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 hybrid control system and a method for predicting a behavior of a physical system using the hybrid control system is disclosed. The hybrid control system may include a model inverting control system capable of implementing a model inverting control law and determining an active set of goals and limits and a model predictive control system capable of implementing a model predictive control law and utilizing the active set of goals and limits to determine current effector requests, the current effector requests being used to control behavior of the physical system.

Abstract: A method for controlling a gas turbine engine includes: generating model parameter data as a function of prediction error data, which model parameter data includes at least one model parameter that accounts for off-nominal operation of the engine; at least partially compensating an on-board model for the prediction error data using the model parameter data; generating model term data using the on-board model, wherein the on-board model includes at least one model term that accounts for the off-nominal operation of the engine; respectively updating one or more model parameters and one or more model terms of a model-based control algorithm with the model parameter data and model term data; and generating one or more effector signals using the model-based control algorithm.

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 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 hybrid control system and a method for predicting a behavior of a physical system using the hybrid control system is disclosed. The hybrid control system may include a model inverting control system capable of implementing a model inverting control law and determining an active set of goals and limits and a model predictive control system capable of implementing a model predictive control law and utilizing the active set of goals and limits to determine current effector requests, the current effector requests being used to control behavior of the physical system.

Abstract: A substrate for use in a laminated chip carrier (LCC) and a system in package (SiP) device having a coreless buildup layer and at least one metal and at least one dielectric layer. The coreless buildup dielectric layers can include thermoset and thermoplastic resin.

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 method for controlling a gas turbine engine includes: generating model parameter data as a function of prediction error data, which model parameter data includes at least one model parameter that accounts for off-nominal operation of the engine; at least partially compensating an on-board model for the prediction error data using the model parameter data; generating model term data using the on-board model, wherein the on-board model includes at least one model term that accounts for the off-nominal operation of the engine; respectively updating one or more model parameters and one or more model terms of a model-based control algorithm with the model parameter data and model term data; and generating one or more effector signals using the model-based control algorithm.

Abstract: A method of estimating a basepoint includes receiving a plurality of goals, wherein each goal has a desired value, and receiving a plurality of sensor feedback signals from a controlled system. A plurality of predicted output values of the controlled system are received from a mathematical model. A desired change for a plurality of basepoint values is estimated in response to the goals, the feedback, and the predicted output values. An actual change in basepoint values is calculated in response to a plurality of limits and the desired change for the plurality of basepoint values according to a plurality of goal weights while holding limits. The actual change in basepoint values is combined with last pass values of the plurality of basepoint values to produce an updated basepoint estimate.

Abstract: A method of estimating a basepoint includes a plurality of goals, wherein each goal has a desired value, receiving a plurality of sensor feedback signals from a controlled system, and receiving a plurality of predicted output values of the controlled system from a mathematical model. A desired change for a plurality of basepoint values is estimated in response to the goals, the feedback, and the predicted ooutput values. An actual change in basepoint values is calculated in response to a plurality of limits and the desired change for the plurality of basepoint values. The desired change is modified as necessary to hold the limits. The actual change in basepoint values is combined with last pass values of the plurality of basepoint values to produce an updated basepoint estimate.

Abstract: A method for controlling a multivariable system according to one non-limiting embodiment includes receiving a plurality of limits, receiving a first quantity of goals each having a desired value, and receiving sensor feedback. The method further includes estimating a basepoint in response to the first quantity of goals, the plurality of limits, and the sensor feedback, wherein the basepoint includes a set of values corresponding to an equilibrium point at which a predetermined amount of enabled limits are met and a second quantity of goals are fulfilled according to a goal prioritization scheme. Actuator requests are transmitted to a controlled system in response to the estimated basepoint. Predicted values from a mathematical model are compared to the sensor feedback, and the estimated basepoint is selectively adjusted in response to a difference between the predicted values and the sensor feedback in order to reduce the difference.

