The present invention is a control device which includes a filter unit for performing an attenuation process at a predetermined frequency on a control input based on a predetermined target command, generates the control input through model predictive control executed by a model predictive control unit and causes an output of a predetermined control object to follow the predetermined target command. A prediction model defines a correlation between the control input and predetermined extended state variables including a state variable related to a predetermined control object and a predetermined filter state variable related to the filter unit, and a predetermined evaluation function for model predictive controls configured to calculate a state quantity cost that is a stage cost with respect to state variables except the predetermined filter state variable among the predetermined extended state variables, and a control input cost that is a stage cost related to the control input.

Disclosed are a method for automatically diagnosing and controlling an apparatus in an intelligent building and relevant device. The method includes: performing, based on historical data of working parameters of multiple apparatuses, an abnormal diagnosis on received real-time data of the working parameters; determining an abnormal apparatus; selecting a neural network predictive control model corresponding to the abnormal apparatus; selecting one piece of non-abnormal data which has a same parameter type as that of the abnormal data and is close to the current abnormal data in time as a predictive control target, and determining a predictive control data that can cause an output matching the predictive control target; and controlling the abnormal apparatus according to the predictive control data. The automatic diagnosis and automatic control of an apparatus in an intelligent building are realized, meanwhile the safe and efficient operation of all apparatuses in an intelligent building is ensured.

A method or system for adaptive vehicle spacing, including determining a current state of a vehicle based on sensor data captured by sensors of the vehicle; for each possible action in a set of possible actions: (i) predicting based on the current vehicle state a future state for the vehicle, and (ii) predicting, based on the current vehicle state a first zone future safety value corresponding to a first safety zone of the vehicle; and selecting, based on the predicted future states and first zone future safety values for each of the possible actions in the set, a vehicle action.

A boom sprayer includes any number of components to treat plants as the boom sprayer travels through a plant field. The components take actions to treat plants or facilitate treating plants. The boom sprayer includes any number of sensors to measure the state of the boom sprayer as the boom sprayer treats plants. The boom sprayer includes a control system to generate actions for the components to treat plants in the field. The control system includes an agent executing a model that functions to improve the performance of the boom sprayer treating plants. Performance improvement can be measured by the sensors of the boom sprayer. The model is an artificial neural network that receives measurements as inputs and generates actions that improve performance as outputs. The artificial neural network is trained using actor-critic reinforcement learning techniques.

A method for automating system identification includes performing a system identification experiment, and performing a system identifying processing by fitting a model to data from the system identification experiment. The method also includes performing model reduction to generate a model numerically suitable for controller synthesis by removing inconsequential states that cause controller optimization methods to fail. The method further includes performing control synthesis using the generated model or reduced models, including disturbance spectrum estimates, to generate a candidate controller design to be used during system operation. The method also includes checking for controller robustness using the identified model to ensure stability of the system while maximizing closed-loop bandwidth and performance.

This disclosure relates generally to a method and system for real time monitoring and forecasting of fouling of an air preheater (APH) in a thermal power plant. The system is deploying a digital replica or digital twin that works in tandem with the real APH of the thermal power plant. The system receives real-time data from one or more sources and provides real-time soft sensing of intrinsic parameters as well as that of health, fouling related parameters of APH. The system is also configured to diagnose the current class of fouling regime and the reasons behind a specific class of fouling regime of the APH. The system is also configured to be used as advisory system that alerts and recommends corrective actions in terms of either APH parameters or parameters controlled through other equipment such as selective catalytic reduction or boiler or changes in operation or design.

A model is generating for predicting energy workloads to adjust electrical energy supply to meet varying short-term energy demands at a microcosm level. A model is developed, using a computer, to facilitate predicting energy workloads for adjusting energy supplies to meet an energy demand. The model includes receiving, at the computer, input parameters of dynamic values of workloads as historical data, and generating a predictive model by analyzing the input parameters. The model further includes predicting short-term energy demands based on the predictive model, the predicted short-term energy demands include identifying a predicted peak value. Also, the model includes initiating short term energy output in an electrical grid to a transformer level component in the electrical grid based on the predicted short term energy demands.

A system for climate control comprises a controller, comprising a processor and a non-transitory computer-readable medium with instructions stored thereon, a plurality of sensors communicatively connected to the controller, and at least one HVAC component communicatively connected to the controller, wherein the instructions, when executed by the processor, perform steps comprising receiving sensor data from at least one sensor of the plurality of sensors, executing a machine learning model using the received sensor data as inputs, calculating a predicted temperature, humidity, or occupancy state from the machine learning model, and sending a control instruction to the at least one HVAC component based on the calculated temperature, humidity, or occupancy state. A method for training a machine learning algorithm for a climate control system and a method for HVAC control in a building are also described.

The present disclosure is directed to systems and methods for economically optimal control of an electrical system. Some embodiments employ generalized multivariable constrained continuous optimization techniques to determine an optimal control sequence over a future time domain in the presence of any number of costs, savings opportunities (value streams), and constraints. Some embodiments also include control methods that enable infrequent recalculation of the optimal setpoints. Some embodiments may include a battery degradation model that, working in conjunction with the economic optimizer, enables the most economical use of any type of battery. Some embodiments include techniques for load and generation learning and prediction. Some embodiments include consideration of external data, such as weather.

Environmental data of an environment of the building and cooling load demand data are received as first training data, which are used for training a first machine learning model to predict a cooling load demand from environmental data. Furthermore, control signals for the chiller plant and cooling power data resulting from applying the control signals to the chiller plant are received as second training data which are used for training a second machine learning model to predict a cooling power from control signals. Actual environmental data are received, from which a cooling load demand is predicted by the trained first machine learning model. Furthermore, candidate control signals for the chiller plant are generated, and from which a resulting cooling power is predicted by the trained second machine learning model. From the candidate control signals, applicable control signals are selected for which a predicted cooling power fulfills the predicted cooling load demand.