An environmental control system for a building including building equipment operable to affect a variable state or condition of the building. The system includes a controller including a processing circuit. The processing circuit can obtain training data relating to operation of the building equipment and can perform a system identification process to identify parameters of a system model using the training data. The processing circuit can augment the system model with a disturbance model and estimate values of a historical heat disturbance in the training data based on the augmented system model. The processing circuit can train one or more heat disturbance models based on the training data and the estimated values. The processing circuit can predict a heat disturbance using the augmented system model along with the one or more heat disturbance models and can control the building equipment based on the predicted heat disturbance.

An apparatus, including a plurality of devices, a network operations center (NOC), and a plurality of control nodes. Each device consumes a portion of the resource when turned on, and performs a corresponding function within an acceptable operational margin by cycling on and off. The NOC generates a plurality of run time schedules that coordinates run times for the each of the plurality of devices to control the peak demand of the resource. Each of the control nodes is coupled to a corresponding one of the devices. The plurality of control nodes transmits sensor data and device status to the NOC for generation of the plurality of run time schedules, and executes selected ones of the run time schedules to cycle the plurality of devices, and employs the sensor data and device status in a model to detect exceptions to normal operation of a facility.

A building management system (BMS) including a controller having an adaptive interaction manager and an agent manager. The system further includes one or more input-output (I/O) devices, the I/O devices in communication with the adaptive interaction manager. The controller further including a number of BMS field devices. The I/O devices are configured to receive an input from a user, and further configured to communicate the input to the adaptive interaction manager. The agent manager is configured to determine if one or more existing software agents are capable of performing the desired action, and to automatically transmit the existing software agents to one or more of the BMS field devices based on the agent manager determining the existing software agents are capable of performing the desired action. The software agents are configured to automatically be installed in a processing circuit of the BMS field device to perform the required action.

An electronic landing platform state module is configured to generate a state estimation of a platform surface at sea includes a plurality of electronic platform state process modules configured to receive an output from a respective spectral sensor. The plurality of electronic platform state process modules are further configured to output a monitored spectral platform state signal in response to applying a spectral process on a respective output. Each spectral process corresponds to a particular spectral modality of the respective spectral sensor. The electronic landing platform state module further includes an electronic platform state estimator module configured to determine a corrected dynamic state of the platform in response to fusing together the individual monitored spectral platform state signals.

According to an aspect, a method includes generating, by a computer processor, thermo-fluid parameter estimates of a thermal management system (TMS) of an engine based on sensed parameters and monitoring for TMS component failures based on the thermo-fluid parameter estimates and the sensed parameters. Thermo-mechanical parameter estimates are generated based on selected thermo-fluid parameters. Life usage estimates and life usage rate estimates are generated based on the selected thermo-fluid parameters and the thermo-mechanical parameter estimates. Life usage rate targets are generated based on external commands and the life usage estimates. Limits and goals are modified based on the life usage rate estimates, failure flags, and the life usage rate targets. A model predictive control is applied to command one or more TMS control components based on thermo-mechanical model parameters, the failure flags, and the limits and goals.

Provided are a flight simulation and control method of a unmanned aerial vehicle with regenerative fuel cells and solar cells for high altitude long endurance, and a control apparatus thereof. The high altitude long endurance simulation method for an unmanned aerial vehicle based on regenerative fuel cells and solar cells includes: a variable inputting step of inputting design variables of the unmanned aerial vehicle based on regenerative fuel cells and solar cells; a modeling step of performing modeling of the unmanned aerial vehicle based on regenerative fuel cells and solar cells using the design variables input in the variable inputting step; and an analyzing step of analyzing a modeling result in the modeling step to perform a high altitude long endurance simulation while controlling any one of the design variables input in the variable inputting step.

An apparatus includes a memory and one or more processors operably connected to the memory. The one or more processors are configured to receive data collected from a process facility system, detect anomalies for field device or process failures associated with the process facility system that are not monitored by alarms, detect leading indicators for field device or process failures that are monitored by alarms, and monitor the process facility system to detect further anomalies and leading indicators before failures occur.

A prediction device including: a recognizer recognizing a road structure and another vehicle in the vicinity of a subject vehicle; and a predictor predicting a running locus of the other vehicle recognized by the recognizer in the future on the basis of the road structure recognized by the recognizer in a predetermined situation, wherein, in the predetermined situation, in a case in which at least a part of the road structure used for predicting the running locus of the other vehicle in the future is not recognizable for the recognizer, the predictor predicts the running locus of the other vehicle in the future on the basis of a running locus of the other vehicle in the past acquired on the basis of a result of recognition in the past that is acquired by the recognizer.

A chemical system for an operation exhibiting steady-state gain inversion is provided herein and includes a reactor configured to receive a feed stream and produce an outlet stream to form a process and a control device configured to control a process. The control device receives inputs indicative of an operational parameter and output variables and, in response to the inputs and output variables, provides a steady-state manipulated input configured to control or optimize the process. The control device includes an input disturbance model, a state estimator, a non-linear steady-state target calculator, and a regulator configured to provide a signal for adjustment of one or more inputs based on the steady-state manipulated input and associated output variables.

A method for controlling a system using an explicit model predictive control (EMPC) evaluates, with respect to a state of the system, each inequality in a set of inequalities defining a set of regions of a state space of the system to produce a set of Boolean results. At least some of the inequalities are evaluated concurrently, and a size of the set of Boolean results equals a size of the set of inequalities. The method determines a region including the state by applying a Boolean function to elements of the set of Boolean results corresponding to inequalities forming boundaries of the region and determines a control for the system based on the state and a gain associated with the region. At least some Boolean functions are applied to corresponding elements concurrently after all elements in the set of Boolean results are evaluated.