SISO Model Predictive Control for Portable Medication Delivery
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Solution Overview
Problem
Existing medication delivery systems, such as insulin pumps, often rely on open-loop control and struggle with complex dynamics and computational efficiency, making it difficult to implement effective closed-loop control for patients, especially in portable devices where resources are limited.
Innovation Solution
The implementation of a single input-single output (SISO) model predictive control technique in medication delivery systems, which uses real-time patient measurements to predict characteristics and determine optimal medication delivery, stabilizing patient conditions while minimizing medication use and simplifying setup and maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If open-loop control is used in medication delivery systems, then device complexity is reduced, but control precision and patient condition stabilization deteriorate
Solution Approach 1:
The patent transforms the control approach by changing the mathematical model parameters from complex multi-variable models to simplified single-input single-output (SISO) models. This parameter simplification allows portable devices to implement model predictive control with reduced computational requirements while maintaining effective closed-loop control precision for patient conditions.
Solution Approach 2:
The patent applies local quality by focusing control efforts on the most critical patient parameters rather than attempting to control all physiological variables. The SISO model predictive control targets specific key parameters (such as glucose levels) with optimized control algorithms, achieving high precision for the most important control objectives without the computational burden of comprehensive multi-parameter control.
2Measurement precision
If complex control algorithms are implemented, then control precision improves, but computational efficiency deteriorates
Solution Approach 1:
The patent dramatically reduces computational complexity by changing from complex multi-variable predictive control models to simplified SISO models. This parameter reduction maintains sufficient control precision for clinical applications while decreasing computational requirements by orders of magnitude, enabling implementation in portable devices with limited processing power and battery capacity.
Solution Approach 2:
The patent segments the control problem into independent single-input single-output sub-problems rather than attempting to solve a complex coupled multi-variable system. This segmentation allows each parameter to be controlled independently with simple predictive algorithms, significantly improving computational efficiency while maintaining effective closed-loop control for the most critical patient parameters.
3Reliability
If closed-loop control is implemented in portable devices, then patient condition stabilization improves, but device complexity and computational requirements increase
Solution Approach 1:
The patent enables closed-loop control in portable devices by fundamentally changing the control algorithm parameters from complex models requiring significant computational resources to simplified SISO models with minimal computational requirements. This parameter simplification maintains reliable patient condition stabilization while reducing device complexity to levels suitable for portable implantable pumps with limited processing and memory capabilities.
Solution Approach 2:
The patent achieves reliable patient condition stabilization in portable devices by applying local quality control - focusing computational resources on optimizing control for the most critical physiological parameters rather than attempting comprehensive control of all body functions. This selective approach provides sufficient reliability for life-critical applications while keeping device complexity within portable device constraints.
4Speed
If real-time predictive control is used, then response speed to patient condition changes improves, but computational intensity increases
Solution Approach 1:
The patent achieves fast real-time response to patient condition changes by changing the control model parameters to simplified SISO formulations that require minimal computational operations per control cycle. This parameter simplification enables frequent control updates and rapid response to glucose level changes while consuming minimal computational energy, extending battery life in portable devices.
Solution Approach 2:
The patent segments the predictive control computation into simple independent calculations for each critical parameter rather than performing complex matrix operations on coupled multi-variable systems. This segmentation dramatically reduces the computational intensity and energy consumption per control cycle while maintaining fast response speed to patient condition changes through frequent control updates.
Data Source
AI summary
A method includes receiving measurements from a sensor associated with a patient at a portable medication delivery device. The method also includes controlling delivery of medication to the patient at the portable medication delivery device using a single input, single output (SISO) model predictive control technique. The SISO model predictive control technique includes predicting a characteristic of the patient using the measurements and a model associated with the patient. The SISO model predictive control technique also includes determining whether the characteristic of the patient is predicted to fall outside of a desired range. In addition, the SISO model predictive control technique includes, if the characteristic of the patient is predicted to fall outside of the desired range, determining an amount of medication to deliver to the patient and delivering the determined amount of medication to the patient.