Abstract: A nutrient deficiency detection system including an image gathering unit that gathers at least one representation of a field and stiches the images together to produce a large single image of the field, an image analysis unit that identifies areas of nutrient deficiency in the field, and a deficiency analysis unit processes and calculates an effect on the yield of the field based on the nutrient deficiency.

Abstract: Systems and methods of adaptive control for uncertain nonlinear multi-input multi-output systems in the presence of significant unmatched uncertainty with assured performance are provided. The need for gain-scheduling is eliminated through the use of bandwidth-limited (low-pass) filtering in the control channel, which appropriately attenuates the high frequencies typically appearing in fast adaptation situations and preserves the robustness margins in the presence of fast adaptation.

Abstract: Systems and methods of adaptive control for uncertain nonlinear multi-input multi-output systems in the presence of significant unmatched uncertainty with assured performance are provided. The need for gain-scheduling is eliminated through the use of bandwidth-limited (low-pass) filtering in the control channel, which appropriately attenuates the high frequencies typically appearing in fast adaptation situations and preserves the robustness margins in the presence of fast adaptation.

Abstract: An adaptive control system is provided that scales both gain and commands to avoid input saturation. The input saturation occurs when a commanded input uc exceeds an achievable command limit of umax. To avoid input saturation, the commanded input uc is modified according to a factor ?.

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: An adaptive control method is provided that scales both gain and commands to avoid input saturation. The input saturation occurs when a commanded input uc exceeds an achievable command limit of umax. To avoid input saturation, the commanded input uc is modified according to a factor ?.

Abstract: An adaptive control system is provided that scales both gain and commands to avoid input saturation. The input saturation occurs when a commanded input uc exceeds an achievable command limit of umax. To avoid input saturation, the commanded input uc is modified according to a factor ?.

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: 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: A novel 1 adaptive/neural control architecture provides a device and method that permits fast adaptation and yields guaranteed transient response simultaneously for both the system's input and output signals, in addition to providing asymptotic tracking. The main feature of the invention is rapid adaptation with a guaranteed low frequency control signal. The ability to adapt rapidly ensures the desired transient performance for both the system's input and output signals, simultaneously, while a low-pass filter in the feedback loop attenuates the high-frequency components in the control signal.

Abstract: An adaptive control method is provided that scales both gain and commands to avoid input saturation. The input saturation occurs when a commanded input uc exceeds an achievable command limit of umax. To avoid input saturation, the commanded input uc is modified according to a factor ?.

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: 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: 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: 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.