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 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 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.
Abstract:
A control system includes a plant 56 having resonant modes and a controller 5 which receives plant input signals on lines 14,24 from the plant 56 and provides a controller output signal I, related to a filter output signal V, which controls the plant 6. The controller is provided with a non-linear notch filter 30 which receives a filter input signal x related to the plant input signals and provides the filter output signal V. The notch filter has at least one notch frequency near one of the resonant modes so as to attenuate one of the modes by a predetermined amount and has a phase lag a decade below the lowest notch frequency of said notch filter which is less than that of a corresponding linear notch filter, thereby allowing the control system 7 to exhibit faster time response and increased bandwidth than a system employing linear notch filters.
Abstract:
A reduced phase-shift nonlinear filter includes linear filter logic 10 responsive to a filter input signal x and having a linear transfer function G(s), which provides a linear filtered signal g, zero-cross sample-and-hold logic 16 responsive to the filter input signal x and the linear filtered signal g, which provides a square wave signal n which crosses zero at the same time and in the same direction as the filter input signal x and has an amplitude proportional to the value of the linear filtered signal g at that time, complementary filter logic 20 responsive to said square wave signal n and having a complementary transfer function (1-G(s)) which provides a complementary filtered signal c, and a summer 30 which adds the complementary filtered signal and the linear filtered signal to provide a filter output signal y which exhibits less phase shift over certain frequency bands than that of the linear transfer function. If the linear transfer function has numerator and denominator polynomials of the same order, the complementary transfer function (1-G(s)) 20 may be a reduced-order transfer function.
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 is provided for the semi-active damping of oscillations during vertical motion of an elevator car relative to a desired trajectory along a relatively lengthy elevator travel path. The elevator car is connected to a motor-controlled support rope in a manner allowing limited relative vertical motion therebetween. A soft spring and a controllable damping means are connected in parallel between the rope and the elevator car. The damping means may be a hydraulic piston and cylinder arrangement controlled via a variable orifice valve. The spring may be a gas-pressurized accumulator connected to the hydraulics of the damping means. A control system provides a motion command signal to control the motor for motion control and the variable orifice valve for damping oscillations. Full closure of the variable orifice valve effectively locks the damping means to maintain a position when the elevator car is braked, and a tension release control gradually releases any accumulated tension across the valve when the brake is released.
Abstract:
An elevator motion control system compares a dictated flight path signal (101), indicative of a desired elevator flight path along a nominal flight trajectory, with a measured flight path signal (108), indicative of actual elevator motion, and provides a motion command signal (115) to both a high pass filter (117) and a low pass filter (116) such that the frequency of the motion command signal is split into high and low frequency components (141,118). An active elevator hitch (36) is used to implement the high frequency/low stroke portion of the motion command signal while the elevator motor (28) is used to implement the low frequency/high stroke portion of the motion command signal. A time delay (106) delays the dictated flight path signal prior to its use with the measured flight path signal for providing the motion command signal, the duration of the time delay corresponding to the delay associated with a motion perturbation propagating along a main rope (14) between the elevator motor and the elevator car (12). The active elevator hitch (36) includes a support plate (40) interconnected to the elevator car, a hitch plate (46), and at least one force actuator (56) having a variable extension. The force actuator is connected between the hitch plate and the support plate, and the variable extension is controlled for varying the vertical position of the elevator car along the elevator flight path for damping at least the high frequency components of elevator car vertical oscillations.
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.