The present invention discloses a networked control system (NCS) time-delay compensation method based on predictive control. The method comprises the following steps: (1) acquiring random time-delay data in an NCS, and preprocessing the data; (2) predicting the current time-delay by using a fuzzy neural network (FNN) optimized by a particle swarm optimization (PSO) algorithm; (3) compensating the predicted time-delay by using an implicit proportional-integral-based generalized predictive control (PIGPC) algorithm; (4) determining whether a preset work end time is up according to a clock in the NCS; if yes, ending the process; if no, returning to step (2). The method disclosed by the present invention can accurately predict and effectively compensate the NCS time-delay and has excellent development prospect.

A chemical system for an operation exhibiting steady-state gain inversion is provided herein and includes a reactor configured to receive a feed stream and produce an outlet stream to form a process and a control device configured to control a process. The control device receives inputs indicative of an operational parameter and output variables and, in response to the inputs and output variables, provides a steady-state manipulated input configured to control or optimize the process. The control device includes an input disturbance model, a state estimator, a non-linear steady-state target calculator, and a regulator configured to provide a signal for adjustment of one or more inputs based on the steady-state manipulated input and associated output variables.

A cooperative fuzzy-neural control method is designed in this present invention. Due to the difficulty for cooperatively controlling the concentrations of the dissolved oxygen and nitrate nitrogen in wastewater treatment process, a cooperative fuzzy-neural control method is investigated. In this proposed method, firstly, a interval type-2 fuzzy neural network is employed to construct the cooperative fuzzy-neural controller. Secondly, a parameter cooperative strategy is proposed to cooperatively optimize the global and local parameters of the cooperative fuzzy-neural controller to meet the control requirements. This proposed cooperative fuzzy-neural control method can cooperatively control the concentrations of the dissolved oxygen and nitrate nitrogen in wastewater treatment process. The results illustrate that the proposed cooperative fuzzy-neural control method can achieve the high control accuracy and guarantee the normal operations of wastewater treatment process under the different operation conditions.

Arrangements for dynamic system control evaluation and issue identification and mitigation are provided. In some examples, data may be received from a plurality of sources. The data may be received in batches at predetermined intervals or time periods, and/or as streaming data. In some examples, a first system control may be identified and a first system control value may be determined for the first system control. A plurality of threshold ranges associated with the first system control may be identified and the first system control value may be compared to the plurality of threshold ranges. Based on the comparing, the first system control value may be mapped to an objective score on a cyber health scale. The objective score may then be evaluated to determine whether an issue is occurring or is likely to occur. If so, one or more mitigation actions may be identified and implemented.

Embodiments determine historical geographic travel data of a vehicle over a contaminant exposure time period that is inclusive of a present time; identify a contaminant that is likely located on an external surface of the vehicle as a function of historic meteorological and terrain data of the historical geographic travel data of a vehicle over a contaminant exposure time period; select a cleaning solution that is appropriate for use in cleaning the identified contaminant from the external surface of the vehicle; and drive a vehicle wash system nozzle to use the selected cleaning solution to wash the identified contaminant from the external surface of the vehicle.

A motor control apparatus including a controller that controls a servo motor or a spindle motor and includes a switching determining part that determines a switching condition of the controller based on axis position information on a motor related to control of the motor control apparatus, a machine learning part that adjusts one or more parameters for the controller by machine learning for each switching condition, and a parameter holding part that holds the parameter adjusted by the machine learning part for each switching condition. The switching determining part, when determining the switching condition after adjustment of the parameter, uses the adjusted parameter corresponding to the switching condition in the controller. The apparatus enables changing, and automatic adjustment, of a parameter or controller to be used depending on a switching condition of the parameter related to axis position information or a switching condition of the controller using the parameter.

The present invention discloses a networked control system (NCS) time-delay compensation method based on predictive control. The method comprises the following steps: (1) acquiring random time-delay data in an NCS, and preprocessing the data; (2) predicting the current time-delay by using a fuzzy neural network (FNN) optimized by a particle swarm optimization (PSO) algorithm; (3) compensating the predicted time-delay by using an implicit proportional-integral-based generalized predictive control (PIGPC) algorithm; (4) determining whether a preset work end time is up according to a clock in the NCS; if yes, ending the process; if no, returning to step (2). The method disclosed by the present invention can accurately predict and effectively compensate the NCS time-delay and has excellent development prospect.

An apparatus for monitoring of a device including a moveable part, especially a rotating device, wherein the apparatus includes a control module which receives a measured vibration signal of the device provided by a sensor connected to the device, provides a spectrum of the measured vibration signal, pre-processes the spectrum to determine base frequencies and side frequencies, where the base frequencies are frequencies having peak powers corresponding to eigen frequencies of the device or faulty frequencies and the side frequencies correspond to other frequencies, where the control module additionally processes the base and side frequencies by applying separately a one-class classification on the base and side frequencies, combines the results of the one-class classifications to obtain a classification signal representing a confidence level, and outputs a decision support signal based on the classification signal, where the decision support signal indicates an error status of the monitored device.

A machine learning device performs machine learning with respect to a servo motor controller that converts a three-phase current to a two-phase current of the d- and q-phase. The machine learning device includes: a state information acquisition unit configured to acquire, from the servo motor controller, state information including velocity or a velocity command, reactive current, and an effective current command and effective current or a voltage command; an action information output unit configured to output action information including a reactive current command to the servo motor controller; a reward output unit configured to output a value of a reward of reinforcement learning based on the voltage command or the effective current command and the effective current; and a value function updating unit configured to update a value function on the basis of the output value of the reward, the state information, and the action information.

The invention provides control systems and methodologies for controlling a process having computer-controlled equipment, which provide for optimized process performance according to one or more performance criteria, such as efficiency, component life expectancy, safety, emissions, noise, vibration, operational cost, or the like. More particularly, the subject invention provides for employing machine diagnostic and/or prognostic information in connection with optimizing an overall business operation over a time horizon.