Adaptive fuzzy command-filtered control strategy for electro-hydraulic braking systems of winding hoist with prescribed performance

In the braking control of winding hoisting systems, proper braking strategy is of great significance in reducing the probability of significant hidden dangers and improving the safety and reliability of the system. However, as a typical elastic and variable stiffness system, the winding hoisting systems have large nonlinearity, parameter uncertainties, random disturbances, and so on, which severely restrict the improvement of braking performance. To this end, an adaptive fuzzy command-filtered controller with prescribed performance is developed for electro-hydraulic braking systems of winding hoist in this article. First, the dynamic model of the winding hoisting system is established with consideration of the elasticity of wire rope. Based on the established dynamic model, using the fuzzy logic system, the algebraic loop problem caused by non-strict feed structure is overcome, and the unknown nonlinearities arising from modeling error, parameter uncertainties, and system disturbances are also approximated for the subsequent active unknown nonlinearities compensation. Next, to enhance the convergence rate and guarantee the position tracking error within a prescribed region, a tracking error transformation method based on the prescribed performance function is utilized. Subsequently, the adaptive fuzzy control algorithm is developed with command filters, which can also avoid the complexity explosion issue due to the analytic calculation of the derivatives of stabilizing functions, and the rigorous stability of the close-loop system is proved by the Lyapunov theory. Finally, experimental results are utilized to illustrate the effectiveness of the developed controller.

Automated summary

In this article, an adaptive fuzzy command-filtered controller is developed for the electro-hydraulic braking systems of winding hoists. The controller is able to overcome non-strict feed structure and approximate unknown nonlinearities arising from modeling error, parameter uncertainties, and system disturbances. The use of command filters avoids complexity explosion and the stability of the closed-loop system is proved by the Lyapunov theory. Experimental results demonstrate the effectiveness of the developed controller.