Abstract

The problem of discontinuous derivatives with respect to the control effort is observed in the prototype of preproduction DC motor torque-control. This behavior violates the basic requirement of conventional adaptive controllers. To overcome this issue, a quantum-inference fuzzy rules emulated network (QFREN) is developed, enabling the crossing state of discontinuity through a coherent superposition of its qbit membership functions. The adjustable parameters, which encompass both the linear parameters of QFREN and the nonlinear parameters of the rotation gate and quantum-controlled gate, are adjusted using the derived learning laws. Moreover, theoretical results are provided to ensure the convergence of the tracking error based on the selection of the designed variables in a practical context. Experimental validation is conducted to verify the effectiveness of the proposed controller. Additionally, comparative results with similar schemes are presented to highlight the advantages of the proposed approach. Note to Practitioners - The presence of discontinuous derivatives and nonlinearities, such as dead-zone, can compromise the closed-loop performance of control systems. Utilizing adaptive controllers based on quantum computation and neural networks (QNN) appears promising in mitigating these challenges. However, QNN approaches have predominantly remained in theoretical and numerical simulation contexts. In this work, an application of QFREN as a controller is introduced in a practical control engineering scenario. QFREN demonstrates its effectiveness in addressing the issue of discontinuous derivatives and compensating for unknown nonlinearities.