Abstract: A method for tuning flatness control for rolling a strip in a mill including rolls controllable by means of a plurality of actuators, which mill is modeled by means of a mill matrix. The method includes: a) obtaining an equivalent movement range for each actuator, b) determining a scaled mill matrix by scaling the mill matrix based on the equivalent movement ranges, and c) obtaining a singular value decomposition of the scaled mill matrix for providing flatness control of the strip by means of the actuators. A computer program and a control system for carrying out the above method are also presented herein.

Abstract: A method for inspecting a solar panel of a solar power station is performed in a controller for an unmanned aerial vehicle, UAV, and includes the steps of: receiving an inspection request for a subset of the solar panels navigating, in a first stage, using radio signals, the UAV to an initial location in a vicinity of a particular solar panel of the subset of solar panels; positioning, in a second stage, the UAV using at least one near field sensor of the UAV; and capturing, using the infrared camera, an image of the particular solar panel.

Abstract: A method for inspecting a solar panel of a solar power station is performed in a controller for an unmanned aerial vehicle, UAV, and includes the steps of: receiving an inspection request for a subset of the solar panels navigating, in a first stage, using radio signals, the UAV to an initial location in a vicinity of a particular solar panel of the subset of solar panels; positioning, in a second stage, the UAV using at least one near field sensor of the UAV; and capturing, using the infrared camera, an image of the particular solar panel.

Abstract: A method of providing flatness control for rolling a strip in a mill including a plurality of rolls controllable by actuators. The method includes the steps of: receiving flatness measurement data pertaining to a flatness of the strip; determining a flatness error as a difference between a reference flatness of the strip and the flatness measurement data; determining an adjusted flatness error based on the flatness error and weights for actuator position combinations which provide a flatness effect below a threshold value; and utilizing the adjusted flatness error for the control units to control the actuators to thereby control the flatness of the strip. A computer program product and a control system for carrying out the above method are also presented herein.

Abstract: A method for tuning flatness control for rolling a strip in a mill including rolls controllable by means of a plurality of actuators, which mill is modeled by means of a mill matrix. The method includes: a) obtaining an equivalent movement range for each actuator, b) determining a scaled mill matrix by scaling the mill matrix based on the equivalent movement ranges, and c) obtaining a singular value decomposition of the scaled mill matrix for providing flatness control of the strip by means of the actuators. A computer program and a control system for carrying out the above method are also presented herein.

Abstract: A method of providing flatness control for rolling a strip in a mill including a plurality of rolls controllable by actuators. The method includes the steps of: receiving flatness measurement data pertaining to a flatness of the strip; determining a flatness error as a difference between a reference flatness of the strip and the flatness measurement data; determining an adjusted flatness error based on the flatness error and weights for actuator position combinations which provide a flatness effect below a threshold value; and utilizing the adjusted flatness error for the control units to control the actuators to thereby control the flatness of the strip. A computer program product and a control system for carrying out the above method are also presented herein.

Abstract: A method and a device for tuning and control of industrial processes having varying material flow rate. An adder is configured to add excitation signals to the controller output signal. A measurement system is configured to measure a property in response to the excitation signals. A model based tuning unit is adapted to estimate the value of at least one parameter with unknown value of a process model structure describing the effect of varying material flow rate, based on the measurements of the property and the output signal from the controller, and to calculate a model that describes the dynamics from controller output to controller input based on the estimated value of the parameter, and to perform model based tuning of the controller based on the model that describes the dynamics from controller output to controller input.

Abstract: A method and a device for tuning and control of industrial processes having varying material flow rate. An adder is configured to add excitation signals to the controller output signal. A measurement system is configured to measure a property in response to the excitation signals. A model based tuning unit is adapted to estimate the value of at least one parameter with unknown value of a process model structure describing the effect of varying material flow rate, based on the measurements of the property and the output signal from the controller, and to calculate a model that describes the dynamics from controller output to controller input based on the estimated value of the parameter, and to perform model based tuning of the controller based on the model that describes the dynamics from controller output to controller input.

Abstract: A methodology for process modeling and control and the software system implementation of this methodology, which includes a rigorous, nonlinear process simulation model, the generation of appropriate linear models derived from the rigorous model, and an adaptive, linear model predictive controller (MPC) that utilizes the derived linear models. A state space, multivariable, model predictive controller (MPC) is the preferred choice for the MPC since the nonlinear simulation model is analytically translated into a set of linear state equations and thus simplifies the translation of the linearized simulation equations to the modeling format required by the controller. Various other MPC modeling forms such as transfer functions, impulse response coefficients, and step response coefficients may also be used. The methodology is very general in that any model predictive controller using one of the above modeling forms can be used as the controller.