Abstract: A transformable Unmanned Aerial Vehicle (UAV), can operate as a coplanar hexacopter or as an omnidirectional multirotor based on different operation modes. The UAV has 100% force efficiency for launching or landing tasks in the coplanar mode. In the omnidirectional mode, the UAV is fully actuated in the air for agile mobility in six degrees of freedom (DOFs). Models and control design are developed to characterize the motion of the transformable UAV. Simulation results are presented to validate the transformable UAV design and the enhanced UAV performance, compared with a fixed structure.

Abstract: Systems and methods for tracking EEG data and providing enhanced seizure detection and prediction are disclosed. The systems and methods use input sensors for receiving and collecting data from a plurality of EEG channels in association with a subject and processing said data to calculate and average Lyapunov exponents for a composite EEG data set. The systems and methods convert the average Lyapunov exponents into graphical representations that are displayed against a time axis. The graphical output adjusts in real-time according to the input data obtained from EEG channels. The systems and methods utilize pattern recognition to output alarms based upon input data and recommend diagnoses related to seizures.

Abstract: A method includes obtaining a process model representing an industrial process and obtaining controller specifications for an industrial process controller. The method also includes identifying a controller design having one or more parameters for the industrial process controller using the process model, an uncertainty associated with the process model, and the one or more parameters. The method further includes validating the controller design of the industrial process controller for use in a closed-loop control system and deploying the controller design if validated to the industrial process controller.

Abstract: This disclosure provides adaptive control techniques for pH control or control of other industrial processes. For example, in one method, a robust stability condition (RSC) value is determined during operation of a process controller, and a characteristic of the process controller is adaptively modified based on the RSC value. The RSC value provides an estimate of performance of the process controller in controlling the industrial process. In another method, one of multiple process controllers is selected based on RSC values associated with the process controllers, and one or more control signals are output from the selected process controller to an industrial process in order to control the industrial process. The RSC values provide estimates of performances of the multiple process controllers in controlling the industrial process.

Abstract: Systems and methods for tracking EEG data and providing enhanced seizure detection and prediction are disclosed. The systems and methods use input sensors for receiving and collecting data from a plurality of EEG channels in association with a subject and processing said data to calculate and average Lyapunov exponents for a composite EEG data set. The systems and methods convert the average Lyapunov exponents into graphical representations that are displayed against a time axis. The graphical output adjusts in real-time according to the input data obtained from EEG channels. The systems and methods utilize pattern recognition to output alarms based upon input data and recommend diagnoses related to seizures.

Abstract: This disclosure provides adaptive control techniques for pH control or control of other industrial processes. For example, in one method, a robust stability condition (RSC) value is determined during operation of a process controller, and a characteristic of the process controller is adaptively modified based on the RSC value. The RSC value provides an estimate of performance of the process controller in controlling the industrial process. In another method, one of multiple process controllers is selected based on RSC values associated with the process controllers, and one or more control signals are output from the selected process controller to an industrial process in order to control the industrial process. The RSC values provide estimates of performances of the multiple process controllers in controlling the industrial process.

Abstract: A method includes obtaining a process model representing an industrial process and obtaining controller specifications for an industrial process controller. The method also includes identifying a controller design having one or more parameters for the industrial process controller using the process model, an uncertainty associated with the process model, and the one or more parameters. The method further includes validating the controller design of the industrial process controller for use in a closed-loop control system and deploying the controller design if validated to the industrial process controller.

Abstract: One method includes obtaining a preliminary model associated with a system to be controlled and constructing a weighted model using one or more weighting factors. The method also includes identifying a final model of the system using the preliminary and weighted models, where the final model has a stability margin that is greater than an uncertainty associated with the final model. The method further includes controlling the system using a controller designed based on the final model. Another method includes identifying a first model associated with a system to be controlled, performing model order reduction to identify a second model, and controlling the system using a controller designed based on the second model. Performing the model order reduction includes reducing a weighted coprime factor model uncertainty between the first and second models.

Abstract: An apparatus, method, and computer program are provided for controller performance monitoring in a process control system. A level of disturbance associated with the process system is determined, and at least one value identifying a stability measure of a controller in the process system is determined using the determined level of disturbance and operating data associated with operation of the process system. The at least one value is compared to at least one threshold value, and a problem with the controller is identified based on the comparison. As an example, the process system could represent a product production system. Also, the operating data could include at least one of: measurement data from one or more sensors and control data for one or more actuators. The controller may be operable to receive the measurement data and generate the control data.

Abstract: One method includes obtaining a preliminary model associated with a system to be controlled and constructing a weighted model using one or more weighting factors. The method also includes identifying a final model of the system using the preliminary and weighted models, where the final model has a stability margin that is greater than an uncertainty associated with the final model. The method further includes controlling the system using a controller designed based on the final model. Another method includes identifying a first model associated with a system to be controlled, performing model order reduction to identify a second model, and controlling the system using a controller designed based on the second model. Performing the model order reduction includes reducing a weighted coprime factor model uncertainty between the first and second models.

