Publications

Abstract : Parkinson’s disease (PD) is a neurodegenerative disease that inhibits motor activities due to the impairment of brain cells which produce dopamine. Even though medications as well as Deep Brain Stimulations (DBS) are the two treatment options available to control parkinsonian symptoms, the first one is preferred mostly during the early stages of the disease. Continuous medication through Portable Duodopa Pump (PDP) without feedback is the existing method. This paper focuses on the development of a suitable closed-loop control strategy for traditional PDP; thereby ensuring fully automated drug infusion without wearing off. In fact, the drug infused by the controller is increased proportional to reduction in plasma level of dopamine. This results in the alleviation of side effects caused by incorrect dosages in medication therapy. The main control objective is set-point tracking of the closed-loop system with minimum settling time and good steady state accuracy even in the presence of large time delay, food and exercise disturbances as well as parameter variations present in the system. For achieving this objective, we modified the existing dose-effect model of Levodopa. The proposed model is enriched by (i) Levodopa to dopamine conversion factor, (ii) recirculation time delay and (iii) initial dopamine level present in patient’s BP (Blood Plasma). The in-silico analysis of oral medication is used to validate pharmacokinetics (PK), pharmacological activation (PA) and pharmacodynamics of Levodopa. The performance of the proposed fully automatic PDP has been evaluated by traditional Proportional Integral Derivative (PID) controller under two different tuning rules. It is revealed that, both Ziegler- Nichols (ZN) and Particle Swam Optimization (PSO) tuned PID controllers are robust regarding inter-patient variability and the dynamic performance of the later is superior in comparison with the former under intra-patient variability and disturbances.