Mehran Shakarami

Mehran Shakarami, Claudio De Persis, Nima Monshizadeh

arXiv preprint arXiv:1910.13928

2019

This paper considers the problem of Nash equilibrium (NE) seeking in aggregative games, where the payoff function of each player depends on an aggregate of all players' actions. We present a distributed continuous time algorithm such that the actions of the players converge to NE by communicating to each other through a connected network. A major concern in communicative schemes among strategic agents is that their private information may be revealed to other agents or to a curious third party who can eavesdrop the communications. We address this concern for the presented algorithm and show that private information of the players cannot be reconstructed even if all the communicated variables are compromised. As agents may deviate from their optimal strategies dictated by the NE seeking protocol, we investigate robustness of the proposed algorithm against time-varying disturbances. In particular, we provide rigorous robustness guarantees by proving input to state stability (ISS) properties of the NE seeking dynamics. Finally, we demonstrate practical applications of our theoretical findings on two case studies; namely, on an energy consumption game and a charging coordination problem of electric vehicles.

Mehran Shakarami, Kasra Esfandiari, Amir Abolfazl Suratgar, Heidar Ali Talebi

IEEE Transactions on Automatic Control

2020

This article presents a new state estimation scheme using the second-level adaptation technique and multiple high-gain observers (MHGO) for improving the transient response and attenuating undesired peaks of high-gain observers (HGOs). The proposed method, MHGO, considers state estimation as a convex combination of provided information by multiple HGOs. In this regard, it is shown that there exist some constant parameters in such combination that result in perfect state estimation; then, an adaptive algorithm is employed for estimating those parameters. The stability of the proposed scheme and convergence of state estimation to the state of the plant are guaranteed. In addition, MHGO is proved to be able to provide a state estimation with smaller peaks in comparison to a single HGO. The performance of MHGO in the presence of measurement noise is also investigated. We consider existence of abrupt external disturbances as well. To alleviate the effects of those disturbances and attenuate their resulting peaking, we present a resetting scheme. Moreover, the output feedback control problem is considered, and it is demonstrated that a separation principle is valid for MHGO. Finally, simulation results illustrate that MHGO provides an accurate state estimation, and MHGO-based controller is able to recover the performance of state feedback controller.

Mehran Shakarami, Amir Abolfazl Suratgar, Heidar Ali Talebi

IEEE Transactions on Control Systems Technology

2018

Intraoperative brain shift decreases the accuracy of neuronavigation systems based on preoperative images. In this paper, this problem is addressed by calculating an estimation of brain shift which can be employed to update the preoperative brain images. Therefore, the precision of navigation can be improved. In this regard, a brain shift estimation method is proposed using an atlas of brain deformations and constrained Kalman filter (ACKF). In addition, it is proven that the obtained ACKF estimation is the best unbiased minimax estimation when the risk function is the estimation error variance. Furthermore, a comparison is performed between the ACKF and two existing methods, namely, CKF and atlas-based method. The comparison demonstrates that the ACKF results in a more accurate estimation and needs less computation time. Finally, the supremacy of the proposed ACKF method with respect to the CKF and atlas-based method is illustrated through simulation.