초록 ▼

A model-free adaptive controller is disclosed, which uses a dynamic artificial neural network with a learning algorithm to control any single-variable or multivariable open-loop stable, controllable, and consistently direct-acting or reverse-acting industrial process without requiring any manual tun

A model-free adaptive controller is disclosed, which uses a dynamic artificial neural network with a learning algorithm to control any single-variable or multivariable open-loop stable, controllable, and consistently direct-acting or reverse-acting industrial process without requiring any manual tuning, quantitative knowledge of the process, or process identifiers. The need for process knowledge is avoided by substituting 1 for the actual sensitivity function .differential.y(t)/.differential.u(t) of the process.

대표청구항 ▼

[ What is claimed is:] [1.] A model-free adaptive control system for an open-loop stable, controllable, and direct-acting or reverse-acting industrial process having a process input, a process output which is a constituent of a measured process variable, and a sensitivity function which is the ratio

[ What is claimed is:] [1.] A model-free adaptive control system for an open-loop stable, controllable, and direct-acting or reverse-acting industrial process having a process input, a process output which is a constituent of a measured process variable, and a sensitivity function which is the ratio ]EQU20 ] of the partial derivatives with respect to time of said process output and said process input, comprising:a) a dynamic block having a single error value input and a control value output, said dynamic block being arranged to iteratively, over a moving window of time, vary said control value to minimize said error value;b) a control objective function arranged to compute said error value from the difference value between said measured process variable and a selected setpoint; andc) a learning mechanism arranged to continuously iteratively modify the parameters of said dynamic block to vary said control value so as to reduce said error value, said learning mechanism being based solely on said measured process variable, said control value output, and said setpoint;d) an arbitrary non-zero constant being substituted for the actual sensitivity function of said process in said learning mechanism;e) said process being controlled in accordance with said control value output.