Abstract: An apparatus, method, and computer program are provided for controller performance monitoring in a process control system. A level of disturbance associated with the process system is determined, and at least one value identifying a stability measure of a controller in the process system is determined using the determined level of disturbance and operating data associated with operation of the process system. The at least one value is compared to at least one threshold value, and a problem with the controller is identified based on the comparison. As an example, the process system could represent a product production system. Also, the operating data could include at least one of: measurement data from one or more sensors and control data for one or more actuators. The controller may be operable to receive the measurement data and generate the control data.

Abstract: A method to automatically tune control systems, the method comprising selecting a target loop transfer function for at least one plant subsystem in response to an input excitation, selecting at least one fit-error criterion for a plant subsystem transfer function, and automatically inputting the input excitation and tuning the plant subsystem transfer function to the target loop transfer function. The tuning includes automatically selecting a bandwidth of the plant subsystem transfer function from a plurality of bandwidths determined in accordance with limits of the plant subsystem, inputting the input excitation to the plant subsystem, determining the fit-error of the plant subsystem transfer function to the target loop transfer function in response to the input excitation, and continuing the selecting of a bandwidth in accordance with the fit-error criterion.

Abstract: A automated run-to-run controller for controlling manufacturing processes comprises set of processing tools, a set of metrology tools for taking metrology measurements from the processing tools, and a supervising station for managing and controlling the processing tools. The supervising station comprises an interface for receiving metrology data from the metrology tools and a number of variable parameter tables, one for each of the processing tools, collectively associated with a manufacturing process recipe. The supervising station also includes one or more internal models which relate received metrology data to one or more variables for a processing tool, and which can modify variables stored in the variable parameter table to control the process tools using feedback and/or feed-forward control algorithms. Feed-forward control algorithms may, in certain embodiments, be used to adjust process targets for closed loop feedback control.

Abstract: A temperature control system for a thermal reactor is disclosed that addresses many of the problems in the art. In accordance with one aspect of the disclosed control system, a plurality of temperature controllers are employed. Each temperature controller employs one or more dynamic models that are optimized for a given temperature range. The temperature range over which a particular controller is optimized is preferably generally exclusive of the temperature ranges over which other controllers of the plurality of temperature controllers are optimized. In accordance with another aspect of the control system, the control system employs enhanced ramp trajectory logic. In accordance with another aspect of the control system, the control system employs a virtual temperature sensor in the event of a hardware failure of the corresponding non-virtual temperature sensor.

Abstract: A temperature control system for a thermal reactor is disclosed that addresses many of the problems in the art. In accordance with one aspect of the disclosed control system, a plurality of temperature controllers are employed. Each temperature controller employs one or more dynamic models that are optimized for a given temperature range. The temperature range over which a particular controller is optimized is preferably generally exclusive of the temperature ranges over which other controllers of the plurality of temperature controllers are optimized. In accordance with another aspect of the control system the control system employs enhanced ramp trajectory logic. In accordance with another aspect of the control system, the control system employs a virtual temperature sensor in the event of a hardware failure of the corresponding non-virtual temperature sensor.

Abstract: A temperature control system for a thermal reactor is disclosed that addresses many of the problems in the art. In accordance with one aspect of the disclosed control system, a plurality of temperature controllers are employed. Each temperature controller employs one or more dynamic models that are optimized for a given temperature range. The temperature range over which a particular controller is optimized is preferably generally exclusive of the temperature ranges over which other controllers of the plurality of temperature controllers are optimized. In accordance with another aspect of the control system, the control system employs enhanced ramp trajectory logic. In accordance with another aspect of the control system, the control system employs a virtual temperature sensor in the event of a hardware failure of the corresponding non-virtual temperature sensor.

Abstract: A temperature control system for a thermal reactor is disclosed that addresses many of the problems in the art. In accordance with one aspect of the disclosed control system, a plurality of temperature controllers are employed. Each temperature controller employs one or more dynamic models that are optimized for a given temperature range. The temperature range over which a particular controller is optimized is preferably generally exclusive of the temperature ranges over which other controllers of the plurality of temperature controllers are optimized. In accordance with another aspect of the control system, the control system employs enhanced ramp trajectory logic. In accordance with another aspect of the control system, the control system employs a virtual temperature sensor in the event of a hardware failure of the corresponding non-virtual temperature sensor.

Abstract: Controllers and associated methods for controlling a thermal reactor or other thermal semiconductor processors which include a heating element powered by a power source, and having profile thermocouples and spike thermocouples. One preferred method comprises the following steps: modeling thermal dynamic characteristics of the thermal reactor, the modeling step including providing thermocouple instrumented wafers in the thermal reactor, perturbing the thermal reactor by controlling the heating element using a stimulation sequence, and developing models based on changes in temperature created by the perturbations. The models are developed off-line and can include one or more models, such as a model of power versus spike thermocouple readings, a model of spike thermocouple readings versus profile thermocouple readings, and a model of profile thermocouple readings versus thermocouple instrumented wafer readings.