A method includes receiving first data indicative of a range of values of a quality parameter of a type of manufacturing chamber component. Each value in the range of values meets one or more threshold criteria. The method further includes providing the first data to a physics-based model of a manufacturing chamber. The method further includes receiving, from the physics-based model, second data indicating a relationship between values of the quality parameter and predicted conditions in the manufacturing chamber. The method further includes determining, based on the relationship between values of the quality parameter and the predicted conditions, whether a new manufacturing chamber component of the manufacturing chamber component type is to be installed in the manufacturing chamber.

Described is system for tele-robotic operations over time-delayed communication links. Sensor data is acquired from at least one sensor for sensing surroundings of a robot having at least one robotic arm for manipulating an object. A three-dimensional model of the sensed surroundings is generated, and the sensor data is fit to the three-dimensional model. Using the three-dimensional model, a user demonstrates a movement path for the at least one robotic arm. A flow field representing the movement path is generated and combined with obstacle-repellent forces to provide force feedback to the user through a haptic device. The flow field comprises a set of parameters, and the set of parameters are transmitted to the robot to execute a movement of the at least one robotic arm for manipulating the object.

Sheetmaking cross-directional (CD) control requires a sophisticated model-based controller whose operation requires an accurate model of process behavior, but due to the complexity of the process, identifying these process models is challenging. Current techniques rely on open-loop process experimentation. Using non-causal scalar transfer functions for the steady-state CD process model and controller model avoid the problem of large dimensions associated with the CD process. These non-causal transfer functions can be represented by causal transfer functions that are equivalent to the non-causal ones in the sense of the output spectrum. A closed-loop optimal input design framework is proposed based on these causal equivalent models. CD actuators have responses in both sides along the cross direction which can be viewed as a non-causal behavior. Techniques to perform the non-causal modeling are demonstrated and developed in a closed-loop optimal input design framework based on non-causal modeling of the closed-loop CD process.

To accurately predict a sensor value even in the case where external force is received. A control apparatus according to the present disclosure includes a prediction section that, in an actuator including a torque sensor that detects torque generated at a driving shaft, and an encoder that detects a rotational angle of the driving shaft, predicts a detection value of the encoder on a basis of a detection value of the torque sensor, or predict the detection value of the torque sensor on a basis of the detection value of the encoder, and a trouble determination section that compares a prediction value predicted by the prediction section with an actually measured value of the torque sensor or the encoder to perform trouble determination on the torque sensor or the encoder.

Sheetmaking cross-directional (CD) control requires a sophisticated model-based controller whose operation requires an accurate model of process behavior, but due to the complexity of the process, identifying these process models is challenging. Current techniques rely on open-loop process experimentation. Using non-causal scalar transfer functions for the steady-state CD process model and controller model avoid the problem of large dimensions associated with the CD process. These non-causal transfer functions can be represented by causal transfer functions that are equivalent to the non-causal ones in the sense of the output spectrum. A closed-loop optimal input design framework is proposed based on these causal equivalent models. CD actuators have responses in both sides along the cross direction which can be viewed as a non-causal behavior. Techniques to perform the non-causal modeling are demonstrated and developed in a closed-loop optimal input design framework based on non-causal modeling of the closed-loop CD process.

Automated parameter tuning techniques for cross-directional model predictive control for paper-making under user-specified parametric uncertainties are developed. The CD-MPC design explicitly accounts for parametric model uncertainty while finding a value for the CD profile trajectory generation that minimizes the CD settling time of the measurement 2 sigma spread and does not exceed an overshoot limit for actuator 2 sigma spread. The inventive technique includes: (i) providing uncertainty specifications for the temporal parameters of the process model, (ii) specifying 2 sigma overshoot limits for the CD actuator profile, (iii) using robust stability theory to find a minimum bound for the profile trajectory tuning parameter, (iv) using a frequency domain technique to reduce the search range for the profile trajectory tuning parameter, and (v) performing an intelligent search for the tuning parameter that minimizes measurement 2 sigma settling time without exceeding the actuator 2 sigma overshoot limit.

To accurately predict a sensor value even in the case where external force is received. A control apparatus according to the present disclosure includes: a prediction section (260) configured to, in an actuator including a torque sensor that detects torque generated at a driving shaft, and an encoder that detects a rotational angle of the driving shaft, predict a detection value of the encoder on a basis of a detection value of the torque sensor, or predict the detection value of the torque sensor on a basis of the detection value of the encoder; and a trouble determination section (266) configured to compare a prediction value predicted by the prediction section with an actually measured value of the torque sensor or the encoder to perform trouble determination on the torque sensor or the encoder.

Controlling a multiple-array, sheetmaking cross-directional process with a multivariable model predictive controller (MPC) employs a cost function incorporating a prediction horizon. The MPC provides a measurement profile target reference trajectory over the prediction horizon of the MPC cost function. Improved CD-MPC performance is achieved by employing a measurement profile target reference trajectory over the prediction horizon in the MPC cost function. A series of target profiles creates a reference trajectory to bring the cross-direction measurements smoothly from their current profile to the final target. By carefully designing the reference trajectory, the CD-MPC exhibits a good measurement response without aggressive control action. The current measurement target profile can be filtered through a first order plus deadtime process at each controller update and repeating the filter operation once for each step of the MPC prediction horizon generates a full reference trajectory for the profile.

Controlling a multiple-array, sheetmaking cross-directional process with a multivariable model predictive controller (MPC) employs a cost function incorporating a prediction horizon. The MPC provides a measurement profile target reference trajectory over the prediction horizon of the MPC cost function. Improved CD-MPC performance is achieved by employing a measurement profile target reference trajectory over the prediction horizon in the MPC cost function. A series of target profiles creates a reference trajectory to bring the cross-direction measurements smoothly from their current profile to the final target. By carefully designing the reference trajectory, the CD-MPC exhibits a good measurement response without aggressive control action. The current measurement target profile can be filtered through a first order plus deadtime process at each controller update and repeating the filter operation once for each step of the MPC prediction horizon generates a full reference trajectory for the profile.

Automated parameter tuning techniques for cross-directional model predictive control for paper-making under user-specified parametric uncertainties are developed. The CD-MPC design explicitly accounts for parametric model uncertainty while finding a value for the CD profile trajectory generation that minimizes the CD settling time of the measurement 2 sigma spread and does not exceed an overshoot limit for actuator 2 sigma spread. The inventive technique includes: (i) providing uncertainty specifications for the temporal parameters of the process model, (ii) specifying 2 sigma overshoot limits for the CD actuator profile, (iii) using robust stability theory to find a minimum bound for the profile trajectory tuning parameter, (iv) using a frequency domain technique to reduce the search range for the profile trajectory tuning parameter, and (v) performing an intelligent search for the tuning parameter that minimizes measurement 2 sigma settling time without exceeding the actuator 2 sigma overshoot limit.