Distributed Model-based Control for Gas Turbine Engines

Controlling a gas turbine engine is a fascinating problem. As one of the most complex systems developed, it relies on thermodynamics, fluid mechanics, materials science as well as electrical, control and systems engineering. The evolution of gas turbine engines is marked with an increase in the number of actuators. Naturally, this increase in actuation capability has also been followed by the improvement of other technologies such as advanced high-temperature and lighter materials, improving the efficiency of the aero engines by extending their physical limits. An improvement in the way to control the engine has to be undertaken in order for these technological improvements to be fully harnessed. This starts with the selection of a novel control system architecture and is followed by the design of new control techniques. Model-based control methods relying on distributed architectures have been studied in the past for their ability to handle constraints and to provide optimal control strategies. Applying them to gas turbine engines is interesting for three main reasons. First of all, distributed control architectures provide greater modularity during the design than centralized control architectures. Secondly, they can reduce the life cycle costs linked to both the fuel burnt and the maintenance by bringing optimal control decisions. Finally, distributing the control actions can increase flight safety through improved robustness as well as fault tolerance. This thesis is concerned with the optimal selection of a distributed control system architecture that minimizes the number of subsystem to subsystem interactions. The control system architecture problem is formulated as a binary integer linear programming problem where cuts are added to remove the uncontrollable partitions obtained. Then a supervised-distributed control technique is presented whereby a supervisory agent optimizes the joint communication and system performance metrics periodically. This online optimal technique is cast as a semi-definite programming problem including a bilinear matrix equality and solved using an alternate convex search. Finally, an extension of this online optimal control technique is presented for non-linear systems modelled by linear parameter-varying models

Model predictive control for linear systems: adaptive, distributed and switching implementations

Thanks to substantial past and recent developments, model predictive control has become one of the most relevant advanced control techniques. Nevertheless, many challenges associated to the reliance of MPC on a mathematical model that accurately depicts the controlled process still exist. This thesis is concerned with three of these challenges, placing the focus on constructing mathematically sound MPC controllers that are comparable in complexity to standard MPC implementations. The first part of this thesis tackles the challenge of model uncertainty in time-varying plants. A new dual MPC controller is devised to robustly control the system in presence of parametric uncertainty and simultaneously identify more accurate representations of the plant while in operation. The main feature of the proposed dual controller is the partition of the input, in order to decouple both objectives. Standard robust MPC concepts are combined with a persistence of excitation approach that guarantees the closed-loop data is informative enough to provide accurate estimates. Finally, the adequacy of the estimates for updating the MPC's prediction model is discussed. The second part of this thesis tackles a specific type of time-varying plant usually referred to as switching systems. A new approach to the computation of dwell-times that guarantee admissible and stable switching between mode-specific MPC controllers is proposed. The approach is computationally tractable, even for large scale systems, and relies on the well-known exponential stability result available for standard MPC controllers. The last part of this thesis tackles the challenge of MPC for large-scale networks composed by several subsystems that experience dynamical coupling. In particular, the approach devised in this thesis is non-cooperative, and does not rely on arbitrarily chosen parameters, or centralized initializations. The result is a distributed control algorithm that requires one step of communication between neighbouring subsystems at each sampling time, in order to properly account for the interaction, and provide admissible and stabilizing control