Abstract: Systems and methods for adaptive control are disclosed. The systems and methods can control uncertain dynamic systems. The control system can comprise a controller that employs a parameter dependent Riccati equation. The controller can produce a response that causes the state of the system to remain bounded. The control system can control both minimum phase and non-minimum phase systems. The control system can augment an existing, non-adaptive control design without modifying the gains employed in that design. The control system can also avoid the use of high gains in both the observer design and the adaptive control law.

Abstract: The present invention relates to a method of providing a nearly continuously updated, on-line estimate of wind magnitude and direction when in turning flight and more particularly, relates to a method that requires only a GPS receiver and y- and z-body axis mounted gyros.

Abstract: An artificial nail composition containing multicarbonyl-vinyl containing monomer, polymerization initiator, and optical brightener. The invention also relates to the process of making the nail composition.

Abstract: A computer system for controlling a nonlinear physical process. The computer system comprises a linear controller and a neural network. The linear controller receives a command signal for control of the nonlinear physical process and a measured output signal from the output of the nonlinear physical process. The linear controller generates a control signal based on the command signal, a measured output signal, and a fixed linear model for the process. The neural network receives the control signal from the linear controller and the measured output signal from the output of the nonlinear physical process. The neural network uses the measured output signal to modify the connection weights of the neural network. The neural network also generates a modified control signal supplied to the linear controller to iterate a fixed point solution for the modified control signal used to control the nonlinear physical process.

Abstract: The invention comprises apparatuses and methods for providing the capability to stabilize and control a non-minimum phase, nonlinear plant with unmodeled dynamics and/or parametric uncertainty through the use of adaptive output feedback. A disclosed apparatus can comprise a reference model unit for generating a reference model output signal ym. The apparatus can comprise a combining unit that combines and differences a plant output signal y of a non-minimum phase plant for which not all of the states can be sensed, and a plant output signal y, to generate an output error signal {tilde over (y)}. The apparatus can further comprise an adaptive control unit for generating an adaptive control signal uad used to control the plant.

Abstract: The invention includes an adaptive control system used to control a plant. The adaptive control system includes a hedge unit that receives at least one control signal and a plant state signal. The hedge unit generates a hedge signal based on the control signal, the plant state signal, and a hedge model including a first model having characteristics of the adaptive control system and plant to which the adaptive control system is not to adapt. The hedge signal is used in the adaptive control system to remove the effects of the characteristics from a signal supplied to an adaptation law unit of the adaptive control system so that the adaptive control system does not adapt to the characteristics in controlling the plant.

Abstract: A computer system for controlling a nonlinear physical process. The computer system comprises a linear controller and a neural network. The linear controller receives a command signal for control of the nonlinear physical process and a measured output signal from the output of the nonlinear physical process. The linear controller generates a control signal based on the command signal, a measured output signal, and a fixed linear model for the process. The neural network receives the control signal from the linear controller and the measured output signal from the output of the nonlinear physical process. The neural network uses the measured output signal to modify the connection weights of the neural network. The neural network also generates a modified control signal supplied to the linear controller to iterate a fixed point solution for the modified control signal used to control the nonlinear physical process.

Abstract: A system and method of guiding an aircraft is disclosed. The aircraft includes at least one non-rigidly attached sensor for providing a signal indicative of the angular rate of turn of the aircraft. The signal includes an oscillation component and an angular rate of turn component. A filter receives the signal from the sensor and attempts to remove the oscillation component found in the signal. A guidance system provides a commanded turn rate signal. A stability augmentation system receives the filtered angular rate of turn signal and the commanded turn rate signal and processes those signals to provide an actuator command signal. The actuator command signal can best be used to control an air vehicle activation device. The method involves receiving at least one signal indicative of an angular rate of turn of the aircraft from the sensor not rigidly suspended from an aircraft, wherein the signal includes an oscillation component and an angular rate of turn component.

Abstract: An adaptive control system (ACS) uses direct output feedback to control a plant. The ACS uses direct adaptive output feedback control developed for highly uncertain nonlinear systems, that does not rely on state estimation. The approach is also applicable to systems of unknown, but bounded dimension, whose output has known, but otherwise arbitrary relative degree. This includes systems with both parameter uncertainty and unmodeled dynamics. The result is achieved by extending the universal function approximation property of linearly parameterized neural networks to model unknown system dynamics from input/output data. The network weight adaptation rule is derived from Lyapunov stability analysis, and guarantees that the adapted weight errors and the tracking error are bounded.

Abstract: A disclosed apparatus comprises an adaptive observer that has an adaptive element to augment a linear observer to enhance its ability to control a nonlinear system. The adaptive element comprises a first, and optionally a second, nonlinearly parameterized neural network unit, the inputs and output layer weights of which can be adapted on line. The adaptive observer generates the neural network units' teaching signal by an additional linear error observer of the nominal system's error dynamics. The adaptive observer has the ability to track an observed system in the presence of unmodeled dynamics and disturbances. The adaptive observer comprises a delay element incorporated in the adaptive element in order to provide delayed values of an actual output signal and a control signal to the neural network units.

Abstract: The invention comprises apparatuses and methods for providing the capability to stabilize and control a non-minimum phase, nonlinear plant with unmodeled dynamics and/or parametric uncertainty through the use of adaptive output feedback. A disclosed apparatus can comprise a reference model unit for generating a reference model output signal ym. The apparatus can comprise a combining unit that combines and differences a plant output signal y of a non-minimum phase plant for which not all of the states can be sensed, and a plant output signal y, to generate an output error signal {tilde over (y)}. The apparatus can further comprise an adaptive control unit for generating an adaptive control signal uad used to control the plant.