A model-based control method includes: (a) acquiring temperature control data including temperature data of each of a plurality of zones of a temperature control member provided in a processing apparatus, temperature of each of the plurality of zones being individually controllable; (b) for each zone, specifying a temperature of another zone that is weight-averaged by a weighting coefficient determined according to a magnitude of heat transfer with the another zone; (c) for each zone, specifying a parameter of a state-space model of multi-input/single-output using the specified temperature of the another zone and the temperature control data; (d) creating a state-space model of multi-input/multi-output by assigning the specified parameter of the state-space model of multi-input/single-output to each element of the state-space model of multi-input/multi-output; and (e) controlling the temperature of each of the plurality of zones of the temperature control member using the state-space model of multi-input/multi-output.

Techniques for generating simulations for evaluating a performance of a controller of an autonomous vehicle are described. A computing system may evaluate the performance of the controller to navigate the simulation and respond to actions of one or more objects (e.g., other vehicles, bicyclists, pedestrians, etc.) in a simulation. Actions of the objects in the simulation may be controlled by the computing system (e.g., by an artificial intelligence) and/or one or more users inputting object controls, such as via a user interface. The computing system may calculate performance metrics associated with the actions performed by the vehicle in the simulation as directed by the autonomous controller. The computing system may utilize the performance metrics to verify parameters of the autonomous controller (e.g., validate the autonomous controller) and/or to train the autonomous controller utilizing machine learning techniques to bias toward preferred actions.

Systems and methods include a computer-implemented method for optimizing well drilling operations. An estimate for a maximum safe rate of penetration (ROP) is determined based on cutting concentrations in annulus (CCA) values. Hydraulics of mud pump+bit and jet impact force hydraulics are evaluated. A developed hole cleaning index is determined based on a carrying capacity model considering chemical and physical influences of drilling fluid rheology. A real-time Drilling Specific Energy (DSE) is determined using the estimate for the maximum safe ROP, the evaluated hydraulics, and the developed hole cleaning index. Optimal drilling parameters are determined using particle swarm optimization (PSO) and a penalty approach (PA). Optimal mechanical drilling parameters are determined using the optimal drilling parameters and by evaluating the real-time DSE. The optimal mechanical drilling parameters are used during drilling. The real-time developed DSE is correlated with fuel consumption to assess CO.sub.2 and toxics gases emission.

A tracking system for a digital Phase Locked Loop (PLL), the tracking system including a PLL model configured to emulate an actual internal PLL signal, wherein the emulation is based on another internal PLL signal received from the digital PLL and on an estimated analog PLL parameter of the PLL model; and a tracker configured to compare the emulated internal PLL signal with the actual internal PLL signal, and to update the estimated analog PLL parameter according to a minimization algorithm that minimizes a result of the comparison.

A method for collecting information required for Bode plot creation of a UAV (Unmanned Aerial Vehicle) autopilot system is provided. The method comprises: creating a Bode plot generation input signal: adding the Bode plot generation input signal to control inputs; collecting data from multiple points within the control system; calculating magnitude and phase at the multiple points using the data collected; recording the magnitude and phase for the multiple points in a datalog; comparing the magnitude and phase for the multiple points to calculate the gain and phase margins for open loop responses in the control system; creating a Bode plot for at least one of the following: i) a closed loop response of the attitude and/or rate loops, ii) an open loop response of the attitude and/or rate loops and iii) a response of the UAV; and outputting the Bode plot.

A non-transitory computer-readable medium comprising computer-executable instructions that, when executed, are configured to cause a processor to perform operations that include receiving operational parameters for one or more automation devices, wherein the one or more automation devices are configured to implement control logic generated based on a decision tree. The operations also include receiving an output by the decision tree based on the operational parameters. Further, the operations include determining the output is an anomalous output based on a constraint associated with the decision tree. Further still, the operations include generating an updated decision tree based on the anomalous output. Even further, the operations include generating updated control logic for the one or more automation devices based on the updated decision tree. Even further, the operations include sending the updated control logic to the one or more automation devices.

A model learning unit learns a prediction model on the basis of learning data, a target setting unit sets a target output parameter value by interpolating between a goal output parameter value and an output parameter value which is the closest to the goal output parameter value in output parameter values in the learning data, a processing condition search unit estimates input parameter values which corresponds to the goal output parameter value and the target output parameter value, a model learning unit updates the prediction model by using a set of the estimated input parameter value and an output parameter value which is a result of processing that a processing device performs as additional learning data.

A method evaluates a trained data-based evaluation model for determining a model output for controlling, regulating, operating, or monitoring a technical system with periodically determined input data sets. The method includes recording input data sets for a predetermined number of time-sequential scanning steps, and aggregating the input data sets into an input data package of validated input data sets. The method further includes determining an evaluation result for each of the input data sets in the input data package using the trained data-based evaluation model. Upon each evaluation, one or more model parameters of the trained data-based evaluation model are reduced by an amount or set to 0. The method is further configured to aggregate the evaluation results to obtain the model output.

A system for performing projective tests includes a web server, a database server, and an artificial intelligence (AI) server. The web server is coupled with an electronic data network and configured to provide a man-machine interface via the electronic data network to a remote client. The database server manages test and training data and is coupled with the web server. The AI server is coupled with the web server and the database server, and configured to execute one or more AI algorithms. The man-machine interface provides administrative tools to control a content of at least one projective test where the content may include at least one projective stimulus comprising at least one of an image, a video, an audio file, and a text file. The man-machine interface provides administrative tools that control a display associated with the projective test. The man-machine interface includes a plurality of web pages for providing interactive displays that allow a remote client to view and execute the projective test. The projective test includes an interactive display component for selecting a portion of projective stimuli and an interactive prompt configured to allow entry of additional data related to the selected portion. The system executes an AI algorithm to generate a score based on the selected portion and the response to the prompt. The system executes a second AI algorithm to associate characteristics to a user based on the selected portion of the projective stimuli, the response to the prompt, and scores from the past AI algorithm. The man-machine interface includes a plurality of web pages for providing interactive displays that allow a remote client to view and engage with their predicted scores and characteristics.

A cascaded MPC system includes an upper tier controller and lower tier controller having stored constraints for controlling a process having manipulated variables (MVs), controlled variables (CVs), and conjoint manipulated variable (CMV). The upper tier passes a target value for the CMV to the lower tier which optimizes for determining a local optimal operating point for the MVs, CVs, and CMV, moves towards the target value starting at the CMVs local operating point, and optimizes for identifying of the constraints as selected constraint(s) when the moving is truncated, passes the selected constraint(s) to the upper tier which performs an overall optimization for the process using the selected constraint to generate an optimal value for the CMV that lower tier uses as a new CM V target value for redetermining updated local optimal operating points for the MVs, CVs, and CMV, and for controlling the process utilizing the updated operating points.