Research Groups

Highly complex and networked systems are being introduced into all levels of our global infrastructure. They provide the basis for many application domains including automated transportation networks, distributed power generation, and formation-flying satellites. The study of these systems, however, has yet to be formalized as a proper engineering discipline. A fundamental goal of work done within the Cooperative Networks and Controls Lab, therefore, is to contribute to the development of the network sciences and engineering as a formal engineering science. Towards this goal, our research focuses on the complimentary problems of analysis and design of networked and multi-agent systems. Our scientific approach is to explore how the mathematical field of graph theory can interface with dynamic systems and control theory in the study of these systems. We are currently focused on three core projects: i) analysis and design of networked systems, ii) formation control and multi-robot coordination, and iii) distributed power generation and the smart-grid. We focus on fundamental theory while also exploring implementation challenges on a multi-robot testbed as a demonstrator.

Currently, my research efforts and contributions are concentrated in two main areas:
(a) estimation and control of stochastic systems with non-Gaussian noises, and (b) low cost control and estimation systems for remotely piloted vehicles. Specifically, I work on robust, adaptive and nonlinear control of aerospace systems, for example, control of highly flexible and over-actuated aircraft, control of a team of multi rotors for slung load transportation, terrain following and more.

My main field of research is Optimal Control Theory essentially from the Calculus of Variations point of view, following the path of my teacher H. J. Kelley. Thus gradient methods, adjoint analysis, singular perturbations, singular control, etc. are all topics in my research works. Modern direct optimization tools are also employed for this purpose in mine and my students’ work, such as collocation and pseudo-spectral methods.

The applications of the theory to real aerospace problems have been at the center of my attention. It includes some major problems in atmospheric flight (e.g. minimum time to fly between points; minimum time to climb)), in rocket performance (Goddard problem, maximal rocket range), in missile guidance (Proportional Navigation, Linear Quadratic Pursuit-Evasion games) and more.

The main threats of the country are ballistic missiles (especially with nuclear warheads) and rockets. The challenges are to provide nearly hermetic defense against the former and cost effective defense against the later. Most of my graduate students come from the aerospace industry (Rafael, Elbit, IMI and IAI) and are involved in this national effort. We direct our research work toward solving problems arising in their industrial activities. Some of our research results have been incorporated in new missile defense systems.

Information Fusion, Optimal Estimation and Control, applications to Aerospace Systems.

Particular interests: Guidance, Navigation and Control (GN&C) systems; interdisciplinary aerospace systems: structural estimation and control, health monitoring/Fault Detection and Isolation (FDI) systems; Flow Control.

My current research includes three main directions: (1) Autonomous vehicles operating in urban environments: applying existing algorithms and developing new ones to solve currently open problems of autonomous driving. (2) Human motor learning theoretical and experimental investigation from control engineering perspective: the goal is verifying and extending a number of novel hypotheses related to human motor equivalence (also known as Bernstein’s Degrees-of-Freedom problem). This research includes modeling of various challenging sports and activities, tracking and analyzing human movements while performing these activities, and developing mathematical tools to gain a thorough insight into the skill acquisition process, sports technique and performance, and interaction between the body and environment. (3) Human-Machine Interaction: training motor and perception skills in Virtual Reality simulators and by the means of Augmented Reality aids. The novelty is in the computation of visual cues displayed to trainees: the cues are extracted from an autonomous system acting beside the trainee in real time and trying to achieve the same task. Thus, the focus is on designing control systems with human-in-the-loop, and investigating perception-action coupling.