Name
Abstract | ||
|---|---|---|
This paper deals with the use of proportional-integral-derivative controllers for the closed-loop control of the depth of hypnosis in anesthesia by using propofol administration and the bispectral index as a controlled variable.The controller parameters are optimized by using genetic algorithms and it is shown that a gain scheduling strategy should be employed to address the induction and maintenance phases separately.The selection of the filter on the controller output is also considered and the trade-off between the performance and the noise effect in the control variable is analyzed. Background and Objective: This paper addresses the use of proportional-integral-derivative controllers for regulating the depth of hypnosis in anesthesia by using propofol administration and the bispectral index as a controlled variable. In fact, introducing an automatic control system might provide significant benefits for the patient in reducing the risk for under- and over-dosing.Methods: In this study, the controller parameters are obtained through genetic algorithms by solving a min-max optimization problem. A set of 12 patient models representative of a large population variance is used to test controller robustness. The worst-case performance in the considered population is minimized considering two different scenarios: the induction case and the maintenance case.Results: Our results indicate that including a gain scheduling strategy enables optimal performance for induction and maintenance phases, separately. Using a single tuning to address both tasks may results in a loss of performance up to 102% in the induction phase and up to 31% in the maintenance phase. Further on, it is shown that a suitably designed low-pass filter on the controller output can handle the trade-off between the performance and the noise effect in the control variable.Conclusions: Optimally tuned PID controllers provide a fast induction time with an acceptable overshoot and a satisfactory disturbance rejection performance during maintenance. These features make them a very good tool for comparison when other control algorithms are developed. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.cmpb.2017.03.013 | Computer Methods and Programs in Biomedicine |
Keywords | Field | DocType |
Depth of hypnosis control,Gain scheduling,Genetic algorithms,PID control | Population,Control theory,PID controller,Control theory,Gain scheduling,Computer science,Overshoot (signal),Population variance,Anesthesia,Automatic control,Control variable | Journal |
Volume | Issue | ISSN |
144 | C | 0169-2607 |
Citations | PageRank | References |
6 | 0.52 | 12 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fabrizio Padula | 1 | 31 | 11.59 |
| Clara M. Ionescu | 2 | 351 | 62.42 |
| N. Latronico | 3 | 12 | 2.71 |
| Massimiliano Paltenghi | 4 | 12 | 2.38 |
| Antonio Visioli | 5 | 224 | 40.89 |
| Giulio Vivacqua | 6 | 6 | 0.52 |