Toward a model-free feedback control synthesis for treating acute inflammation

Abstract

An effective and patient-specific feedback control synthesis for inflammation resolution is still an ongoing research area. A strategy consisting of manipulating a pro and anti-inflammatory mediator is considered here as used in some promising model-based control studies. These earlier studies, unfortunately, suffer from the difficultly of calibration due to the heterogeneity of individual patient responses even under similar initial conditions. We exploit a new model-free control approach and its corresponding "intelligent" controllers for this biomedical problem. A crucial feature of the proposed control problem is as follows: the two most important outputs which must be driven to their respective desired states are sensorless. This difficulty is overcome by assigning suitable reference trajectories to the other two outputs that do have sensors. A mathematical model, via a system of ordinary differential equations, is nevertheless employed as a "virtual" patient for in silico testing. We display several simulation results with respect to the most varied situations, which highlight the effectiveness of our viewpoint.

Combination of Data-Driven Active Disturbance Rejection and Takagi-Sugeno Fuzzy Control with Experimental Validation on Tower Crane Systems

“…), ( ) ( ) ( (13) and it is next fuzzified using a Takagi-Sugeno fuzzy logic controller and added to the ADRC law in (9) leading to the following control law:…”

“…ADRC [1][2][3][4] is one of the most popular data-driven techniques along with Model-Free Adaptive Control [5][6][7][8][9], Model-Free Control [10][11][12][13][14] or Virtual Reference Feedback Tuning [15][16][17][18]. The main advantage that made data-driven techniques so popular is that they use only the input/output data from the process.…”

The Application of Intelligent Control in Clinical Medicine

“…The computer takes advantage of the best models in the literature and computes the mathematical complexity of patient characteristics (weight, height, age, sex, and additional biomarkers). [11] TCI system can be divided into two kinds of works, closed loop and open loop modes, according to the preset program given. In the closed loop, the plasma drug concentration or the indicators such as blood pressure and heart rate variation obtained through real-time monitoring were fed back to the program module, and then the administration speed was adjusted automatically to meet the real-time need of anesthesia depth during the operation.…”

Supplementary Control of Air–Fuel Ratio Using Dynamic Matrix Control for Thermal Power Plant Emission

“…The main advantage of the MFC technique is that the process model is approximated through a fast estimator using an approximation of the process model, which is locally valid and, furthermore, on a relatively short time window. The MFC techniques were applied to a wide range of processes, which include immune systems [13], robot systems [14,15], twin rotor aerodynamic systems (TRASs) [16][17][18], aircraft system [19] and servo systems [20].…”