TEOMAPIN

Determination of the position of sensors and actuators, optimized based on the maximum performance to be achieved. This objective has been approached theoretically, and the results have been applied to two different Active Noise Control problems, the motorcycle case and the acoustic duct case. One of the most significant results is a set of measures that allow comparing the quality of the sensor and actuator location based on practical performance, robustness, and controller complexity criteria.

Identification of multivariable dynamic systems (MIMO), nonlinear and/or time-varying, represented in continuous time, discrete time, or combinations of both (hybrids). Each application has brought with it restrictions and numerous gaps between theory and practice. Specifically, the identification of plants with variable delays in MIMO systems (irrigation channels) has been theoretically analyzed and practically solved, the nonlinear dynamics of acoustic noise propagation in various spaces (helmets, ducts, and boxes with windows) have been identified, and finally, the case of identification in UAVs, specifically helicopters, has been addressed, with the complexity of a multivariable, coupled, and unstable system. In this last case, a methodology has been developed that allows the application of classical techniques.

(In)validation of the previous systems against experimental data, considering variables such as external noise bounds (additive) and uncertainty (global or structured, time-varying or invariant, linear dynamic or nonlinear static). One of the most notable contributions in this framework is a robust identification/validation method for Linear Parameter Varying (LPV) models. This method allows completing nominal LPV identification algorithms by adding uncertainty and noise bounds that generate a set of models consistent with experimental evidence.

Numerical problems associated with the use of computers or DSPs in the analysis, synthesis, and implementation of controllers. The application of active control and UAV control requires real-time implementation and/or significant computational limitations that prevent the use of arbitrarily large or complex controllers. Moreover, these systems often have zeros with positive real parts and poorly damped oscillations. Therefore, designing a low-order controller is a necessity that represents a great challenge to control theory.

Determination of the most appropriate type of uncertainty to describe a physical system through a family of models and the computation of the least conservative bounds possible. Here, stability and robust and nominal performance in models with uncertain delays have been analyzed for global dynamic uncertainty structures and structured parametric uncertainty.