What is Control Engineering?
The science that studies how to manipulate the parameters affecting the behavior of a system to produce a desired or optimal outcome. The tools that form the body of knowledge usually known as 'control theory' are applicable to a very large classes of systems that include electrical, mechanical, chemical, biological systems, economical, and social systems.
One of the key premises upon which the CCDC was created is that a successful MS and PhD program must have a solid theoretical component that is highly integrated with practical applications. Consistent with this vision, the research areas at the center strike a balance between the development of theoretical tool for control and computation, and the areas where these tools find practical application.
|1. Control of Nonlinear Systems
One of main challenges in control arises when the system, whose behavior one is trying to control, is highly nonlinear. Causes for nonlinearity include inherently nonlinear physical laws, limitations on actuators and sensors, faults, etc. In fact, nonlinearity is the rule, rather than the exception. The members of the CCDC center have significant expertise in the analysis and control of systems with nonlinearities that dominate the behavior of the system and for which linear control tools are not applicable.
Teel, Kokotovic, Mezic, Moehlis
2. Adaptive and Robust Control for Systems with Large Uncertainty
Kokotovic, Hespanha, Smith, Bamieh, Khammash
3. Control of Multi-Agent Distributed Systems
This area focuses on the design of control algorithms for groups of autonomous and semi-autonomous vehicles in ground, aerospace and naval environments. In this context, the technological problem is the design of algorithms that will coordinate the group of vehicles while performing spatially-distributed tasks. Example desirable tasks include data gathering, coverage, surveillance, exploration, target detection, and search. These tasks will be performed by robotic sensor networks that will adapt to changing environments and dynamic situations, while relying on ad-hoc communication links (with limited-bandwidth and latency). To achieve these desirable capabilities, the research objective is the design of multi-vehicle coordination algorithms that are scalable, asynchronous, and adaptive. The technical approach relies on a broad approach to distributed and parallel algorithms for ad hoc networks with controlled mobility.
Coordinated control: Hespanha, Smith
4. Numerical Methods in Systems and Control
Numerical linear algebra: Chandrasekaran
5. Logic-Based and Hybrid Control
As computers, digital networks, and embedded systems become ubiquitous and increasingly complex, one needs to understand the coupling between logic-based components and continuous physical systems. This prompted a shift in the standard control paradigm in which dynamical systems were typically described by differential or difference equations to allow the modeling, analysis, and design of systems that combine continuous dynamics with discrete logic. This new paradigm is often called 'hybrid control.
6. Dynamical Systems
1. Engine Control
Kokotovic, Teel, Roy
2. Control of High-Precision/Micro-Mechanical Systems
Micro and nano-devices (MEMS): Bamieh, Turner
3. Control of Chemical Processes
Real-time digital control: Mellichamp
4. Interactive Robotic Systems
Mobile robotics: Hespanha, Paden
Control of Haptic devices: Hespanha
Machine vision in robotics: Manjunath, Hespanha
5. Biological Systems
Doyle, Khammash, Moehlis
6. Space and Aerospace Systems
7. Control and Analysis of Turbulent and Shear Flows
Bamieh, Mezic, Moehlis
8. Communication Network