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:
A method of controlling thrust for a gas turbine engine of an aircraft is provided. The method includes determining a fan speed required for minimum thrust to achieve an aircraft operation. The method also includes determining an excess amount of thrust generated by the gas turbine engine. The method also includes reducing the amount of thrust generated by the gas turbine engine.
Abstract:
A method of controlling thrust for a gas turbine engine of an aircraft is provided. The method includes determining a fan speed required for minimum thrust to achieve an aircraft operation. The method also includes determining an excess amount of thrust generated by the gas turbine engine. The method also includes reducing the amount of thrust generated by the gas turbine engine.
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:
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 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, 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:
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.