Abstract: Systems and methods for controlling a fluid based 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, the dynamic states input to an open loop model based on the model operating mode, where the open loop model generates current state derivatives, solver state errors, and synthesized parameters as a function of the dynamic states and a model input vector. A constraint on the state derivatives and solver state errors is based on a material temperature utility for determining a material temperature associated with a component of the cycle of the control system. The model processor may include an estimate state module for determining an estimated state of the model based on at least one of a prior state, the current state derivatives, the solver state errors, and the synthesized parameters.

Abstract: A full authority digital engine controller (FADEC) based system is also disclosed. The system includes a processor, and a tangible, non-transitory memory configured to communicate with the processor, the tangible, non-transitory memory having instructions stored thereon that, in response to execution by the processor, cause the FADEC to perform operations. The operations may include measuring a first temperature at a first sensor disposed at a first known location of an engine, measuring a second temperature at a second sensor disposed at a second known location of the engine, and estimating at least one of a stress or a strain of a part or component in the engine based on the first temperature and the second temperature. The system may control fuel flow and/or other engine effectors during a thrust transient to limit the estimated stress or the estimated strain of the component from exceeding a predetermined threshold.

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: 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 determining estimator gain associated with the current state model and applying the estimator gain to determine the estimated state of the model.

Abstract: Systems and methods for controlling a fluid based 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, the dynamic states input to an open loop model based on the model operating mode, wherein the open loop model generates current state derivatives, solver state errors, synthesized parameters as a function of the dynamic states and a model input vector. A constraint on the state derivatives and solver state errors is based on a series, of utilities that are based on mathematical abstractions of physical laws that govern behavior of the component. The model processor may include an estimate state module for determining an estimated state of the model based on at least one of a prior state, the current state derivatives, the solver state errors, and the synthesized parameters.

Abstract: Systems and methods for controlling a fluid based engineering system are disclosed. The systems and methods may include a model processor configured to generate 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 a model operating mode. The model processor may further include an estimate state module configured to determine an estimated state of the model based on at least one of a prior state, current state derivatives, solver state errors, and synthesized parameters, the estimate state module using online linearization and gain calculation to determine estimator gain for determining the estimated state of the model.

Abstract: A method and control system for an aircraft using a gas turbine engine is provided. The control system includes a controller that includes a load and engine control modules and communicates control signals to a plant that includes a gas generator and a rotor load, an engine estimation module that receives engine state measurements and effector feedback/command signals from the controller and communicates a power turbine torque estimate, and a load estimation module that receives signals including the power turbine torque estimate, a first power turbine speed value, a first power turbine torque value, a second power turbine speed value, a second power turbine torque value, and a rotor speed value. The load estimation module generates one or more of a power turbine speed estimate, a power turbine torque estimate, and a rotor load estimate based on the received signals.

Abstract: A method and control system for an aircraft using a gas turbine engine is provided. The control system includes a controller that includes a load and engine control modules and communicates control signals to a plant that includes a gas generator and a rotor load, an engine estimation module that receives engine state measurements and effector feedback/command signals from the controller and communicates a power turbine torque estimate, and a load estimation module that receives signals including the power turbine torque estimate, a first power turbine speed value, a first power turbine torque value, a second power turbine speed value, a second power turbine torque value, and a rotor speed value. The load estimation module generates one or more of a power turbine speed estimate, a power turbine torque estimate, and a rotor load estimate based on the received signals.

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 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 load power sharing control system for a twin-engine rotorcraft having a first gas turbine engine and a second gas turbine engine is provided.

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: 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 include an input object for processing model input and setting a model operating mode, the model operating mode being a starting mode if the model input is within a data range associated with a starting operation of the control device. 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.

Abstract: A system and methods are provided for controlling turboshaft engines. In one embodiment, a method includes receiving input signals for a collective lever angle (CLA) command and real-time power turbine speed (NP) of an engine, determining system data for engine effectors by the control unit based on the input signals for the collective lever angle (CLA) command and the real-time power turbine speed (NP) based on an integrated model for the turboshaft engine including a model of a gas generator section of the turboshaft engine and a model of a power turbine and rotor load section of the turboshaft engine. The method may also include determining control output based on model-based multi-variable control including optimization formulation and a constrained optimization solver. The method may also include outputting one or more control signals for control of the turboshaft engine.

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, wherein the open loop model generates a current state model as a function of the dynamic states and the model input, wherein a constraint on the current state model is based a series of cycle synthesis modules, each member of the series of cycle synthesis modules modeling a component of a cycle of the control system and including a series of utilities, the utilities are based on mathematical abstractions of physical properties associated with the component.

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 system may include a control law for directing the actuator as a function of a model output and for determining if the control device is operating with deteriorated conditions. 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 an open loop model.

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, wherein the open loop model generates a current state model as a function of the dynamic states and the model input, wherein a constraint on the current state model is based a series of cycle synthesis modules, each member of the series of cycle synthesis modules modeling a component of a cycle of the control system and including a series of utilities, the utilities are based on mathematical abstractions of physical properties associated with the component. 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.

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 determining estimator gain associated with the current state model and applying the estimator gain to determine the estimated state of the model.