Systems, apparatuses, methods, and computer program products are provided herein. For example, a method included herein includes receiving monitoring data representing operations of a data publisher, a data processing unit, and a database. In some embodiments, the method may include determining a first data processing error associated with the data publisher and the data processing unit based at least in part on the monitoring data. In some embodiments, the method may include determining a second data processing error associated with the data processing unit and the database. In some embodiments, the method may include causing a first controller to control the data publisher or the data processing unit. In some embodiments, the method may include causing a second controller to control the data processing unit or the database based at least in part on the second data processing error.

Systems and methods for dynamic predictive control of autonomous vehicles are disclosed. In one aspect, an in-vehicle control system for a semi-truck includes one or more control mechanisms configured to control movement of the semi-truck and a processor. The system further includes computer-readable memory in communication with the processor and having stored thereon computer-executable instructions to cause the processor to receive a desired trajectory and a vehicle status of the semi-truck, determine a dynamic model of the semi-truck based on the desired trajectory and the vehicle status, determine at least one quadratic program (QP) problem based on the dynamic model, generate at least one control command for controlling the semi-truck by solving the at least one QP problem, and provide the at least one control command to the one or more control mechanisms.

A method of automatically tuning a proportional-integral-derivative (PID) controller (102) controlling a DC-DC converter (100) includes substituting the PID controller (102) with a modified relay feedback test (MRFT) (104) controller having a threshold parameter () and a magnitude parameter (h), iteratively determining a current error signal (e[k]) by measuring an output voltage (Vo[k]) of the DC-DC converter (100) and comparing it to a desired reference output voltage (Vo-ref), input the error signal (e[k]) to the MRFT (104), determining an output value (u[k]) of the MRFT, update the duty cycle (D) based on the output value (u[k]), operating the DC-DC converter (100) using the updated duty cycle (D), exciting oscillations in the loop containing the MRFT (104) and the DC-DC converter (100), measuring a frequency (0) and amplitude (0) of the output voltage (Vo[k]), updating PID controller parameters based on the frequency (0) and amplitude (0), and substituting the MRFT (104) with the PID controller (102).

An aerosol provision device includes a housing; a consumable including aerosol-generating material. The aerosol provision device or the consumable includes an aerosol generator to generate an aerosol from the aerosol-generating material. The device includes a pressure sensor, a temperature sensor, and a high-side load switch coupled to or coupleable with the aerosol generator, and processing circuitry coupled to the high-side load switch, the pressure sensor, and the temperature sensor. The processing circuitry is configured to output a modulated signal with an adjustable duty cycle to cause the high-side load switch to connect and disconnect power to the aerosol generator. The processing circuitry is configured to implement a proportional-integral-derivative (PID) algorithm to adjust the duty cycle based on the processing circuitry determining whether the measurements of temperature or the measurements of pressure take precedence for implementing the PID algorithm to adjust the duty cycle.

An industrial controller implements a closed-loop control technique referred to as generic PID control, or GPID, which makes explicit the basic principles and methods of quantitatively combining the past, present and future in controller design and tuning. Generic PID control is backward compatible in design and in tuning with current industrial control software interfaces, and as such can be easily adopted onto existing control systems. Generic PID is also widely applicable to artificial intelligence (AI) and data analytics, such as machine learning, where error-correction is core to all algorithms.

Various embodiments of the present disclosure provide parameter interpretation techniques for providing predictive insights for complex parameter combinations within a prediction domain. The techniques may include receiving an input parameter and one or more historical parameters for an entity. The techniques may include identifying, using a partial information decomposition (PID) data source, a plurality of PID scores based on the input parameter and the one or more historical parameters. The techniques may include determining an adverse outcome prediction based on the plurality of PID score and, in response to the adverse outcome prediction, initiating the performance of a prediction-based action.

An aerosol provision device includes a housing; a consumable including aerosol-generating material. The aerosol provision device or the consumable includes an aerosol generator to generate an aerosol from the aerosol-generating material. The device includes a pressure sensor, a temperature sensor, and a high-side load switch coupled to or coupleable with the aerosol generator, and processing circuitry coupled to the high-side load switch, the pressure sensor, and the temperature sensor. The processing circuitry is configured to output a modulated signal with an adjustable duty cycle to cause the high-side load switch to connect and disconnect power to the aerosol generator. The processing circuitry is configured to implement a proportional-integral-derivative (PID) algorithm to adjust the duty cycle based on the processing circuitry determining whether the measurements of temperature or the measurements of pressure take precedence for implementing the PID algorithm to adjust the duty cycle.

An apparatus and method for thermal hardware assist. For example, one embodiment of a processor comprises: a plurality of functional circuit blocks; an interconnect coupled to the plurality of functional circuit blocks; one or more physical or virtual temperature sensors to capture one or more skin temperature measurements; a power management controller to execute a dynamically adjustable thermal control loop to perform an evaluation of the one or more skin temperature measurements based on a corresponding one or more temperature control values and to responsively distribute power to the plurality of functional circuit blocks in accordance with the evaluation, the power management controller to dynamically adjust parameters of the thermal control loop based on measured environmental conditions.

This invention disclosure presents a mass flow controller (MFC) with a dual-mode proportional-integral-derivative (PID) control loop. In training mode, the PID loop determines solenoid coil current setpoints for various operating states stipulated by a process recipe, storing them for later retrieval. During the execution of a semiconductor manufacturing process, the stored setpoints enable rapid flow delivery without continuous PID control. Real-time flow rate monitoring and Statistical Process Control (SPC) ensure stability, triggering retraining if necessary, enhancing MFC speed, accuracy, and reliability.