Adaptive control: stability, convergence, and robustness
Adaptive control: stability, convergence, and robustness
Stable adaptive systems
Nonlinear control design: geometric, adaptive and robust
Nonlinear control design: geometric, adaptive and robust
Neural network fundamentals with graphs, algorithms, and applications
Neural network fundamentals with graphs, algorithms, and applications
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Nonlinear Control Systems
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The paper presents an on-line nonlinear approach to adaptively track, estimate or predict the states of a process based on input and output measurements. The approach develops a formulation using techniques from optimization theory, the calculus of variations, and gradient descent dynamics. The formulation allows for general parametrized nonlinear observers, including co-state (sensitivity) dynamics which propagate forward in time. In this setting, the co-state serves as a filtered version of the measured error signal. It is shown via simulation that signal tracking and state estimation are achieved in relatively fast time. Simulation examples are included to illustrate the performance of the approach.