Systems and methods provide a new paradigm of Advanced Process Control that includes building and deploying APC seed models. Embodiments provide automated data cleansing and selection in model identification and adaption in multivariable process control (MPC) techniques. Rather than plant pre-testing onsite for building APC seed models, the embodiments help APC engineers to build APC seed models from existing plant historical data with self-learning automation and pattern recognition, AI techniques. Embodiments further provide “growing” and “calibrating” the APC seed models online with non-invasive closed loop step testing techniques. PID loops and associated SP, PV, and OPs are searched and identified. Only “informative moves” data is screened, identified, and selected among a long history of process variables for seed model development and MPC application. The seed models are efficiently developed while skipping the costly traditional pre-testing steps and minimizing the interferences to the subject production process.

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.

The feedback control device takes information regarding a control deviation between a measured value and a target value of a controlled object as input, and outputs a control amount for the controlled object; comprising: a first control unit that takes information regarding the control deviation as input, and outputs a first control amount for the controlled object; a second control unit that takes information regarding the control deviation as input and outputs a second control amount for the controlled object, and in which a parameter for calculating the second control amount is determined by machine learning; an operation unit that operates the controlled object using the first control amount output from the first control unit and the second control amount output from the second control unit; and a sampling unit for thinning out at a predetermined period information regarding the control deviation input to the second control unit.

Aspects of the present invention disclose a method, computer program product, and system for monitoring a health status of a computing system. The method includes one or more processors deploying a respective monitoring prediction agent in each of a plurality of worker nodes of a computing system. The method further includes determining, for each of the plurality of worker nodes by the respective monitoring prediction agent, a single binary health status value by comparing a time-dependent function of performance metric data values of the respective worker node to upper and lower threshold values. The method further includes receiving the binary health status values together with respective identity information from each of the plurality of worker nodes. The method further includes generating a dataset indicative of a health status of the computing system by feeding the received respective identity information to hash functions of a Counting Bloom Filter.

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.

To improve the usability so that the result of adjustment of a plurality of axes operating in cooperation is easily evaluated. An adjustment apparatus displays a graph indicating a temporal change of measurement data for each control axis and a control instruction on the same time base in a first area in a screen, and displays a control trajectory of a control position of each control axis, the control instruction, and a target trajectory of a target position in the screen.

Aspects of the present invention disclose a method, computer program product, and system for monitoring a health status of a computing system. The method includes one or more processors deploying a respective monitoring prediction agent in each of a plurality of worker nodes of a computing system. The method further includes determining, for each of the plurality of worker nodes by the respective monitoring prediction agent, a single binary health status value by comparing a time-dependent function of performance metric data values of the respective worker node to upper and lower threshold values. The method further includes receiving the binary health status values together with respective identity information from each of the plurality of worker nodes. The method further includes generating a dataset indicative of a health status of the computing system by feeding the received respective identity information to hash functions of a Counting Bloom Filter.

The feedback control device takes information regarding a control deviation between a measured value and a target value of a controlled object as input, and outputs a control amount for the controlled object; comprising:

a first control unit that takes information regarding the control deviation as input, and outputs a first control amount for the controlled object; a second control unit that takes information regarding the control deviation as input and outputs a second control amount for the controlled object, and in which a parameter for calculating the second control amount is determined by machine learning;

an operation unit that operates the controlled object using the first control amount output from the first control unit and the second control amount output from the second control unit; and a sampling unit for thinning out at a predetermined period information regarding the control deviation input to the second control unit.

A method for controlling a heated process of an electric heater includes obtaining a setpoint variable indicating a target temperature of the heater. The method includes identifying an energy profile for the heater based on the setpoint variable. The energy profile provides a defined magnitude of initial electrical energy to be applied to the heater to have a temperature of the heated process reach the target temperature. The method includes obtaining a process variable indicating a performance characteristic of the heated process. The method includes providing electrical energy to the heater based on at least one of the energy profile and the process variable.

Systems and methods provide a new paradigm of Advanced Process Control that includes building and deploying APC seed models. Embodiments provide automated data cleansing and selection in model identification and adaption in multivariable process control (MPC) techniques. Rather than plant pre-testing onsite for building APC seed models, the embodiments help APC engineers to build APC seed models from existing plant historical data with self-learning automation and pattern recognition, AI techniques. Embodiments further provide “growing” and “calibrating” the APC seed models online with non-invasive closed loop step testing techniques. PID loops and associated SP, PV, and OPs are searched and identified. Only “informative moves” data is screened, identified, and selected among a long history of process variables for seed model development and MPC application. The seed models are efficiently developed while skipping the costly traditional pre-testing steps and minimizing the interferences to the subject production process.