Decentralized adaptive control of uncertain systems with gain scheduled reference models

Control theoretic concepts for decentralized adaptive control of uncertain systems with gain scheduled reference model is developed. For each subsystem a single Lyapunov matrix is computed, using convex optimization tools, for multiple linearizations near equilibrium and non-equilibrium points of the nonlinear closed-loop gain scheduled reference subsystem. This approach guarantees stability of the closed-loop gain scheduled reference model. Then, decentralized adaptive state feedback control architecture is developed and its stability is proved. Specifically, the resulting closed-loop system is shown to have bounded solutions with bounded tracking error for all the subsystems. Simulation results for two different models of a turboshaft engine, including the nominal engine model and an engine model with a new core subsystem, illustrate the possibility of stable decentralized adaptive control of gas turbine engines with proper tracking performance.