Milan Mandic

Decentralized observer with a consensus filter for distributed discrete-time linear systems

Abstract This paper presents a decentralized observer with a consensus filter for the state observation of discrete-time linear distributed systems. Each agent in the distributed system has an observer with a model of the plant that utilizes the set of locally available measurements, which may not make the full plant state detectable. This lack of detectability is overcome by utilizing a consensus filter that blends the state estimate of each agent with its neighbors’ estimates. It is proven that the state estimates of the proposed observer exponentially converge to the actual plant states under arbitrarily changing, but connected, communication and pseudo-connected sensing graph topologies. Except these connectivity properties, full knowledge of the sensing and communication graphs is not needed at the design time. As a byproduct, we obtained a result on the location of eigenvalues, i.e., the spectrum, of the Laplacian for a family of graphs with self-loops.

Efficient sensor coverage for acoustic localization

In this paper we consider a network of mobile agents carrying a simple sensor, able to measure the time an impulsive signal propagating isotropically from an unknown point source is detected, with a probability depending on the distance between the signal source and the sensor. Given an a priori probability distribution for the location of the source, we address the problem of controlling the motion of the agents, in such a way that the quality of the localization of the signal source is maximized, assuming an ideal estimation process. The performance criterion is the expected value of the determinant of the Fisher Information Matrix, as computed from the location of the sensors that detect the signal. We develop a gradient control law, ensuring convergence of the agents to a critical point with respect to the stated performance. An application to acoustic detection and localization of sniper Are is discussed and simulation results are presented.

Guidance and Control of Formation Flying Spacecraft

A key element of NASA’s future space exploration is high precision formation flying (FF) for space interferometry. Precision FF has never been attempted before and poses new and significant challenges to the underlying control system. While the guidance and control (G&C) methodologies of single spacecraft for traditional planetary flyby and orbiter missions are well-understood, the G&C of FF missions is fundamentally different. The FF systems require new control systems, architectures, and greater levels of autonomy to meet expected precision performance in the presence of environmental disturbances, plant uncertainties and more complex system interactions. This chapter will trace the motivation for these changes and will layout approaches taken to meet the new challenges. Fred Y. Hadaegh Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, e-mail: Fred.Y.Hadaegh@jpl.nasa.gov Gurkirpal Singh Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, e-mail: Gurkipal.Singh@jpl.nasa.gov Behcet Acikmese Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, e-mail: Behcet.Acikmese@jpl.nasa.gov Daniel P. Scharf Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, e-mail: Daniel.P.Scharf@jpl.nasa.gov Milan Mandic Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, e-mail: Milan.Mandic@jpl.nasa.gov

Guidance and Control for a Mars Helicopter

As part of a future mission to Mars, NASA is considering including a small helicopter capable of operating independently in the Martian environment. The Martian atmosphere is extremely thin, with a density of only 1–2% of Earth’s atmospheric density at sea level; this significantly alters the flight dynamics of the vehicle and has implications for vehicle design and control. In this paper we focus on guidance and control for aMars Helicopter, and in particular on the challenges that are unique to operating in the Mars environment. In 2016, the first-ever controlled flight of a helicopter in Martian atmospheric conditions was performed in the 25-ft Space Simulator at NASA’s Jet Propulsion Laboratory. We provide details of the effort leading to this flight demonstration, including modeling, simulation, system identification, guidance, and control.