Abstract: A method for modeling a system that includes a disturbance rejection model configured for modeling an operation of the system so to generate a predicted value for a system output. The disturbance rejection model having a network for mapping system inputs to the system output, and input-output pairings, each representing a unique pairing of one of the system inputs with the system output. The method may include the steps of: calculating a confidence metric for a selected input-output pairing of the disturbance rejection model; and recommending a modification be made to the disturbance rejection model based upon the confidence metric calculated for the selected one of the input-output pairing. The confidence metric may indicate a probability that a predicted sign of a gain in the system output made by the disturbance rejection model is correct when the system input of the selected input-output pairing is varied.

Abstract: A system that includes: a gas turbine having a combustion system; a control system operably connected to the gas turbine for controlling an operation thereof; and a combustion auto-tuner, which is communicatively linked to the control system, that includes an optimization system having an empirical model of the combustion system and an optimizer; sensors configured to measure the inputs and outputs of the combustion system; a hardware processor; and machine-readable storage medium on which is stored instructions that cause the hardware processor to execute a tuning process for tuning the operation of the combustion system. The tuning process includes the steps of: receiving current measurements from the sensors for the inputs and outputs; given the current measurements received from the sensors, using the optimization system to calculate an optimized control solution for the combustion system; and communicating the optimized control solution to the control system.

Abstract: A system that includes: a gas turbine having a combustion system; a control system operably connected to the gas turbine for controlling an operation thereof; and a combustion auto-tuner, which is communicatively linked to the control system, that includes an optimization system having an empirical model of the combustion system and an optimizer; sensors configured to measure the inputs and outputs of the combustion system; a hardware processor; and machine-readable storage medium on which is stored instructions that cause the hardware processor to execute a tuning process for tuning the operation of the combustion system. The tuning process includes the steps of: receiving current measurements from the sensors for the inputs and outputs; given the current measurements received from the sensors, using the optimization system to calculate an optimized control solution for the combustion system; and communicating the optimized control solution to the control system.

Abstract: A method for modeling an operation of a system that may include a disturbance rejection model that is configured to generate a predicted value for a system output at a future time. The disturbance rejection model may include a neural network for mapping system inputs to the system output. The method may include the steps of: training the disturbance rejection model per a training dataset; and calculating a confidence metric for the disturbance rejection model. The confidence metric is configured to indicate a probability that a predicted sign of a gain in the system output at the future time made by the disturbance rejection model is correct.

Abstract: A method for training a disturbance rejection model that is configured to model an operation of a system so to calculate a predicted value for a system output at a future time. The disturbance rejection model may include a network for mapping system inputs to the system output, the network including a weight vector and a feedback coefficient. The method may include: obtaining a training dataset, and training the disturbance rejection model pursuant to the training dataset. The training may include calculating updated values for each of the weight vector and the feedback coefficient of the network by minimizing an error function that include a first hyperparameter and a second hyperparameter. The first hyperparameter may include a vector for penalizing the weight vector and the second hyperparameter may include a scalar.

Abstract: A method for modeling a system that includes a disturbance rejection model configured for modeling an operation of the system so to generate a predicted value for a system output. The disturbance rejection model having a network for mapping system inputs to the system output, and input-output pairings, each representing a unique pairing of one of the system inputs with the system output. The method may include the steps of: calculating a confidence metric for a selected input-output pairing of the disturbance rejection model; and recommending a modification be made to the disturbance rejection model based upon the confidence metric calculated for the selected one of the input-output pairing. The confidence metric may indicate a probability that a predicted sign of a gain in the system output made by the disturbance rejection model is correct when the system input of the selected input-output pairing is varied.

Abstract: A method for modeling an operation of a system that may include a disturbance rejection model that is configured to generate a predicted value for a system output at a future time. The disturbance rejection model may include a neural network for mapping system inputs to the system output. The method may include the steps of: training the disturbance rejection model per a training dataset; and calculating a confidence metric for the disturbance rejection model. The confidence metric is configured to indicate a probability that a predicted sign of a gain in the system output at the future time made by the disturbance rejection model is correct.

Abstract: A method for training a disturbance rejection model that is configured to model an operation of a system so to calculate a predicted value for a system output at a future time. The disturbance rejection model may include a network for mapping system inputs to the system output, the network including a weight vector and a feedback coefficient. The method may include: obtaining a training dataset, and training the disturbance rejection model pursuant to the training dataset. The training may include calculating updated values for each of the weight vector and the feedback coefficient of the network by minimizing an error function that include a first hyperparameter and a second hyperparameter. The first hyperparameter may include a vector for penalizing the weight vector and the second hyperparameter may include a scalar.

Abstract: A method for controlling an operation of a system that includes a disturbance rejection model that is configured for modeling the operation of the system so to generate a predicted value for a system output at a future time. The disturbance rejection model may include a neural network for mapping system inputs to the system output. The method may include the steps of: calculating a probabilistic distribution for the predicted value of the system output at the future time, where the calculating of the probabilistic distribution includes a Bayesian evidence framework without sampling; and controlling the operation of the system per the calculated probabilistic distribution.

Abstract: An improved autopilot system of the type which uses pitch commands to control airspeed with an improvement of including a temporary pitch hold command being issued if thrust changes would oppose the autopilot pitch command. The temporary pitch hold command would end when the airspeed reaches a calculated airspeed capture point.