SELF-ADAPTIVE AND SELF-ORGANIZING CONTROL APPLIED TO NONLINEAR MULTIVARIABLE ANESTHESIA - A COMPARATIVE MODEL-BASED STUDY

Various SISO feedback control techniques have been applied successfully to muscle relaxant anaesthesia in simulations and clinical trials. SISO generalised predictive control (GPC) altogether with self-organising control using fuzzy logic theory (SOFLC) are among these techniques. A multivariable model combining muscle relaxation (paralysis) and anaesthesia (unconsciousness) has been identified. The multivariable version of GPC in its basic form as well as its different extensions to include model following and observer filter polynomials is outlined in addition to the multivariable version of SOFLC. Both of these strategies are applied to the previous model whose parameters were chosen according to a Monte-Carlo method. The robustness of both control strategies is investigated and the results presented and discussed, enabling a comparison to be made between self-adaptive and self-organising techniques. It is concluded that, when a detailed mathematical model structure is available, GPC provides better control than SOFLC.