The objective of this project is to develop patient dose-response models and to provide an adequate drug administration regimen for anaesthesia to avoid under- or over-dosing of patients. The controllers are designed to compensate for patients’ inherent drug response variability, in order to achieve the best output disturbance rejection, and to maintain optimal set point response.

To address this issue, this project uses four independent methods to investigate the control strategies for the regulation of hypnosis. Two medications are used in a thorough evaluation and comparison of controller performance.

An automatic controller that infuses drugs based on the patient’s anaesthetic level will provide the following benefits:
1. It will reduce the anaesthetist’s workload during the surgery and allow him/her to monitor and deal with other critical aspects of the surgery (blood loss, sudden blood pressure change, etc.).
2. Better depth of anaesthesia will be achieved compared to manual administration because the controller variable is sampled more frequently leading to active adjustment of the delivery rate of the drug.
3. A well-designed automatic control system can tailor the drug dosage based on the patient’s response, which avoids both over-dosage and under-dosage of the drugs. Overall, these improve the patient’s rehabilitation and safety during and after the surgery.
1. Collect data from Toowoomba Based Hospital;
2. Import this data from spreadsheet files into MATLAB;
3. Analyse the data to establish the relative importance of each independent variable in the prediction;
4. Develop the models based on the data analysis.

The methods to use in this project:
Internal Model Control (IMC) to generate the framework. This method provides a much easier framework for the design of robust control system.

Assoc Prof Shahab Abdulla

• Professor Paul Wen

• Open Access College
• School of Engineering

• Biomedical Engineering

• Doctor of Philosophy (DPHD)
• Doctor of Philosophy (DPHD)
• Master of Research (MRES)