In general, the BFO-optimized PID control method produces much better system performance and optimized behavior than PID control scheme, where the optimum controller values are resultant from the IAE criterion.
HIV controllers whose VL values were always below the detection limit of the assays were compared with those who had rare "blips" (<50% of VL values above the detection limit) or frequent blips (>50% of VL values above the detection limit).
In 8.6% of cases, venous blood, rather than sensed, glucose values were sent to the controller due to suboptimal sensor accuracy.
From Figures 5-8, we can see that with output-feedback controller, the state values are larger and the rate of convergence is faster than those with state-feedback controller.
(34 37), {β ProbV, β QrobV, β ProbA, β QrobA } > 0, are the gains of the controller, whose initial values are mentioned in the Appendix.
On the other hand, with the non-switching controller, all global minimum values are between 1.70×10−09 and 1.44×10−06 (the mean is 1.84×10−07, and the standard deviation is 2.70×10−07).
Therefore, the neural controller weights and bias values are obtained from the MATLAB environment as a starting point for hardware implementation.
The controller design is achieved in two steps: first, a controller based on the nominal parameter values is determined, while the parameter variations and the deterioration of the ideal control law, due to the use of a real actuator, are considered disturbances acting on the nominal plant.
For the synthesis of controller, the fault time, patterns and values are unknown in advance, as motivated from a practical spacecraft control application.
The learning algorithm uses a new self-organizing rule that has been specifically designed to learn a controller mapping even when succeeding sensor values are correlated.