Abstract: A method of estimating a basepoint includes receiving a plurality of goals, wherein each goal has a desired value, and receiving a plurality of sensor feedback signals from a controlled system. A plurality of predicted output values of the controlled system are received from a mathematical model. A desired change for a plurality of basepoint values is estimated in response to the goals, the feedback, and the predicted output values. An actual change in basepoint values is calculated in response to a plurality of limits and the desired change for the plurality of basepoint values according to a plurality of goal weights while holding limits. The actual change in basepoint values is combined with last pass values of the plurality of basepoint values to produce an updated basepoint estimate.

Abstract: A method of estimating a basepoint includes receiving a plurality of goals, wherein each goal has a desired value, receiving a plurality of sensor feedback signals from a controlled system, and receiving a plurality of predicted output values of the controlled system from a mathematical model. A desired change for a plurality of basepoint values is estimated in response to the goals, the feedback, and the predicted output values. An actual change in basepoint values is calculated in response to a plurality of limits and the desired change for the plurality of basepoint values. The desired change is modified as necessary to hold the limits. The actual change in basepoint values is combined with last pass values of the plurality of basepoint values to produce an updated basepoint estimate.

Abstract: A method for controlling a multivariable system according to one non-limiting embodiment includes receiving a plurality of limits, receiving a first quantity of goals each having a desired value, and receiving sensor feedback. The method further includes estimating a basepoint in response to the first quantity of goals, the plurality of limits, and the sensor feedback, wherein the basepoint includes a set of values corresponding to an equilibrium point at which a predetermined amount of enabled limits are met and a second quantity of goals are fulfilled according to a goal prioritization scheme. Predicted values from a mathematical model are compared to the sensor feedback, and the estimated basepoint is selectively adjusted in response to a difference between the predicted values and the sensor feedback in order to reduce the difference.

Abstract: A method of making a circuitized substrate assembly wherein the assembly includes individual circuitized substrates bonded together. The substrates each include at least one opening, and a cover is placed over one of the openings and a quantity of conductive paste is positioned thereon prior to bonding the substrates. At least some of the paste is then forced up into an opening in the other substrate as a result of the bonding.

Abstract: Real-time control of a dynamical system is provided by determining control variables that get as close as possible to producing a desired response. Additional consideration of physical limitations leads to a convex Quadratic Program with inequality constraints that needs to be solved in real-time. A new active set algorithm is described to solve the convex Quadratic Program efficiently that meets real-time requirements. Based on the key observation that the physical limitations of the system translate to optimal active sets that remain relatively unchanged over time (even though the actual optimal controls may be varying), starting guesses for the active set obtained from the final iterate in the previous time period greatly reduces the number of iterations and hence allows the Quadratic Programs to be solved to convergence in real-time.

Abstract: An MPC Control system provides a life extending control that includes life-extending goals in the performance index of the MPC controller and limits in the inequality equations. The MPC controller performs the normal functions of a control system for a physical system, but does so in a manner that extends the life or time-to-next maintenance or reduces the number of parts that need to be replaced. If the life extending functions do not degrade other control functions, they can be always enabled, making the system less expensive to maintain. If the life extending functions degrade some other control functions, they can be adjusted in-the-field or on-the-fly to stretch the time-until-maintenance until it is more convenient, but with some impact on performance.

Abstract: An efficient method for solving a model predictive control problem is described. A large sparse matrix equation is formed based upon the model predictive control problem. The square root of H, Hr, is then formed directly, without first forming H. A square root (LSMroot) of a large sparse matrix of the large sparse matrix equation is then formed using Hr in each of a plurality of iterations of a quadratic programming solver, without first forming the large sparse matrix and without recalculating Hr in each of the plurality of iterations. The solution of the large sparse matrix equation is completed based upon LSMroot.

Abstract: A system and method reduces undesired noise or vibration in a vehicle. The ambient vibration is measured and command signals are generated over time. The command signals are generated based upon the measured vibration and based upon a control weighting. By varying the control weighting over time, the maximum possible performance is always obtained subject to the saturation constraints.