Optimally Scaled H Infinity Full Information Control Synthesis with Real Uncertainty

Reprinted with permission of the American Institute of Aeronautics and Astronautics, Inc. See http://www.aiaa.org/content.cfm?pageid=2 for more information.An algorithm to synthesize optimal controllers for the scaled H Infinity full information problem with real and complex uncertainty is presented. The control problem is reduced to a linear matrix inequality, which can be solved via a finite dimensional convex optimization. This technique is compared with the optimal scaled H Infinity full information with only complex uncertainty and D-K iteration control design to synthesize controllers for a missile autopilot. Directly including real parametric uncertainty into the control design results in improved robust performance of the missile autopilot. The controller synthesized via D-K iteration achieves results similar to the optimal designs