HYBRID ADAPTIVE INTELLIGENT CONTROLLER DESIGN USING QUANTUM WAVELET NEURAL NETWORKS FOR TRAJECTORY TRACKING CONTROL IN FINITE DIMENSIONAL CLOSED QUANTUM SYSTEMS

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SAHEBI ZEINAB; Yarahmadi Majid

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Journal: Modares Mechanical Engineering Year:2018 | Volume:18 | Issue:2 Page(s): 179-188

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Title

HYBRID ADAPTIVE INTELLIGENT CONTROLLER DESIGN USING QUANTUM WAVELET NEURAL NETWORKS FOR TRAJECTORY TRACKING CONTROL IN FINITE DIMENSIONAL CLOSED QUANTUM SYSTEMS

Author(s)

SAHEBI ZEINAB | Yarahmadi Majid | Issue Writer Certificate

Keywords

QUANTUM ADAPTIVE CONTROLQ1

QUANTUM WAVELET NEURAL NETWORKQ1

QUANTUM INTELLIGENT CONTROLQ1

QUANTUM TRAJECTORY TRACKINGQ1

QUANTUM HYBRID CONTROLLERQ1

Abstract

In this paper, a new hybrid adaptive intelligent controller is introduced to track a dynamic trajectory in finite dimensional closed quantum systems. The problem of inherent singularities in control signals of trajectory tracking in quantum systems leads to a sharp increase in control signal amplitude. As a result, the amplitude of the large signal increases the control cost and control system instability. Consequently, the large control signal amplitude increases the control cost and leads to instability in control system. Firstly, according to the Lyapunov stability theory, an adaptive controller is designed to track the dynamic path. Then, to overcome the singularity drawback, a QUANTUM INTELLIGENT CONTROLler is designed based on a quantum adaptive wavelet neural network with batch back propagation learning and combined with adaptive controller by a singularity observer. The proposed hybrid adaptive intelligent controller by combining the adaptive and intelligent control signals adjusts the quantum state so that the desired dynamic trajectory is traced effectively and simultaneously eliminates the effects of singularities and reduces the control amplitude. The performance of the hybrid adaptive intelligent controller is checked for step response tracking in a population transfer of a four-level closed quantum system. The simulation results show that the introduced controller reduces the tracking error and significantly decreases the number of singular points. Also, the control cost is reduced by effective adjustment of the control signal’s amplitude.

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APA: Copy

SAHEBI, ZEINAB, & Yarahmadi, Majid. (2018). HYBRID ADAPTIVE INTELLIGENT CONTROLLER DESIGN USING QUANTUM WAVELET NEURAL NETWORKS FOR TRAJECTORY TRACKING CONTROL IN FINITE DIMENSIONAL CLOSED QUANTUM SYSTEMS. MODARES MECHANICAL ENGINEERING, 18(2 ), 179-188. SID. https://sid.ir/paper/178669/en

Vancouver: Copy

SAHEBI ZEINAB, Yarahmadi Majid. HYBRID ADAPTIVE INTELLIGENT CONTROLLER DESIGN USING QUANTUM WAVELET NEURAL NETWORKS FOR TRAJECTORY TRACKING CONTROL IN FINITE DIMENSIONAL CLOSED QUANTUM SYSTEMS. MODARES MECHANICAL ENGINEERING[Internet]. 2018;18(2 ):179-188. Available from: https://sid.ir/paper/178669/en

IEEE: Copy

ZEINAB SAHEBI, and Majid Yarahmadi, “HYBRID ADAPTIVE INTELLIGENT CONTROLLER DESIGN USING QUANTUM WAVELET NEURAL NETWORKS FOR TRAJECTORY TRACKING CONTROL IN FINITE DIMENSIONAL CLOSED QUANTUM SYSTEMS,” MODARES MECHANICAL ENGINEERING, vol. 18, no. 2 , pp. 179–188, 2018, [Online]. Available: https://sid.ir/paper/178669/en

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