Abstract: Systems and methods for controlling a fluid based engineering system are disclosed. The systems and methods may include a model processor for generating a model output, the model processor including a set state module for setting dynamic states of the model processor, the dynamic states input to an open loop model based on the model operating mode. The model processor may further include an estimate state module for determining an estimated state of the model based on a prior state model output and the current state model of the open loop model the estimate state module using online linearization and gain calculation to determine estimator gain for determining the estimated state of the model.

Abstract: A control system for a gas turbine engine, a method for controlling a gas turbine engine, and a gas turbine engine are disclosed. The control system may include a hybrid model predictive control (HMPC) module, the HMPC module receiving power goals and operability limits and determining a multi-variable control command for the gas turbine engine, the multi-variable control command determined using the power goals, the operability limits, actuator goals, sensor signals, and synthesis signals. The control system may further include system sensors for determining the sensor signals and a non-linear engine model for estimating corrected speed signals and synthesis signals using the sensor signals, the synthesis signals including an estimated stall margin remaining. The control system may further include a goal generation module for determining actuator goals for the HMPC module using the corrected speed signals and an actuator for controlling the gas turbine engine based on the multivariable control command.

Abstract: A control system for a gas turbine engine, a method for controlling a gas turbine engine, and a gas turbine engine are disclosed. The control system may include a hybrid model predictive control (HMPC) module, the HMPC module receiving power goals and operability limits and determining a multi-variable control command for the gas turbine engine, the multi-variable control command determined using the power goals, the operability limits, actuator goals, sensor signals, and synthesis signals. The control system may further include system sensors for determining the sensor signals and a non-linear engine model for estimating corrected speed signals and synthesis signals using the sensor signals, the synthesis signals including an estimated stall margin remaining. The control system may further include a goal generation module for determining actuator goals for the HMPC module using the corrected speed signals and an actuator for controlling the gas turbine engine based on the multivariable control command.

Abstract: A control system for a gas turbine engine is disclosed. The control system may include a computer processor. The control system may also include an outer loop control module programmed into the computer processor to determine a torque request based at least in part on a real-time collective lever angle command. The control system may also include an inner loop control module programmed into the computer processor to receive the torque request from the outer loop control module, to determine fuel flow and inlet guide vane schedules based at least in part on the received torque request, and to send signals to a gas generator of the gas turbine engine in order to control the gas generator according to the determined fuel flow and inlet guide vane schedules.

Abstract: A method for model-based control of a gas turbine engine is disclosed. An operating point of the gas turbine engine is generated from measured parameters using a component-level model. The component-level model is analytically linearized by taking the first partial derivative of output parameters of each component with respect to input parameters of each component, and evaluating the result at the operating point. Components of the linearized component-level model are combined to form a combined perturbational model of the gas turbine engine, which is inverted to solve for control commands as a function of target parameters and measured parameters.

Abstract: A controller for a power generating system that includes an engine and a generator, wherein the engine provides mechanical force to the generator, which converts the mechanical force to electrical energy that is distributed via a distribution network. The controller includes a complementary filter that applies a low-frequency response to changes in the monitored power output and a high-frequency response to changes in the monitored grid frequency. The complementary filter combines outputs of the high-frequency response and low-frequency response to generate a process variable. A feedback controller generates a fuel flow value in response to the process variable.

Abstract: A gas turbine engine inlet sensor fault detection and accommodation system comprises an engine model, an engine parameter comparison block, an inlet condition estimator, control laws, and a fault detection and accommodation system. The engine model is configured to produce a real-time model-based estimate of engine parameters. The engine parameter comparison block is configured to produce residuals indicating the difference between the real-time model-based estimate of engine parameters and sensed values of the engine parameters. The inlet condition estimator is configured to iteratively adjust an estimate of inlet conditions based on the residuals. The control laws are configured to produce engine control parameters for control of gas turbine engine actuators based on the inlet conditions.

Abstract: A gas turbine engine inlet sensor fault detection and accommodation system comprises an engine model, an engine parameter comparison block, an inlet condition estimator, control laws, and a fault detection and accommodation system. The engine model is configured to produce a real-time model-based estimate of engine parameters. The engine parameter comparison block is configured to produce residuals indicating the difference between the real-time model-based estimate of engine parameters and sensed values of the engine parameters. The inlet condition estimator is configured to iteratively adjust an estimate of inlet conditions based on the residuals. The control laws are configured to produce engine control parameters for control of gas turbine engine actuators based on the inlet conditions.

Abstract: A gas turbine engine comprises a compressor, a combustor, a turbine, and an electronic engine control system. The compressor, combustor, and turbine are arranged in flow series. The electronic engine control system is configured to generate a real-time estimate of compressor stall margin from an engine model, and command engine actuators to correct for the difference between the real time estimate of compressor stall margin and a required stall margin.

Abstract: A method and system for controlling a multivariable system. The method includes: (a) generating bias data as a function of model error in an on-board model; (b) updating a dynamic inversion algorithm with one or more model terms generated by the on-board model; (c) generating effector equation data by processing reference value data with the updated dynamic inversion algorithm, which effector equation data is indicative of one or more goal equations and one or more limit equations, and which reference value data is indicative of one or more goal values and one or more limit values and is determined as a function of predicted parameter data; (d) at least partially adjusting at least one of the reference value data and predicted parameter data for the model error using the bias data; and (e) generating one or more effector signals by processing the effector equation data with an optimization algorithm.

