LMI-Based Reset Unknown Input Observer for State Estimation of Linear Uncertain Systems

This paper proposes a novel kind of Unknown Input Observer (UIO) called Reset Unknown Input Observer (R-UIO) for state estimation of linear systems in the presence of disturbance using Linear Matrix Inequality (LMI) techniques. In R-UIO, the states of the observer are reset to the after-reset value based on an appropriate reset law in order to decrease the $L_2$ norm and settling time of estimation error. It is shown that the application of the reset theory to the UIOs in the LTI framework can significantly improve the transient response of the observer. Moreover, the devised approach can be applied to both SISO and MIMO systems. Furthermore, the stability and convergence analysis of the devised R-UIO is addressed. Finally, the efficiency of the proposed method is demonstrated by simulation results

Dissipative Imitation Learning for Discrete Dynamic Output Feedback Control with Sparse Data Sets

Imitation learning enables the synthesis of controllers for complex objectives and highly uncertain plant models. However, methods to provide stability guarantees to imitation learned controllers often rely on large amounts of data and/or known plant models. In this paper, we explore an input-output (IO) stability approach to dissipative imitation learning, which achieves stability with sparse data sets and with little known about the plant model. A closed-loop stable dynamic output feedback controller is learned using expert data, a coarse IO plant model, and a new constraint to enforce dissipativity on the learned controller. While the learning objective is nonconvex, iterative convex overbounding (ICO) and projected gradient descent (PGD) are explored as methods to successfully learn the controller. This new imitation learning method is applied to two unknown plants and compared to traditionally learned dynamic output feedback controller and neural network controller. With little knowledge of the plant model and a small data set, the dissipativity constrained learned controller achieves closed loop stability and successfully mimics the behavior of the expert controller, while other methods often fail to maintain stability and achieve good performance

Sistemas de control reseteado discretos : aplicaciones a control en red

Esta tesis presenta un estudio de los sistemas de control reseteados en tiempo discreto y su aplicación a los sistemas de control en red. Se da una formulación formal que es coherente con las formulaciones de sistemas de control reseteados continuos. Para ello se emplean elementos de sistemas impulsivos y sistemas conmutados. En este trabajo se aborda la estabilidad de los sistemas reseteados en tiempo discreto empleando técnicas de regularización temporal y también técnicas de análisis de parámetros lineales variables temporalmente. Se emplean controladores reseteados en sistemas de control en red para intentar compensar los efectos negativos que introduce la presencia de la red. Se comprueba que los controladores reseteados, bien sintonizados, pueden contribuir mejorar las prestaciones de los sistemas de control en red. También se han aplicado los resultados obtenidos a dos casos prácticos donde se observa cómo los controladores reseteados pueden superar las limitaciones de los controladores lineales. RESUMEN INGLÉS: This work presents a study of discrete time reset control systems and its application to networked control systems. It gives a formal formulation that is consistent with the formulation of continuous time reset control systems. This is done by using elements of switched and impulsive control theory. This work addresses the stability of discrete time reset control systems employing discrete time version of temporal regularization technique and linear varying parameters analysis. Reset controllers are applied to networked control systems to try to offset the negative effects introduced by the presence of the network, such as the induced delay and packet loss. It can be seen how a well tuned reset controller, can improve the performance of networked linear control systems. The results obtained have been applied into two practical cases where it can be seen how the reset controllers overcome the limitations of linear controllers