Using Sliding Mode Controller and Eligibility Traces for Controlling the Blood Glucose in Diabetic Patients at the Presence of Fault

Authors

  • A. Noori Faculty of Electrical and Robotics Engineering, Shahrood University of Technology, Shahrood, Iran
  • M. A. Sadrnia Faculty of Electrical and Robotics Engineering, Shahrood University of Technology, Shahrood, Iran
  • M. B. Naghibi Sistani Department of Electrical Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
Abstract:

Some people suffering from diabetes use insulin injection pumps to control the blood glucose level. Sometimes, the fault may occur in the sensor or actuator of these pumps. The main objective of this paper is controlling the blood glucose level at the desired level and fault-tolerant control of these injection pumps. To this end, the eligibility traces algorithm is combined with the sliding mode control. The eligibility traces algorithm is one of the newest solving methods of the Reinforcement Learning approach. The major disadvantage of the sliding mode control method is the chattering phenomenon. In this paper, the novel idea is the combination of these methods to remove the chattering phenomena in simulation results. To demonstrate the superiority of the proposed method, it is compared with another combinatory method that is the sliding mode control and Artificial Neural Networks. Simulation results reveal that the combination of the eligibility traces algorithm and the sliding mode control can control the blood glucose level and insulin with a higher speed and bring them to the desired level, even in the case the sensor and actuator faults are present in the system. When the proposed hybrid method is used, the injected dosage of the drug is lower, which will result in reduced side effects. Finally, the noise, as well as the uncertainty in system parameters and initial conditions are applied to the system to investigate the performance of the proposed controller, under the faulty condition.

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Journal title

volume 33  issue 2

pages  257- 268

publication date 2020-02-01

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