Disclosed is a reconfigurable device for dispensing/distributing tablets in the blister pockets of a packaging strip subjected to longitudinal movement in a substantially horizontal plane of a packaging machine having a blister-packer, the reconfigurable device having a frame designed to be mounted on the blister-packer, the frame having a front attachment wall preferably arranged vertically, at least one accessory for dispensing/distributing the tablets in the blister pockets of the strip, the accessory being designed to be mounted removably on the frame. The device includes an intermediate interface intended to be attached removably to the frame, against the front attachment wall thereof, in order to support the at least one accessory. The interface includes at least one drive member of a mobile element of the accessory/accessories supported thereby.

Disclosed is a reconfigurable device for dispensing/distributing tablets in the blister pockets of a packaging strip subjected to longitudinal movement in a substantially horizontal plane of a packaging machine having a blister-packer, the reconfigurable device having a frame designed to be mounted on the blister-packer, the frame having a front attachment wall preferably arranged vertically, at least one accessory for dispensing/distributing the tablets in the blister pockets of the strip, the accessory being designed to be mounted removably on the frame. The device includes an intermediate interface intended to be attached removably to the frame, against the front attachment wall thereof, in order to support the at least one accessory. The interface includes at least one drive member of a mobile element of the accessory/accessories supported thereby.

A method may include controlling, by a computing device, a directed energy deposition material addition (DED MA) technique based at least in part on a thermal model. The thermal model may define a plurality of default operating parameters for the DED MA technique. The method also may include detecting, by at least one sensor, at least one parameter related to the DED MA technique. Further, the method may include, responsive to determining, by the computing device, that a value of the at least one detected parameter is different from an expected value of a corresponding parameter predicted by the thermal model, determining, by the computing device and using a neuro-fuzzy algorithm, an updated value for at least one operating parameter for the DED MA technique, and controlling, by the computing device, the DED MA technique based at least in part on the updated value.

A control device for performing optimal control by path integral includes a neural network section including a machine-learned dynamics model and cost function, an input section that inputs a current state of a control target and an initial control sequence for the control target into the neural network section, and an output section that outputs a control sequence for controlling the control target, the control sequence being calculated by the neural network section by path integral from the current state and the initial control sequence by using the dynamics model and the cost function. Here, the neural network section includes a second recurrent neural network incorporating a first recurrent neural network including the dynamics model.

A method of transient testing a prime mover wherein the prime mover is coupled to a power absorbing dynamometer. The method comprising the steps of: receiving a first load setpoint and a first rotational speed setpoint, wherein the first load setpoint and the first rotational speed setpoint correspond to a first time point in a prime mover testing profile, wherein the prime mover testing profile is a model of a real-world testing profile; outputting the first load setpoint or the first rotational speed setpoint to the power absorbing dynamometer; determining a first baseline prime mover demand input using a first feedforward loop; determining a first prime mover demand input, wherein the first prime mover demand input is based on the first baseline prime mover demand input and the first load setpoint or the first rotational speed setpoint; outputting the first prime mover demand input to the prime mover; wherein, upon the first load setpoint being provided to the power absorbing dynamometer, the first prime mover demand input is based on the first baseline prime mover demand input and the first rotational speed setpoint, and wherein, upon the first rotational speed setpoint being provided to the power absorbing dynamometer, the first prime mover demand input is based on the first baseline prime mover demand input and the first load setpoint; receiving a first load measurement value and a first rotational speed measurement value; and determining a second prime mover demand input based on: the first prime mover demand input or a second baseline prime mover demand input; a second load setpoint or a second rotational speed setpoint; and the first load measurement value or the first rotational speed measurement value.