A closed-loop system may include a plant (an electric machine requiring control) and a fractional-order proportional-resonant controller. The fractional-order proportional-resonant controller may have an order greater than zero and less than or equal to one. The order for the fractional-order proportional-resonant controller may be selected to yield a target amplitude and target slope for frequency response. The frequency response may be such that a steady-state error associated with a speed of the electric machine is inversely proportional to the target amplitude and less than a predetermined threshold. The order of the controller may be 0.9.

A closed-loop system may include a plant (an electric machine requiring control) and a fractional-order proportional-resonant controller. The fractional-order proportional-resonant controller may have an order greater than zero and less than or equal to one. The order for the fractional-order proportional-resonant controller may be selected to yield a target amplitude and target slope for frequency response. The frequency response may be such that a steady-state error associated with a speed of the electric machine is inversely proportional to the target amplitude and less than a predetermined threshold. The order of the controller may be 0.9.

A neural control system comprises an input controller arranged to receive neural data regarding neural signals relating to a bodily state of a subject from at least one neural sensor, at least one machine learning means using at least one machine learning model to process the received neural data to determine at least one output signal required to achieve a desired value of the bodily state, and means arranged to send the determined output signal to at least one output device, whereby the neural control system forms a first control loop providing closed loop control of the bodily state.

A neural control system comprises an input controller arranged to receive neural data regarding neural signals relating to a bodily state of a subject from at least one neural sensor, at least one machine learning means using at least one machine learning model to process the received neural data to determine at least one output signal required to achieve a desired value of the bodily state, and means arranged to send the determined output signal to at least one output device, whereby the neural control system forms a first control loop providing closed loop control of the bodily state.

An arrangement for the control of an actuator (48) for the adjustment of an adjustable element (16) of an agricultural work machine (10) is equipped with a control unit (50) to produce adjustment signals for the adjustable element (16) in the sense of moving to a theoretical position, a control arrangement (62) of the actuator (48), coupled with the adjustable element (16), which receives the adjustment signals of the control unit (50), and a determination device (54) to make available at least one parameter (0), determined with the aid of recorded vibration characteristics of the system consisting of the adjustable element (16) and the work machine (10). The determination device (54) can be operated so as to determine at least one parameter (0) at the successively following time points and to supply it to the control unit (50). The parameter (0) serves to optimize the regulating behavior of the control unit (50).

An arrangement for the control of an actuator (48) for the adjustment of an adjustable element (16) of an agricultural work machine (10) is equipped with a control unit (50) to produce adjustment signals for the adjustable element (16) in the sense of moving to a theoretical position, a control arrangement (62) of the actuator (48), coupled with the adjustable element (16), which receives the adjustment signals of the control unit (50), and a determination device (54) to make available at least one parameter (0), determined with the aid of recorded vibration characteristics of the system consisting of the adjustable element (16) and the work machine (10). The determination device (54) can be operated so as to determine at least one parameter (0) at the successively following time points and to supply it to the control unit (50). The parameter (0) serves to optimize the regulating behavior of the control unit (50).

A controller for controlling a micromechanical actuator having a setpoint input for receiving a setpoint signal, an actual-value input for receiving an actual-value signal, a setpoint filter to attenuate a first predefined frequency or a first predefined frequency band in the received setpoint signal to generate a filtered setpoint signal, a differentiator to generate a time derivative of the received actual-value signal; a controller core to generate a manipulated variable signal based on a system deviation between the filtered setpoint signal and the actual-value signal; a phase rotation element to modify the phase of the difference between the manipulated variable signal and the derivative of the actual-value signal for a second frequency or in a predefined second frequency band to generate a modified manipulated variable signal; and a first manipulated variable filter to suppress a predefined third frequency in the modified manipulated variable signal.

A controller for controlling a micromechanical actuator having a setpoint input for receiving a setpoint signal, an actual-value input for receiving an actual-value signal, a setpoint filter to attenuate a first predefined frequency or a first predefined frequency band in the received setpoint signal to generate a filtered setpoint signal, a differentiator to generate a time derivative of the received actual-value signal; a controller core to generate a manipulated variable signal based on a system deviation between the filtered setpoint signal and the actual-value signal; a phase rotation element to modify the phase of the difference between the manipulated variable signal and the derivative of the actual-value signal for a second frequency or in a predefined second frequency band to generate a modified manipulated variable signal; and a first manipulated variable filter to suppress a predefined third frequency in the modified manipulated variable signal.

A machining time estimating apparatus is stored with mechanical configuration-time data, which are control parameters relating to respective machining times of a plurality of numerically-controlled machine tools, and is provided with a machining time estimation unit, configured to estimate the machining time required for machining performed based on an NC command in a first one of the plurality of numerically-controlled machine tools, and a mechanical configuration difference time calculation unit configured to calculate machining times required for machining performed based on the NC command in the other ones of the plurality of numerically-controlled machine tools than the first numerically-controlled machine tool, based on the respective mechanical configuration-time data of the plurality of numerically-controlled machine tools.

A machining time estimating apparatus is stored with mechanical configuration-time data, which are control parameters relating to respective machining times of a plurality of numerically-controlled machine tools, and is provided with a machining time estimation unit, configured to estimate the machining time required for machining performed based on an NC command in a first one of the plurality of numerically-controlled machine tools, and a mechanical configuration difference time calculation unit configured to calculate machining times required for machining performed based on the NC command in the other ones of the plurality of numerically-controlled machine tools than the first numerically-controlled machine tool, based on the respective mechanical configuration-time data of the plurality of numerically-controlled machine tools.