Adaptive controller based on quantum computation and coherent superposition fuzzy rules network with unknown nonlinearities.

In the realm of control engineering applications, compensating for unknown dynamics and nonlinearities is of paramount importance for shaping closed-loop performance. This paper introduces a novel solution to this challenge: the adaptive controller based on Quantum-Inspired Fuzzy Rules Emulated Network (QFREN). Leveraging its intrinsic learning capacity, QFREN assimilates human knowledge through a series of IF-THEN rules based on quantum computation principles. By defining quantum states for membership functions, the concept of coherent superposition of tracking errors is employed to effectively mitigate the effects of disturbances and nonlinearities. Learning laws are derived to finely calibrate all network and quantum computation parameters, accompanied by a thorough analysis of closed-loop performance to ensure robustness. Experimental validation and comparative assessments substantiate the efficacy of the proposed scheme, showcasing a reduction in tracking error of at least 20 % compared to recent comparative controllers based on data-driven and quantum-neural network schemes. [ABSTRACT FROM AUTHOR]