Professor Scott Bortoff
United Technologies Research Center
(on sabbatical leave from the University of Toronto)
Control systems based on real-time on-line optimization,
such as Model Predictive Control (MPC), can provide several advantages
over other model-based methods. Perhaps the most important of these
is that constraints on the control, state and output can be
incorporated directly into the design. Constraints are often the
dominant characteristic in an industrial control problem. i
This talk will focus on two applications of OBC on United Technologies
products. First, we will study the problem of path planning for an
Unmanned Air Vehicle (UAV), such as the Sikorsky Cipher. We will
frame this as an optimal control problem which is constrained by the
UAV dynamics and control rates and magnitudes. The objective function
is a trade-off between flight distance to a known target, and radar
reflectivity to enemy radar sites (stealth). This problem can be
solved in closed form for some simple cases. The UAV path planning
problem will also be posed and solved using potential field theory.
The second application of OBC is for gas turbine engines.
This problem is dominated by constraints on the state and inputs,
and optimization is important for purposes of efficiency and
wear reduction.