domain including the preset function and the convergence function, so that the boundary of the convergence domain gradually converges to a symmetrical boundary as time changes; the tracking error constrained by the convergence domain is transformed into an unconstrained variable; according to The sliding mode function is designed with unconstrained variables; the limited controller is designed according to the sliding mode function and the tracking error including the compensation vector; the method uses the preset function and the convergence function to design a convergence domain that gradually converges to a symmetric boundary with time, and the tracking The compensation vector is added to the error, so that the tracking error can be in the central part of the symmetric boundary when the tracking error is stable, thereby eliminating the tracking error offset error caused by the asymmetry of the upper and lower boundaries of the convergence domain.

Description

technical field [0001] The present application relates to the technical field of automation control, in particular, to a model-free adaptive sliding mode constraint control method based on improved preset performance. Background technique [0002] With the continuous development of engineering technology, the complexity of control systems is increasing, making the accurate modeling of the system the most difficult task. [0003] Traditional control strategies based on system mathematical models are no longer suitable for increasingly complex control systems. To overcome such problems, data-driven control methods using only input and output data have emerged. Data-driven control methods have been applied in many practical scenarios, such as on four-wheel drive vehicles, autonomous vehicles, robots, and other industrial process systems; model-free adaptive control methods are one of the data-driven methods. [0004] The existing model-free adaptive control methods generally ...

AI Technical Summary

Problems solved by technology

[0004] The existing model-free adaptive control methods generally have the following problems: when the upper and lower boundaries of the preset function of the convergence domain are not equal, there is always an offset error in the steady state of the tracking error, and precise control cannot be achieved

[0005] For the above problems, there is no effective technical solution

Images