Adaptive control system having direct output feedback and related apparatuses and methods

DETAILED DESCRIPTION The adaptive control system (ACS) and method of this invention uses direct adaptive output feedback to control a plant.
The system can comprise a linear controller (LC) and an adaptive element (AE).
The linear controller can be used as a dynamic compensator to stabilize a model of the plant, and provide output regulation.
The adaptive element can compensate for disturbances, and modeling error resulting from approximation in modeling of the plant.
The adaptive element can comprise a neural network (NN).
The adaptive element can receive a signal from the linear controller used to adapt its NN's weights.
The input vector to the NN can comprise current and/or past plant output signals together with other available signals.
The past plant output signal(s) can be used as inputs to the NN to ensure boundedness of the adaptive element in controlling the plant.
The adaptive control system can comprise an error conditioning element having a low-pass filter designed to satisfy a strictly positive real (SPR) condition of a transfer function associated with Lyapunov stability analysis of the control system.
The stability analysis can be used to construct the NN adaptation law using only the plant output signal(s) and other available signals as inputs to the NN, and to ensure boundedness of error signal(s) of the closed-loop adaptive control system.
Apparatuses forming components of the ACS are also disclosed.
A method of the invention comprises generating at least one control signal δc to regulate a plant output signal y by feedback of the plant output signal y, and optionally other sensed variables related to the state of the plant, in which y is a function of the plant state having known but unrestricted relative degree r.
The control signal δc can be generated so as to control the plant based on an approximate dynamic model, and so as to control the plant in the presence of unmodeled dynamics in the plant based on an adaptive control technique.
The adaptive control technique can be implemented with a neural network