The compared controllers were an already established controller based on elemental balances (EBC), which was adapted for two feeds, and a model predictive controller (MPC) based on a mechanistic model, which was developed and configured for this study.
As shown in Fig. 2, the desired ranges reported in clinical publications have varied greatly, making it difficult to compare controller performance across trials.
The performance of the Type-2 (T2) controller is compared with conventional controller and Type-1 (T1) fuzzy controller with regard to Generation Rate Constraint (GRC).
It represents an increase of variations, control efforts, and lower performance of reduced order controller compared to full order controller.
P-action controller is compared with fuzzy controller in the case of structured uncertainty.
Frequency distortions are drastically reduced with sliding mode controller when compared to PI controller as shown in Fig. 15.
The corresponding controller and its closed-loop response is then compared with controllers that are determined from the original model and a linearized version of this model.
During the second lengthening approach, distraction progress declined in the radiographic controls compared to the controller.
TI CC3200 reduces the complexity and improves speed of operation compared with controllers with external Wi-Fi module (Texas instrument CC3200 Simple Link, 2017).
Performance of the ZA-LMS controller is compared with conventional controllers such as normalized least mean square (NLMS) and dq0 frame theory.
