Colorado State University Research Foundation

Douglas C Hittle

Peter Michael Young

Charles Anderson

Matthew Kretchmar

Christopher Delnero

Tifani L Andr&eacute;

Michael Anderson

A neural network controller in parallel with a proportional-plus-integral (PI) feedback controller in a control system. At least one input port of the neural network for receiving an input signal representing a condition of a process is included. A first set of data is obtained that includes a plurality of output values of the neural network obtained during a training period thereof using a plurality of first inputs representing a plurality of conditions of the process. The process/plant condition signals generally define the process/plant, and may include one set-point as well as signals generated using measured systems variables/parameters. In operation, the neural network contributes to an output of the PI controller only upon detection of at least one triggering event, at which time a value of the first set of data corresponding with the condition deviation is added-in thus, contributing to the proportional-plus-integral feedback controller. The triggering event can be characterized as (a) a change in any one of the input signals greater-than a preselected amount, or (b) a detectable process condition deviation greater-than a preselected magnitude, for which an adjustment is needed to the process/plant being controlled. Also a method for controlling a process with a neural network controller operating in parallel with a IP controller is included.

Combined proportional plus integral (PI) and neural network (nN) controller

A feedback control system for automatic on-line training of a controller for a plant, the system having a reinforcement learning agent connected in parallel with the controller. The learning agent comprises an actor network and a critic network operatively arranged to carry out at least one sequence of a stability phase followed by a learning phase. During the stability phase, a multi-dimensional boundary of values is determined. During the learning phase, a plurality of updated weight values is generated in connection with the on-line training, if and until one of the updated weight values reaches the boundary, at which time a next sequence is carried out to determine a next multi-dimensional boundary of values followed by a next learning phase. Also, a method for automatic on-line training of a feedback controller within a system comprising the controller and a plant by employing a reinforcement learning agent comprising a neural network to carry out at least one sequence comprising a stability phase followed by a learning phase. Further included, a computer executable program code on a computer readable storage medium, for on-line training of a feedback controller within a system comprising the controller and a plant.

Control system and technique employing reinforcement learning having stability and learning phases

An apparatus and method for evaluation of a material having a first and second contact-surface. The apparatus includes a computer-controlled thermally-variable central element comprising a first and second outer surface, at least one outer surface having at least one temperature sensor thereon. Facing the first outer surface is a first exterior surface of a first thermally-variable side element, and facing the second outer surface is a second exterior surface of a second thermally-variable side element. A mechanism is a second exterior surface of a second thermally-variable side element. A mechanism is included that operates to move at least the first exterior surface toward the thermally-variable central element to apply a generally uniform pressure against the material contact-surfaces once the material has been so positioned.

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Fort Collins, CO, United States of America

Douglas C Hittle