Abstract: A system comprises an apparatus, an actuator and a processor. The apparatus defines a flow path through an aperture, the aperture defines a pressure drop along the flow path, and the actuator regulates fluid flow across the pressure drop. The processor comprises a flow module, a comparator, an estimator and a control law. The flow module maps a flow curve relating a flow parameter to a pressure ratio, and defines a solution point located on the flow curve and a focus point located off the flow curve. The comparator generates an error as a function of a slope defined between the focus and solution points. The estimator moves the solution point along the flow curve, such that the error is minimized. The control law directs the actuator to position the control element, such that the flow parameter describes the fluid flow and the pressure ratio describes the pressure drop.

Abstract: A method for model-based control of a gas turbine engine is disclosed. An operating point of the gas turbine engine is generated from measured parameters using a component-level model. The component-level model is analytically linearized by taking the first partial derivative of output parameters of each component with respect to input parameters of each component, and evaluating the result at the operating point. Components of the linearized component-level model are combined to form a combined perturbational model of the gas turbine engine, which is inverted to solve for control commands as a function of target parameters and measured parameters.

Abstract: A system comprises a rotary apparatus, a control law and a processor. The rotary apparatus comprises a rotor and a housing forming a gas path therebetween, and the control law controls flow along the gas path. The processor comprises an output module, a plurality of temperature modules, a thermodynamic module, a comparator and an estimator. The output module generates an output signal as a function of a plurality of rotor and housing temperatures defined along the gas path, and the temperature modules determine time derivatives of the rotor and housing temperatures. The thermodynamic module models boundary conditions for the gas path, and the comparator determines errors in the boundary conditions. The estimator estimates the rotor and housing temperatures based on the time derivatives, such that the errors are minimized and the flow is controlled.

Abstract: A method and system for controlling a multivariable system. The method includes: (a) generating bias data as a function of model error in an on-board model; (b) updating a dynamic inversion algorithm with one or more model terms generated by the on-board model; (c) generating effector equation data by processing reference value data with the updated dynamic inversion algorithm, which effector equation data is indicative of one or more goal equations and one or more limit equations, and which reference value data is indicative of one or more goal values and one or more limit values and is determined as a function of predicted parameter data; (d) at least partially adjusting at least one of the reference value data and predicted parameter data for the model error using the bias data; and (e) generating one or more effector signals by processing the effector equation data with an optimization algorithm.

Abstract: A control system comprises an actuator, a control law and a processor. The actuator positions a control surface and the control law controls the actuator. The processor comprises an open loop module, a corrector, a comparator, and an estimator, and generates model output to direct the control law. The open loop module generates the model output as a function of a model state and a model input. The corrector generates a corrector output as a function of the model output. The comparator generates an error by comparing the corrector output to the model input. The estimator generates the model state as a function of the error, such that the error is minimized as a function of single-input, single-output gain matrix.

Abstract: A control system comprises an actuator, a control law and a processor. The actuator positions a control surface and the control law controls the actuator. The processor comprises an open loop module, a corrector, a comparator, and an estimator, and generates an output vector to direct the control law. The open loop module generates the output vector as a function of a state vector and an input vector. The corrector generates a corrector vector as a function of the output vector. The comparator generates an error vector by comparing the corrector vector to the input vector. The estimator generates the state vector as a function of the error vector, such that the error vector is minimized.

Abstract: A control system comprises a controller for positioning an actuator in a working fluid flow and a model processor for directing the controller as a function of a model feedback. The model processor comprises an output module, a comparator and an estimator. The output module generates the model feedback as a function of a constraint, a model state and a model input describing fluid parameters measured along the working fluid flow. The comparator generates an error by comparing the model feedback to the model input. The estimator generates the constraint and the model state, such that the error is minimized.

Abstract: A control system comprises an actuator, a control law and a processor. The actuator positions a control surface and the control law controls the actuator. The processor comprises an open loop module, a corrector, a comparator, and an estimator, and generates an output vector to direct the control law. The open loop module generates the output vector as a function of a state vector and an input vector. The corrector generates a corrector vector as a function of the output vector. The comparator generates an error vector by comparing the corrector vector to the input vector. The estimator generates the state vector as a function of the error vector, such that the error vector is minimized.

Abstract: A control system comprises an actuator, a control law and a processor. The actuator positions a control surface and the control law controls the actuator. The processor comprises an open loop module, a corrector, a comparator, and an estimator, and generates model output to direct the control law. The open loop module generates the model output as a function of a model state and a model input. The corrector generates a corrector output as a function of the model output. The comparator generates an error by comparing the corrector output to the model input. The estimator generates the model state as a function of the error, such that the error is minimized as a function of single-input, single-output gain matrix.

Abstract: A control system comprises a controller for positioning an actuator in a working fluid flow and a model processor for directing the controller as a function of a model feedback. The model processor comprises an output module, a comparator and an estimator. The output module generates the model feedback as a function of a constraint, a model state and a model input describing fluid parameters measured along the working fluid flow. The comparator generates an error by comparing the model feedback to the model input. The estimator generates the constraint and the model state, such that the error is minimized.