Book Description

This dissertation investigates the problem of developing verifiable stable control architectures for gas turbine engines. First, a nonlinear physics-based dynamic model of a twin spool turboshaft engine which drives a variable pitch propeller is developed. In this model, the dynamics of the engine are defined to be the two spool speeds, and the two control inputs to the system are fuel flow rate and prop pitch angle. Experimental results are used to verify the dynamic model of JetCat SPT5 turboshaft engine. Based on the experimental data, performance maps of the engine components including propeller, high pressure compressor, high pressure, and low pressure turbines are constructed. The engine numerical model is implemented using Matlab. Second, a stable gain scheduled controller is described and developed for a gas turbine engine that drives a variable pitch propeller. A stability proof is developed for a gain scheduled closed-loop system using global linearization and linear matrix inequality (LMI) techniques. Using convex optimization tools, a single quadratic Lyapunov function is computed for multiple linearizations near equilibrium and non-equilibrium points of the nonlinear closed-loop system. This approach guarantees stability of the closed-loop gas turbine engine system. To verify the stability of the closed-loop system on-line, an optimization problem is proposed which is solvable using convex optimization tools. Through simulations, we show the developed gain scheduled controller is capable to regulate a turboshaft engine for large thrust commands in a stable fashion with proper tracking performance. Third, a gain scheduled model reference adaptive control (GS-MRAC) concept for multi-input multi-output (MIMO) nonlinear plants with constraints on the control inputs is developed and described. Specifically, adaptive state feedback for the output tracking control problem of MIMO nonlinear systems is studied. Gain scheduled reference model system is used for generating desired state trajectories, and the stability of this reference model is also analyzed using convex optimization tools. This approach guarantees stability of the closed-loop gain scheduled gas turbine engine system, which is used as a gain scheduled reference model. An adaptive state feedback control scheme is developed and its stability is proven, in addition to transient and steady-state performance guarantees. The resulting closed-loop system is shown to have ultimately bounded solutions with a priori adjustable bounded tracking error. The results are then extended to GS-MRAC with constraints on the magnitudes of multiple control inputs. Sufficient conditions for uniform boundedness of the closed-loop system is derived. A semi-global stability result is proven with respect to the level of saturation for open-loop unstable plants, while the stability result is shown to be global for open-loop stable plants. Simulations are performed for three different models of the turboshaft engine, including the nominal engine model and two models where the engine is degraded. Through simulations, we show the developed GS-MRAC architecture can be used for the tracking problem of degraded turboshaft engine for large thrust commands with guaranteed stability. Finally, a decentralized linear parameter dependent representation of the engine model is developed, suitable for decentralized control of the engine with core and fan/prop subsystems. Control theoretic concepts for decentralized gain scheduled model reference adaptive control (D-GS-MRAC) systems is developed. For each subsystem, a linear parameter dependent model is available and a common Lyapunov matrix can be computed using convex optimization tools. With this control architecture, the two subsystems of the engine (i.e., engine core and engine prop/fan) can be controlled with independent controllers for large throttle commands in a decentralized manner. Based on this D-GS-MRAC architecture, a "plug and play" (PnP) technology concept for gas turbine engine control systems is investigated, which allows us to match different engine cores with different engine fans/propellers. With this plug and play engine control architecture, engine cores and fans/props could be used with their on-board subordinate controllers ready for integration into a functional propulsion system. Simulation results for three different models of the engine, including the nominal engine model, the model with a new prop, and the model with a new engine core, illustrate the possibility of PnP technology development for gas turbine engine control systems.