The present disclosure relates to a method and system for integrated path planning and path tracking control of an autonomous vehicle. The method includes: obtaining five input control variables and eleven system state variables of an autonomous vehicle at current time; constructing a vehicle path planning-tracking integrated state model according to the obtained variables at the current time; enveloping external contours of two autonomous vehicles using elliptical envelope curves to determine elliptical vehicle envelope curves of the two autonomous vehicles, respectively; determining time to collision (TTC) between the vehicles according to elliptical vehicle envelope curves and vehicle driving states; establishing an objective function of a model prediction controller (MPC) according to the model; and solving the objective function based on the TTC, and determining input control variables to the MPC at the next time. Autonomous vehicle collision avoidance can be achieved according to the present disclosure.

A transport control device includes a state predictor that is machine-learned to output, for each candidate of a setting value of a parameter to control the operation of a transport system, state prediction information indicating a predicted state of the transport system associated with a second period after a first period, and a determinator that determines state prediction information associated with a prediction target period for each candidate of the setting value of the parameter, and determines the setting value of the parameter to be applied to the transport system in the prediction target period based on an evaluation result of the state prediction information for each candidate of the setting value of the parameter.

Disclosed are systems, servers and methods for improving temperature profile control in a reactor with at least three fixed beds, exothermic reactions and interstage cooling. A model of the temperature differential across the first bed is developed and its error is used to infer unmeasured feed composition disturbances, which are used in the control of the downstream fixed beds for faster response to unmeasured feed composition changes and improved control of the temperature profile throughout the reactor. The first bed model error is then used as an input into an overall model that predicts reactor temperature profiles, which provides advanced notice of reactions in downstream beds, and enables efficient adjustment and compensation to a feed composition change. A Model Predictive Control (MPC) algorithm is applied to adjust the bed intercooling and first bed feed temperature so that the reactor temperature profile can be more precisely controlled.

A method includes receiving, at a server, first sensor data from a first vehicle. The method includes receiving, at the server, second sensor data from a second vehicle. The second sensor data includes condition data indicating a road condition. The method includes aggregating, at the server, a plurality of sensor readings to generate aggregated sensor data. The plurality of sensor readings include the first sensor data and the second sensor data. The method further includes transmitting a first message based on the aggregated sensor data to the first vehicle, wherein the first message causes the first vehicle to perform a first action, the first action comprising avoiding the road condition, displaying an indicator corresponding to the engine problem, displaying a booked route, or a combination thereof.

The invention relates to a method for predicting flooding in a distillation column by machine learning including a constructing and training phase of a machine learning model obtained from previously collected data and from a set of sensors, an operational phase for predicting flooding(s), by collecting a current data flow until a buffer is filled, pre-processing data from the data buffer by predetermined cleansing and classification, synchronizing the data of the current set of clean and classified data, determining a value of a current variable representative of at least one current performance of the at least one distillation column, forming a current set of transformed data by calculating predetermined derivatives, and predicting the current state of said distillation column by applying said learning model to said current set of transformed data.

A method including: obtaining one or more models configured for predicting a process metric of a manufacturing process based on inputting process data; and using a reinforcement learning framework to evaluate the one or more models and/or model configurations of the one more models based on inputting new process data to the one or more models and determining a performance indication of the one or more models and/or model configurations in predicting the process metric based on inputting the new process data.

In order to test an autonomous behavior controller for a technical system, the following are input: a machine model for physically simulating the technical system; an environment model modelling an environment of the technical system; as well as a disruption model modelling potential disruptions in the environment. Disruption data is generated by means of the disruption model, and the environment model is modified according to the disruption data. Environment-specifically simulated sensor data the technical system is then generated by means of the modified environment model and the machine model. According to the simulated sensor data, control data is generated for the technical system by the autonomous behavior controller. An operating behavior of the technical system induced by the control data is then simulated by means of the machine model. Furthermore, a performance value quantifying the operating behavior is determined and output as a test result.

A controller for a technical system is trained using a machine learning method. For this purpose, a chronological sequence of training data is detected for the machine learning method, the training data including both state data as well as control action data of the technical system. A change in the control action data over time is detected specifically and correlated with changes in the state data over time within different time windows, wherein a time window specific correlation value is ascertained in each case. A resulting time window is then ascertained on the basis of the ascertained correlation values, and the training data which is found within the resulting time window is extracted in a time window-specific manner. The controller is trained by means of the machine learning method using the extracted training data and thereby configured to control the technical system.

A method for prioritizing predictive model data streams includes receiving, by a first device, a plurality of predictive model data streams. Each predictive model data stream includes a set of model parameters for a corresponding predictive model. Each predictive model is trained to predict future data values of a data source. The method includes prioritizing, by the first device, priorities to each of the plurality of predictive model data streams. The method includes selecting at least one of the predictive model data streams based on a corresponding priority. The method includes parameterizing, by the first device, a predictive model using the set of model parameters included in the selected predictive model data stream. The method includes predicting, by the first device, future data values of the data source using the parameterized predictive model.

Process recipe data associated a process to be performed for a substrate at a process chamber is provided as input to a trained machine learning model. A set of process recipe settings for the process that minimizes scratching on one or more surfaces of the substrate is determined based on one or more outputs of the machine learning model. The process is performed for the substrate at the process chamber in accordance with the determined set of process recipe settings.