A review of convex approaches for control, observation and safety of linear parameter varying and Takagi-Sugeno systems

This paper provides a review about the concept of convex systems based on Takagi-Sugeno, linear parameter varying (LPV) and quasi-LPV modeling. These paradigms are capable of hiding the nonlinearities by means of an equivalent description which uses a set of linear models interpolated by appropriately defined weighing functions. Convex systems have become very popular since they allow applying extended linear techniques based on linear matrix inequalities (LMIs) to complex nonlinear systems. This survey aims at providing the reader with a significant overview of the existing LMI-based techniques for convex systems in the fields of control, observation and safety. Firstly, a detailed review of stability, feedback, tracking and model predictive control (MPC) convex controllers is considered. Secondly, the problem of state estimation is addressed through the design of proportional, proportional-integral, unknown input and descriptor observers. Finally, safety of convex systems is discussed by describing popular techniques for fault diagnosis and fault tolerant control (FTC).Peer ReviewedPostprint (published version

Decision-Oriented Multi-Outcome Modeling for Anesthesia Patients

Anesthesia drugs have impact on multiple outcomes of an anesthesia patient. Most typical outcomes include anesthesia depth, blood pressures, heart rates, etc. Traditional diagnosis and control in anesthesia focus on a one-drug-one-outcome scenario. This paper studies the problem of real-time modeling for monitoring, diagnosing, and predicting multiple outcomes of anesthesia patients. It is shown that consideration of multiple outcomes is necessary and beneficial for anesthesia managements. Due to limited real-time data, real-time modeling in multi-outcome modeling requires low-complexity model strucrtures. This paper introduces a method of decision-oriented modeling that significantly reduces the complexity of the problem. The method employs simplified and combined model functions in a Wiener structure to contain model complexity. The ideas of drug impact prediction and reachable sets are introduced for utility of the models in diagnosis, outcome prediction, and decision assistance. Clinical data are used to evaluate the effectiveness of the method