Type I diabetes mellitus (T1DM) is a chronic metabolic disease resulting from insufficient insulin secretion into the bloodstream‚ causing elevated blood glucose concentrations to dangerous levels․ Automated regulation of blood glucose levels in T1DM can be modeled as a nonlinear‚ uncertain‚ and disturbance-affected closed-loop control process with a time delay‚ time-varying insulin sensitivity, and imperfect glucose measurements. This paper presents the design, multi-objective tuning, and robustness evaluation of a fuzzy logic controller (FLC) for automated insulin-infusion regulation. The proposed FLC uses the glucose tracking error and its time derivative as feedback signals to determine the required insulin control action and maintain glucose within the desired range of (70–160 mg/dL). The controller parameters are optimized using the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) to address three competing control objectives: minimizing hypoglycemia risk, minimizing hyperglycemia risk, and reducing total insulin usage. The resulting Pareto-optimal solutions provide a set of trade-off controller designs for decision-makers based on safety, performance, and insulin-efficiency requirements. The robustness of the proposed automated control framework is evaluated under challenging operating conditions, including elevated initial glucose levels, model-parameter uncertainties, external disturbances, variations in insulin sensitivity, distorted glucose measurements, and delayed insulin infusion. A comparative study with a linear quadratic regulator-based controller (LQRC) is conducted as a benchmark. Simulation results demonstrate that the optimized FLC provides superior closed-loop performance and stronger robustness than the LQRC across all tested scenarios. The proposed fuzzy-control framework, therefore, offers a promising automation-based strategy for resilient glucose regulation under uncertainty, measurement imperfections, and actuation delays.
Raya Abu Shaker, Yousef Sardahi, Ahmad M. Alshorman· Automation· 0 citations
Fast charging of lithium-ion battery packs involves a compromise between charging speed, temperature rise, and aggressive current profiles that may accelerate battery degradation. This paper presents a current-stress-aware fuzzy logic control framework for safe fast charging of series-connected lithium-ion battery cells. The proposed controller uses a physically interpretable two-input, one-output fuzzy structure in which the highest cell-voltage difference, Vd, and the lowest single-cell voltage, VB, are used to determine the charging-current command, Icharge. Unlike conventional fuzzy charging approaches that rely on manually selected membership functions or weighted single-objective tuning, the proposed method simultaneously optimizes the Gaussian membership-function parameters and the input/output scaling gains using a Pareto-based multi-objective optimization framework. The resulting design vector contains 21 decision variables, including 18 membership-function parameters and three scaling gains. Three conflicting objectives are minimized: the time required to reach 95% state of charge, the maximum temperature rise above the reference temperature, and a normalized current-stress index based on the integral of the squared charging current. The framework is implemented in MATLAB/Simulink using a three-cell Panasonic NCR18650PF lithium-ion battery pack model. The obtained Pareto front reveals the expected trade-off between fast charging and battery protection. The fastest solution reaches 95% SOC in 5440 s but produces the highest temperature rise and current-stress index, whereas the selected knee-point controller reaches the target in 6880 s while reducing the maximum temperature rise and current-stress index compared with the fastest solution. Robustness tests under variations in initial SOC, cell imbalance, initial temperature, capacity scaling, and internal-resistance scaling show that the knee-point controller maintains stable charging behavior and satisfies the imposed thermal safety constraint. The results demonstrate that the proposed current-stress-aware Pareto-optimized fuzzy controller provides a systematic and interpretable approach for balancing charging speed, thermal safety, and battery stress in lithium-ion battery fast charging.
