Daniel E. Viassolo

Model Adaptation and Nonlinear Model Predictive Control of an Aircraft Engine

The performance improvement of constrained nonlinear model predictive control (NMPC) with state and parameter estimation over traditional control architectures is investigated and applied to a model turbofan aircraft engine. Strong nonlinearities are present in turbofan aircraft engines due to the large range of operating conditions and power levels experienced during a typical mission. Also, turbine operation is restricted due to mechanical, aerodynamic, thermal, and flow limitations. Current control methodologies rely strictly on a priori information; therefore they fail to utilize current engine state or health information for reducing conservatism and improving engine performance. NMPC is selected because it depends on a model that can be adapted to the current engine conditions, it can explicitly handle the nonlinearities, both input and output constraints of many variables, and determine the optimal control that will meet the requirements for any engine condition all in a single control formulation. A physics based component level model is developed as the heart of the architecture. The state or health of the engine is determined using a joint state and parameter estimator utilizing extended Kalman filter (EKF) techniques. With the necessary engine information in hand, a constrained NMPC is used to determine the optimal actuator commands. Results regarding steady state performance improvements are presented.Copyright

Towards In-Flight Detection and Accommodation of Faults in Aircraft Engines

To effectively accommodate safety-critical faults in-flight it is necessary to rapidly detect them and to have a means to accommodate the fault. We present results on model-based fault detection using sensor residuals from an extended Kalman filter with an embedded real-time engine model to characterize un-faulted behavior over the flight envelope. Thereafter, we present an approach for online fault accommodation via optimal changes in a set of suitable adjustments in the existing FADEC control logic. These optimal adjustments are obtained through off-line optimization for recovery of stall margins and thrust, then interpolated online for the existing flight conditions. We present results of the fault detection & accommodation applied to a high-bypass commercial aircraft engine over the flight envelope.

Advanced Estimation for Aircraft Engines

This paper reviews recent industrial applications of estimation techniques to aircraft engines. Estimation is considered here in a broad sense. An estimator is any algorithm that processes engine measurements to compute engine signals, or parameters of interest, that are not directly measured. The paper begins with a summary of aircraft engines fundamentals, followed by brief descriptions of basic concepts in the areas of engine controls, engine fault detection and isolation, and engine health management. Practical estimation-based approaches in these areas are covered in some detail. These approaches include: control schemes that regulate estimated thrust, fault detection and classification algorithms based on estimator residuals, and health monitoring procedures that track deterioration of key engine model parameters. Several case studies, based on recent programs at GE Global Research and GE Aviation, are described in detail. These case studies contain validations of the proposed solutions via implementations using high-fidelity engine models. The paper concludes with a review of some open problems. This paper intends to be a starting point for researchers in academia and industry looking for an overview of estimation methods used in industrial jet engine applications.

Advanced Controls for Fuel Consumption and Time-on-Wing Optimization in Commercial Aircraft Engines

This paper introduces an architecture that improves the existing interface between flight control and engine control. The architecture is based on an on-board dynamic engine model, and advanced control and estimation techniques. It utilizes a Tracking Filter (TF) to estimate model parameters and thus allow a nominal model to match any given engine. The TF is combined with an Extended Kalman Filter (EKF) to estimate unmeasured engine states and performance outputs, such as engine thrust and turbine temperatures. These estimated outputs are then used by a Model Predictive Control (MPC), which optimizes engine performance subject to operability constraints. MPC objective and constraints are based on the aircraft operation mode. For steady-state operation, the MPC objective is to minimize fuel consumption. For transient operation, such as idle-to-takeoff, the MPC goal is to track a thrust demand profile, while minimizing turbine temperatures for extended engine time-on-wing. Simulations at different steady-state conditions over the flight envelope show important fuel savings with respect to current control technology. Simulations for a set of usual transient show that the TF/EKF/MPC combination can track a desired transient thrust profile and achieve significant reductions in peak and steady-state turbine gas and metal. These temperature reductions contribute heavily to extend the engine time-on-wing. Results for both steady state and transient operation modes are shown to be robust with respect to engine-engine variability, engine deterioration, and flight envelope operating point conditions. The approach proposed provides a natural framework for optimal accommodation of engine faults through integration with fault detection algorithms followed by update of the engine model and optimization constraints consistent with the fault. This is a potential future work direction.Copyright

Robust estimation algorithm for spectral Neugebauer models

Colorimetric models are used to predict printer output colors from a set of toner control values. The so-called Neugebauer model predicts the spectral reflectance of the printed colors. This paper presents an algorithm for estimating the parameters of the Neugebauer model from a set of measured reflectances. The algorithm requires a bound on the magnitude of the output reflectance errors. A case study using a high-end color printer suggests that this algorithm yields robust spectral models, which are less sensitive (to undesirable variations in the data) than the models obtained with the methods of least squares and total least squares.