Identification and low-complexity regime-switching insulin control of type I diabetic patients

Abstract

This paper studies benefits of using simplified re-gime-switching adaptive control strategies in improving performance of insulin control for Type I diabetic patients. Typical dynamic models of glucose levels in diabetic patients are nonlinear. Using a linear time invariant controller based on an operating condition is a common method to simplify control design. On the other hand, adaptive control can potentially improve system performance, but it increases control complexity and may create further stability issues. This paper investigates patient models and presents a simplified switching control scheme using PID controllers. By comparing different switching schemes, it shows that switched PID controllers can improve performance, but frequent switching of controllers is unnecessary. These findings lead to a control strategy that utilizes only a small number of PID controllers in this scheduled adaptation strategy

Observer-Based State Feedback for Enhanced Insulin Control of Type ‘I’ Diabetic Patients

“…The design of the PID controller was done by applying the root locus method at these operating points. The parameters K p , K i , and K d of the PID at each operating points are shown in Table 2 [34]. …”

“…When the control scheme was simulated with only PID controllers$g_c^1,g_c^{20},g_c^{40}\mathit{ and }{g}_c^{60}$ are executed, the output g(t) of the system reaches the glucose basal level (99 mg/dl) within 40 minutes, and it stays in that neighborhood as shown in Fig. ( 3 ) [34]. …”

“…In comparison to individual controllers, the regime-switching control scheme achieves design specification while all individual controllers fail to deliver the required performance [34]. As a result, it is desirable to investigate simpler and more robust control strategies.…”

“…( 1 ), it can be clearly seen that the glucose level does not come down to the basal level after injecting an amount of 337 mg/dl of glucose inside a diabetic patient. The graph shows that the level of the glucose inside a diabetic patient decreases for almost the first 100 minutes and starts increasing afterward and reaches the value of almost 310 mg/dl after 3 hours from the time the glucose was injected [ 34 ]. The goal is to bring down the value of the glucose inside a diabetic patient to the normal level.…”

Chattering ‐ free hybrid adaptive neuro‐fuzzy inference system‐particle swarm optimisation data fusion‐based BG‐level control

“…In [23], by switching on Bergman model (BM) and setting appropriate parameters and overshoot time, the suggested proportional–integral–derivative (PID) controller will be able to reach the BG levels into a reference level. Controllers based on induced L 2‐norm minimisation of glucose–insulin system [24], improved PID [25], fuzzy logic [26] and robust control approach [27] have been studied and tested in clinical trials.…”