Guaranteed Margins for LQR Regulators

John Doyle's famous paper! He presents a counterexample that shows that are no guaranteed margins for LQG systems. 

Perspectives on Control-Relevant Identification Through the Use of Interacti...

This paper presents a control-relevant identification methodology through an intuitive interactive tool called "Interactive Tool for Control Relevant Identification (ITCRI)". ITCRI...

A Tutorial on PES Pareto Methods for Analysis of Noise Propagation in Feedba...

This paper represents a tutorial on the so called PES Pareto methodology of analyzing the sources of noise in a feedback loop. Originally conceived for analyzing noise contributors in...

Manuscript about ITISE: an Interactive Software Tool for System Identificati...

The paper describes the conceptual basis, main features and functionality of an interactive software tool developed in support of system identification education and discovery.

This...

Vibrational control of nonlinear systems: Vibrational controllability and tr...

In the first part of this work, the criteria for the existence of stabilizing parametric oscillations have been derived. In the present paper, the problem of choosing the stabilizing...

Feedforward tuning rules for measurable disturbances with PID control: a tut...

Feedforward control can be considered as the most well-known control approach to deal with measurable disturbances. It started to be used almost 100 years ago, and since then it is being...

Robotarium by Georgia Tech

The Robotarium project provides a remotely accessible swarm robotics research platform that remains freely accessible to anyone. Currently, Robotics research requires significant investments...

Averaging and Vibrational Control of Mechanical Systems

Abstract. This paper investigates averaging theory and oscillatory control for a large class of mechanical systems. A link between averaging and controllability theory is presented by...

Dealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learning

From the abstract

Recent developments in deep reinforcement learning are concerned with creating decision-making agents which can perform well in various complex domains. A particular...

Multi-agent reinforcement learning: An overview

From the abstract:

Multi-agent systems can be used to address problems in a variety of do- mains, including robotics, distributed control, telecommunications, and economics. The complexity...

Multifunction Phased Array Radar (MPAR) for Aircraft and Weather Surveillanc...

MIT Lincoln Laboratory and M/A-COM are jointly conducting a technology demonstration of affordable Multifunction Phased Array Radar (MPAR) technology for Next Generation air traffic control...

The Demod Squad: A Tutorial on the Utility and Methodologies for Using Modul...

This paper is a brief tutorial on methods for using modulated signals in feedback loops, and especially of the different methods and trade offs used for demodulating those signals to get...

Smart Projectile State Estimation Using Evidence Theory

This journal article provides a very good practical understanding of Dempster-Shafer theory using sensor fusion and state estimation as the backdrop.