Robust and reliable control via quadratic Lyapunov functions

In this dissertation we present a new approach to design robust and reliable controllers. Our results are used to find control laws for systems that are subject to (1) real polytopic and norm bounded uncertainties, (2) actuator and sensor variations and (3) actuator and sensor failure. In addition, we present conditions that can be added to the control design problem to constrain the controller to be stable or strictly positive real, further strengthening the robustness and reliability of the control design.;The basic framework relies on the use of quadratic Lyapunov functions to accommodate potentially time varying uncertainty. Conditions are derived that, when satisfied, allow a robust control design to be obtained by performing two convex optimizations. These controllers recover the performance robustness of either state feedback or full information controllers. Sufficient conditions are presented that remove the non-convexity in terms of the control design variables. This allows a robust control design to be obtained by solving a set of linear matrix inequalities.;These general robustness results are then applied to the reliability problem. Actuator and sensor variations are modeled using real polytopic uncertainties. It is shown that under some simplifying assumptions the state feedback problem reduces to a single linear matrix inequality. It also shows that the Riccati equations for standard LQR and ;Additionally, when applicable, stronger reliability guaranties may be obtained by constraining the controller to be strictly positive real. This guarantees stability for positive real plants with possibly large plant variations and additive positive real unmodeled dynamics. We develop an LMI condition that can be added to the solvability conditions for robist and reliable control, to include a constraint on the controller to be stable or strictly positive real.