Hierarchical Self-Organizing Fuzzy Control for Uncertain Nonlinear Systems

For the uncertain nonlinear systems, the disturbance caused by external factors can affect the accuracy of tracking control. To solve this issue, a hierarchical self-organizing fuzzy control (HSOFC) strategy is proposed to improve the tracking control performance. First, the self-organizing fuzzy neural network is employed as a fuzzy approximator to estimate the unknown nonlinear function. Specifically, a hierarchical strategy is designed to improve the estimation accuracy by preventing the wrong pruning of rules, without setting the pruning threshold based on rule density and significance. Second, an additional robust control component based on sliding mode surface is designed to suppress the influence on the control performance by external disturbances and the approximation error. Furthermore, to further improve the robust control performance and reduce computational complexity, an adaptive allocation strategy is studied to set the parameters of HSOFC. Finally, the stability of HSOFC is proven. The simulation results show that HSOFC can reduce computational complexity and obtain accurate tracking control performance.