Optimally scaled H-infinity full information control synthesis with real uncertainty

Abstract

An algorithm to synthesize optimal controllers for the scaled Tioo 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 1-C^ 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.