This research proposes a robust two-stage optimization framework for the efficient design and tuning of Fuzzy Logic Controllers (FLCs) applied to nonlinear control systems. Conventional FLCs often rely on trial-and-error tuning or single optimization methods, which may result in suboptimal performance, slow convergence, or entrapment in local optima. To address these limitations, the proposed methodology systematically integrates a global optimization stage with a local refinement stage, thereby leveraging the complementary strengths of exploration and exploitation. In the first stage, a Genetic Algorithm (GA) is employed to perform global search over high-dimensional decision variables, including membership function parameters, scaling factors, and rule weights. The GA ensures population diversity and robustness against nonlinearities while minimizing performance indices such as integral of squared error, rise time, settling time, and overshoot. The best candidate solution obtained is then passed to the second stage, where Pattern Search (PS), a derivative-free local optimization technique, refines the solution to achieve higher accuracy. The hybrid framework is implemented in MATLAB and validated on a DC motor speed control problem. Results demonstrate that the proposed two-stage optimization yields faster response, reduced overshoot, and lower steady-state error compared to single-method approaches, ensuring superior robustness and efficiency in fuzzy controller design.
Fuzzy Logic Controller (FLC); Genetic Algorithm (GA); Pattern Search (PS); Two-stage optimization; Membership function tuning; DC motor speed control; Nonlinear control systems; Hybrid optimization; Robust control; Intelligent control.
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