Optimization of Interval Type-2 Fuzzy Servo Controllers for State Feedback in Nonlinear Systems Using a Quantum Genetic Algorithm
Nonlinear Control, Interval Type-2 Fuzzy Logic, Quantum Genetic Algorithm, State Feedback, MAGLEV
The control of nonlinear systems subject to uncertainties and open-loop instability, such as magnetic levitation systems, which are the central focus of this study, poses significant challenges to engineering, making it difficult to obtain precise mathematical models for achieving full control. This work proposes the development and analysis of a tuning strategy for optimizing servo controllers based on Interval Type-2 Fuzzy Logic applied to state feedback. The control architecture uses Parallel Distributed Compensation to integrate local linear controllers with integral action, ensuring trajectory tracking. Given the challenge in fine-tuning the controller to handle uncertainties modeled by the Footprint of Uncertainty, the application of a Quantum Genetic Algorithm is investigated. Performance is compared to that of the classical Genetic Algorithm in optimizing closed-loop pole placement, as well as against the classical Fuzzy system, also optimized by the same strategy. Results obtained through computational simulations demonstrated the effectiveness of the approach: the GA achieved the best Integral of Time-weighted Absolute Error index, indicating fast stabilization, while the QGA excelled in precision, minimizing the Root Mean Square error, confirming the technical feasibility of the proposal.