Low-complexity and adaptive saturation control method for nonlinear system

Abstract

The invention discloses a low-complexity and adaptive saturation control method for a nonlinear system. The control method comprises the following steps of: firstly, converting control saturation nonlinearity into a linear model relative to an actual input signal by adopting a dead zone model; then, converting an original system with time-varying output constraint into unconstrained output constraint, and designing a new adaptive saturation control law along the filtered error manifold based on the system; and finally, by adopting the minimum learning parameter technology and the virtual errorconcept, only requiring update of two adaptive parameters on line to greatly reduce the calculation burden. Compared to the current work, the calculation amount of the adaptive scheme is small, and the asymptotic stability of the tracking error system can be ensured instead of the consistent final bounded stability. The adopted simulation shows that an improvement of about 4 times in tracking accuracy can be obtained, which demonstrates the effectiveness of the proposed control law.

Description

technical field [0001] The invention relates to a control algorithm, in particular to a low-complexity self-adaptive saturation control method for a nonlinear system. Background technique [0002] Over the past few decades, there has been increasing interest in the study of adaptive control system methods for nonlinear systems, such as Duffing-Holmes chaotic systems and reluctance motor systems, and remarkable research results have been achieved, such as based on Adaptive neural network (NN) control, perturbed strict feedback nonlinear systems, and barrier Lyapunov function (BLF) based output control are all used for strict feedback systems with unknown nonlinearities. [0003] Among existing adaptive control schemes, backstepping techniques have been developed as effective tools for dealing with nonlinear cascaded systems (also known as strict or pure feedback systems). For example, an adaptive neural control protocol based on input-to-state stability (ISS) was developed f...

AI Technical Summary

Problems solved by technology

Although the computational complexity can be greatly reduced, it is still computationally expensive to update NN or fuzzy logic system elements, especially for high-order nonlinear systems

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