A system for flight control system using simulator data for an electric aircraft is presented. The system includes a computing device, the computing device configured to receive a plurality of measured flight data, simulate a plurality of aircraft performance model outputs as a function of a flight simulator and the plurality of measured flight data, determine a moment datum as a function of the plurality of measured flight data and the plurality of aircraft performance model outputs, generate an allocation command datum as a function of the moment datum and the plurality of aircraft performance model outputs, and perform a torque allocation on a flight component of a plurality of flight components as a function of the allocation command and the moment datum.

A system for flight control system using simulator data for an electric aircraft is presented. The system includes a computing device, the computing device configured to receive a plurality of measured flight data, simulate a plurality of aircraft performance model outputs as a function of a flight simulator and the plurality of measured flight data, determine a moment datum as a function of the plurality of measured flight data and the plurality of aircraft performance model outputs, generate an allocation command datum as a function of the moment datum and the plurality of aircraft performance model outputs, and perform a torque allocation on a flight component of a plurality of flight components as a function of the allocation command and the moment datum.

A method for controlling production of one or more refined resources by an energy provider includes predicting a demand for the refined resources by one or more consumers of the refined resources as a function of an incentive offered by the energy provider. The method further includes performing an optimization of an objective function subject to a constraint based on the predicted demand for the refined resources to determine an amount of the refined resources for the energy provider to produce and a value of the incentive at multiple times within a time period. The method also includes providing setpoints for equipment of the energy provider that cause the equipment to produce the amount of the refined resources determined by performing the optimization.

A model predictive control system is used to optimize energy cost in a variable refrigerant flow (VRF) system. The VRF system includes an outdoor subsystem and a plurality of indoor subsystems. The model predictive control system includes a high-level model predictive controller (MPC) and a plurality of low-level indoor MPCs. The high-level MPC performs a high-level optimization to generate an optimal indoor subsystem load profile for each of the plurality of indoor subsystems. The optimal indoor subsystem load profiles optimize energy cost. Each of the low-level indoor MPCs performs a low-level optimization to generate optimal indoor setpoints for one or more indoor VRF units of the corresponding indoor subsystem. The indoor setpoints can include temperature setpoints and/or refrigerant flow setpoints for the indoor VRF units.

A system, methodology, and programming logic for active stormwater controls to optimize sizing and design of Hydromodification Management (HM) structural Best Management Practices (BMPs) to achieve optimal flow duration control. Control logic enables the controlled release of stormwater from a BMP in a manner most akin to pre-development flow duration curves. Inputs to this logic include: flow duration curves based on continuous hydrologic simulation for pre- and post-development conditions; real-time measurement of water level within the BMP; and real-time measurement of discharge entering the BMP. This control logic can interact with control logic for other stormwater management objectives, such as harvest and reuse, infiltration, and combined sewer overflow prevention, and respective inputs, such as real-time weather forecast data, precipitation gage data, downstream flow gauge data, and water quality data, to meet those design objectives as well. New HM BMPs can be optimized to be smaller and, thus, more feasible to implement. Existing stormwater facilities designed for flood control or other management objectives can be retrofitted to provide hydromodification control as well. When utilized with real-time flow and water level monitoring equipment and data, the flow release logic can be adaptively adjusted without physical retrofit of the BMP's outlet.

A system, methodology, and programming logic for active stormwater controls to optimize sizing and design of Hydromodification Management (HM) structural Best Management Practices (BMPs) to achieve optimal flow duration control. Control logic enables the controlled release of stormwater from a BMP in a manner most akin to pre-development flow duration curves. Inputs to this logic include: flow duration curves based on continuous hydrologic simulation for pre- and post-development conditions; real-time measurement of water level within the BMP; and real-time measurement of discharge entering the BMP. This control logic can interact with control logic for other stormwater management objectives, such as harvest and reuse, infiltration, and combined sewer overflow prevention, and respective inputs, such as real-time weather forecast data, precipitation gage data, downstream flow gauge data, and water quality data, to meet those design objectives as well. New HM BMPs can be optimized to be smaller and, thus, more feasible to implement. Existing stormwater facilities designed for flood control or other management objectives can be retrofitted to provide hydromodification control as well. When utilized with real-time flow and water level monitoring equipment and data, the flow release logic can be adaptively adjusted without physical retrofit of the BMP's outlet.

Mitigating the reality gap through optimization of one or more simulated hardware parameters for simulated hardware components of a simulated robot. Implementations generate and store real navigation data instances that are each based on a corresponding episode of locomotion of a real robot. A real navigation data instance can include a sequence of velocity control instances generated to control a real robot during a real episode of locomotion of the real robot, and one or more ground truth values, where each of the ground truth values is a measured value of a corresponding property of the real robot (e.g., pose). The velocity control instances can be applied to a simulated robot, and one or more losses can be generated based on comparing the ground truth value(s) to corresponding simulated value(s) generated from applying the velocity control instances to the simulated robot. The simulated hardware parameters and environmental parameters can be optimized based on the loss(es).

The present disclosure describes system and methods for inferring energy usage at multiple levels of granularity. One embodiment describes an industrial automation system including a first industrial automation component, a first sensor coupled to the first industrial automation component, in which the first sensor measures a first amount of power supplied to the first industrial automation component, a second industrial automation component that couples to the first industrial automation component, and an industrial control system that infers energy usage by the first industrial automation component and the second industrial automation component based at least in part on the first amount of power supplied to the first industrial automation component.

A building management system includes a building efficiency improvement system and method configured to monitor and control subsystems and equipment for improved efficiency of operation. A user device is configured to display a user interface for monitoring and controlling one or more building equipment efficiency parameters and settings. The building efficiency management system further includes a controller configured to collect and analyze data from equipment, generate displays of the operational status and efficiency levels, generate sets of alternative equipment control algorithms based on efficiency objectives, and present users with a set of alternative equipment control algorithms displayed via graphic user interface elements on the user device. The user device further provides a means to select and implement an alternate equipment control algorithm. The controller is further configured to receive inputs from the user device commanding changes to equipment controls and process transactions associated with changes to equipment configuration.

A model-based control system is configured to select a desired parameter of a machinery configured to produce power and to output emissions and to select an emissions model configured to use the desired parameter as input and to output an emissions parameter. The model-based control system is additionally configured to tune the emissions model via a tuning system to derive a polynomial setpoint, and to control one or more actuators coupled to the machinery based on the polynomial setpoint.