The present invention discloses a model-free online recursive optimization method for a batch process based on variable period decomposition. Variable operation data closely related to product quality is acquired, optimization action on each subset is integrated on the basis of time domain variable division on the process by utilizing a data driving method and a global optimization strategy is formed, based on which an online recursive error correction optimization strategy is implemented. According to the method, the online optimization strategy is formed completely based on the operation data of the batch process without needing prior knowledge or a model of a process mechanism. Meanwhile, the optimized operation locus line has better adaptability by using the online recursive correction strategy, and thus the anti-interference requirement of the actual industrial production is better met.

A self-configuring extremum-seeking controller includes a dither signal generator, a communications interface, a phase delay estimator, and a bandwidth estimator. The dither signal generator identifies a stored dither frequency, generates a dither signal having the stored dither frequency, and uses the dither signal to perturb a control input for a plant. The communications interface provides the perturbed control input to the plant and receives an output signal from the plant resulting from the perturbed control input. The phase delay estimator estimates a phase delay between the output signal and the dither signal. The bandwidth estimator estimates a bandwidth of the plant based on the estimated phase delay. The dither signal generator updates the stored dither frequency based on the estimated bandwidth.

A variable refrigerant flow (VRF) system for a building. The VRF system includes at least one outdoor VRF unit configured to heat or cool a refrigerant for use in heating or cooling the building. The at least one outdoor VRF unit includes a sub-cooler and a bypass expansion valve configured to control a flow of the refrigerant through the sub-cooler and an extremum-seeking controller configured to generate a sub-cooling temperature setpoint for the at least one outdoor VRF unit. The extremum-seeking controller is configured to determine a total power consumption of the at least one outdoor VRF unit, generate a sub-cooling temperature setpoint for the at least one outdoor VRF unit using an extremum-seeking control technique that drives the total power consumption toward an extremum, and use the sub-cooling temperature setpoint to operate the at least one outdoor VRF unit.

An extremum-seeking control system includes a plant operable to affect a variable state or condition of a building and an extremum-seeking controller. The extremum-seeking controller is configured to provide a control input to a plant and receive a performance variable as a first feedback from the plant. The plant uses the control input to affect the performance variable. The extremum-seeking controller is configured to receive a constrained variable as a second feedback from the plant and calculate a performance penalty by applying a penalty function to the constrained variable. The extremum-seeking controller is further configured to modify the performance variable with the performance penalty to generate a modified cost function, estimate a gradient of the modified cost function with respect to the control input, and drive the gradient of the modified cost function toward zero by modulating the control input.

A system that includes: a gas turbine having a combustion system; a control system operably connected to the gas turbine for controlling an operation thereof; and a combustion auto-tuner, which is communicatively linked to the control system, that includes an optimization system having an empirical model of the combustion system and an optimizer; sensors configured to measure the inputs and outputs of the combustion system; a hardware processor; and machine-readable storage medium on which is stored instructions that cause the hardware processor to execute a tuning process for tuning the operation of the combustion system. The tuning process includes the steps of: receiving current measurements from the sensors for the inputs and outputs; given the current measurements received from the sensors, using the optimization system to calculate an optimized control solution for the combustion system; and communicating the optimized control solution to the control system.

A system that includes: a gas turbine having a combustion system; a control system operably connected to the gas turbine for controlling an operation thereof; and a combustion auto-tuner, which is communicatively linked to the control system, that includes an optimization system having an empirical model of the combustion system and an optimizer; sensors configured to measure the inputs and outputs of the combustion system; a hardware processor; and machine-readable storage medium on which is stored instructions that cause the hardware processor to execute a tuning process for tuning the operation of the combustion system. The tuning process includes the steps of: receiving current measurements from the sensors for the inputs and outputs; given the current measurements received from the sensors, using the optimization system to calculate an optimized control solution for the combustion system; and communicating the optimized control solution to the control system.

A variable refrigerant flow (VRF) system for a building. The VRF system includes at least one outdoor VRF unit configured to heat or cool a refrigerant for use in heating or cooling the building. The at least one outdoor VRF unit includes a sub-cooler and a bypass expansion valve configured to control a flow of the refrigerant through the sub-cooler and an extremum-seeking controller configured to generate a sub-cooling temperature setpoint for the at least one outdoor VRF unit. The extremum-seeking controller is configured to determine a total power consumption of the at least one outdoor VRF unit, generate a sub-cooling temperature setpoint for the at least one outdoor VRF unit using an extremum-seeking control technique that drives the total power consumption toward an extremum, and use the sub-cooling temperature setpoint to operate the at least one outdoor VRF unit.

A model predictive controller and related charging components producing a charge signal for a battery wherein predicted battery parameters such as state of charge, battery temperature, state of health (e.g., anode overpotential), are used to generate constraints that are subsequently used, such as through an optimizer running a cost function, to produce a charge signal that may include one or more optimized charge attributes including a charge current magnitude or a mean current, a shaped leading edge, an edge time, a body time, and a rest time.

The present invention discloses a model-free online recursive optimization method for a batch process based on variable period decomposition. Variable operation data closely related to product quality is acquired, optimization action on each subset is integrated on the basis of time domain variable division on the process by utilizing a data driving method and a global optimization strategy is formed, based on which an online recursive error correction optimization strategy is implemented. According to the method, the online optimization strategy is formed completely based on the operation data of the batch process without needing prior knowledge or a model of a process mechanism. Meanwhile, the optimized operation locus line has better adaptability by using the online recursive correction strategy, and thus the anti-interference requirement of the actual industrial production is better met.

A process control system includes: a controller; at least one input and output module connected to the controller; and an allowable propagation delay value calculator in the controller, the allowable propagation delay value calculator being configured to calculate, based on the number of input and output modules connected to the controller, an allowable range for propagation delay time between the controller and the input and output module.