Automated embedded tuning of model-less controllers

A method, comprising:

• receiving, via at least one processor, data comprising:
• one or more commands generated by a model-less controller to control one or more operations of one or more devices within a system;
• and
• one or more output parameters associated with the one or more devices of the system;
• determining, via the at least one processor, whether the data is indicative of a change in one or more operational characteristics of the system;
• generating a model representative of the one or more operational characteristics of the system as a function of the data based on a Bayesian optimization algorithm in response to the data being indicative of the change;
• determining an excitation input based on the Bayesian optimization algorithm in response to the data not being indicative of the change, wherein the excitation input is configured to induce the change in the one or more operational characteristics within a threshold range by adjusting at least a first subset of the data;
• after determining the excitation input:
• transmitting the excitation input to the one or more devices;
• after transmitting the excitation input, identifying a time period corresponding to:
• when a second subset of the data is outside a second threshold range;
• and
• when a variation in the second subset of the data exceeds a variation threshold;
• receiving one or more updated output parameters associated with the one or more devices of the system during the time period;
• generating the model based on the one or more updated output parameters received during the time period and the excitation input
• generating one or more model-less parameters associated with the model-less controller based on the model;
• and
• transmitting the one or more model-less control parameters to the model-less controller, wherein the model-less control is augmented based on the one or more model-less control parameters, wherein the model-less controller is configured to control the one or more devices based on the one or more model-less control parameters to converge the system to a desired state.

A control system comprising one or more processors configured to perform operations comprising:

• receiving data representative of:
• one or more commands generated by a model-less controller to control one or more operations of one or more devices within a system;
• and
• one or more output parameters associated with the one or more devices of the system;
• determining whether the data is indicative of a change in one or more operational characteristics of the system;
• generating a model representative of the one or more operational characteristics of the system as a function of the data based on a Bayesian optimization algorithm in response to the data being indicative of the change;
• determining an excitation input based on the Bayesian optimization algorithm in response to the data not being indicative of the change, wherein the excitation input is configured to induce the change in the one or more operational characteristics within a threshold range by adjusting at least a first subset of the data;
• after determining the excitation input:
• transmitting the excitation input to the one or more devices;
• after transmitting the excitation input, identifying a time period corresponding to:
• when a second subset of the data is outside a second threshold range;
• and
• when a variation in the second subset of the data exceeds a variation threshold;
• receiving one or more updated output parameters associated with the one or more devices of the system during the time period;
• generating the model based on the one or more updated output parameters received during the time period and the excitation input
• generating one or more model-less control parameters associated with the model-less controller based on the model;
• and
• transmitting the one or more model-less control parameters to the model-less controller, wherein the model-less control is augmented based on the one or more model-less control parameters, and wherein the model-less controller is configured to control the one or more devices based on the one or more model-less control parameters to converge the system to a desired state.