Farnaz Adib Yaghmaie- Doctor of Philosophy
- Nanyang Technological University
Farnaz Adib Yaghmaie
- Doctor of Philosophy
- Nanyang Technological University
About
24
Publications
4,676
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522
Citations
Introduction
Current institution
Additional affiliations
September 2011 - September 2012
Education
August 2013 - April 2017
September 2009 - September 2011
September 2005 - September 2009
Publications
Publications (24)
This paper bridges optimization and control, and presents a novel closed-loop control framework based on natural gradient descent, offering a trajectory-oriented alternative to traditional cost-function tuning. By leveraging the Fisher Information Matrix, we formulate a preconditioned gradient descent update that explicitly shapes system trajectori...
This paper presents online prior-knowledge-based data-driven approaches for verifying stability and learning a stabilizing dynamic controller for linear stochastic input-output systems. The system is modeled in an autoregressive exogenous (ARX) framework to accommodate cases where states are not fully observable. A key challenge addressed in this a...
This paper presents an online learning mechanism to address the challenge of state tracking for unknown linear systems under general adversarial disturbances. The reference trajectory is assumed to be generated by unknown exosystem dynamics, which relaxes the common assumption of known dynamics for exosystems. Learning a tracking control policy for...
This paper presents a memory-augmented control solution for the optimal reference tracking problem for linear systems subject to adversarial disturbances. We assume that the dynamics of the linear system are known and that the reference signal is generated by a linear system with unknown dynamics. Under these assumptions, finding the optimal tracki...
In this article, we consider linear quadratic (LQ) control problem with process and measurement noises. We analyze the LQ problem in terms of the average cost and the structure of the value function. We assume that the dynamics of the linear system is unknown and only noisy measurements of the state variable are available. Using noisy measurements...
The emerging field of Reinforcement Learning (RL) has led to impressive results in varied domains like strategy games, robotics, etc. This handout aims to give a simple introduction to RL from control perspective and discuss three possible approaches to solve an RL problem: Policy Gradient, Policy Iteration, and Model-building. Dynamical systems mi...
In this paper, we introduce an optimal average cost learning framework to solve output regulation problem for linear systems with unknown dynamics. Our optimal framework aims to design the controller to achieve output tracking and disturbance rejection while minimizing the average cost. We derive the Hamilton–Jacobi–Bellman (HJB) equation for the o...
Differential graphical games have been introduced in the literature to solve state synchronization problem for linear homogeneous agents. When the agents are heterogeneous, the previous notion of graphical games cannot be used anymore and a new definition is required. In this paper, we define a novel concept of differential graphical games for line...
In this paper, we identify a class of input constrained optimal control problems which can be approximately solved using Reinforcement Learning (RL) approaches. We start with a general class of problems which do not admit the theoretical assumptions used to derive RL frameworks. We then restrict this class by extra conditions on the dynamics and th...
In this paper we analyze H∞ output regulation of linear heterogeneous multi-agent systems. The agents are subject to modeled and unmodeled disturbances and communicate over a switching graph. We derive a sufficient condition that guarantees H∞ output regulation for the mentioned setup. This sufficient condition places requirements on both the singl...
In this paper, we investigate the multi-party output consensus of linear heterogeneous agents, in which the outputs of the agents within a party synchronize to each other, but different parties have different synchronized trajectories. To solve this problem, graphs with complex edge weights are used. We provide a sufficient condition for leader-fol...
This paper investigates cooperative output synchronization and bipartite output synchronization of a group of linear heterogeneous agents in a unified framework. For a structurally balanced signed graph, we prove that the bipartite output synchronization is equivalent to the cooperative output synchronization over an unsigned graph whose adjacency...
This paper investigates output H_\infty synchronization of a group of linear heterogeneous agents. Agents are
subject to external unmodeled disturbances and distributively communicate one set of outputs. The aim is to design a distributed controller to achieve synchronization on another set of outputs with an H_\infty bound. We obtain an H_\infty-c...
In this paper we investigate bipartite output synchronization of a group of linear heterogeneous agents communicating their outputs over a signed graph. Our multi-agent system contains N followers and one leader and we aim to achieve bipartite output synchronization to the output of the leader. By the bipartite output synchronization, we mean outpu...
In this paper, we propose output feedback and state feedback controllers for output regulation of linear heterogeneous multi-agent systems. We use a general model for the output regulation problem of linear multi-agent systems which accounts for a general directed and fixed communication graph, heterogeneity of the agents, an arbitrary number of in...
In this paper, we propose output feedback controller for leader-follower output consensus of linear heterogeneous multi-agent systems. We derive a global sufficient condition for the existence of an output feedback and we show that the global condition shrinks to an H∞ criterion of a local system. We indicate that the H∞ criterion is a local suffic...
This paper addresses output regulation of heterogeneous linear multi-agent systems. We first show that output regulation can be achieved through local controller design, then we formulate output regulation in the graphical game framework. To solve output regulation of heterogeneous linear multi-agent system in the graphical game framework, one need...
This paper addresses the output regulation of the heterogeneous multi agent system. First, it is shown that the
output regulation can be achieved through the local controller design, then the output regulation is formulated in the graphical game framework. To solve the output regulation of a heterogeneous multi agent system in the graphical game fr...
In this paper, a new method for navigation in dynamic environment is suggested. In this method named Potential Ban (PB), it is assumed that the robot performs SLAM and autonomous navigation in dynamic environment. The robot uses PB algorithm for dynamic obstacle avoidance and its navigation trough static and dynamic objects is done without any pred...
This paper addresses a new method for navigation in dynamic environment. The proposed method is based on force field method and it is supposed that the robot performs SLAM and autonomous navigation in dynamic environment without any predefined information about dynamic obstacles. The movement of dynamic obstacles is predicted by Kalman filter and i...
In this paper a new controller for nonholonomic system is introduced. This feedback error learning controller benefits from both nonlinear and adaptive controller properties. The nonlinear controller is used to stabilize the nonholonomic behavior of the systems. This controller is a sliding mode controller which is designed based on backstepping me...
Questions
Question (1)
Suppose that the stability of a closed loop system is guaranteed by Spectral radius stability condition. We know that the by Spectral radius stability condition is less conservative than Small gain theorem. Hence, the stability obtained by Spectral radius stability condition may or may not be sufficient in Small gain theorem. In this case, we are sure about L2 gain stability (as the closed loop system is stable) but how we can obtain the L2 gain of the closed loop system? Since our stability condition may not be satisfied in the small gain theorem by L2 norm.
