Model-free direct adaptive controller based on quantum-inspired fuzzy rules network for a class of unknown discrete-time systems

This work introduces a novel direct adaptive controller designed specifically for unknown nonlinear discrete -time systems, demonstrating effective handling of various nonlinearities, including dead -zones. The controller leverages a Quantum -inspired Fuzzy Rules Emulated Network (QFREN), allowing for the omission of input-output scaling and the design of membership functions based on practical operating ranges. By harnessing the power of quantum computation, it thoroughly analyzes closed -loop performance without relying on restrictive conditions. Experimental validation using a nonlinear passive circuit illustrates the controller's proficiency in emulating nonlinearities. The efficacy of the proposed controller is further affirmed through comprehensive assessments, including tracking performance, convergence of adjustable parameters, and compensation of nonlinearities.