فهرست نشریات علمی دسترسی آزاد ایران

This paper proposes a hybrid approach that integrates an Improved Shark Smell Optimization (ISSO) algorithm with a Fuzzy Ant Colony System (FACS) to enhance the parameter tuning of a fuzzy hierarchical controller for the inverted pendulum and cart system. The standard Shark Smell Optimization (SSO) algorithm suffers from premature convergence and limited exploration capability. The proposed ISSO addresses these limitations by introducing dynamic adaptive coefficients for the gradient and inertia phases, along with a linearly decreasing velocity limit factor. This enhanced global search is then synergistically combined with FACS, which performs fuzzy-guided local exploitation in promising regions of the parameter space. The ISSO-FACS hybrid is applied to determine the optimal sliding surface parameters and scaling factors of a Fuzzy Hierarchical Swing-up and Sliding Controller (FHSSC). Simulation results demonstrate that the proposed approach significantly outperforms the previous FACS method, reducing the total stabilization error by 11.59%, the pendulum angle error by 15.23%, and the cart position error by 26.72%, while achieving 1.27 times faster settling time. The proposed controller also exhibits superior robustness under perturbed initial conditions where the previous method fails, and demonstrates enhanced disturbance rejection capabilities. Convergence analysis confirms the stability properties of the hybrid approach, and computational complexity analysis validates its suitability for offline controller design.