Systems and methods for automated workflow comprise assigning a set of first targets to an uncompiled first workflow. The uncompiled first workflow specifies a first set of process modules. Each such module is associated with a subset of unit operations. Each unit operation includes a time interval and specifies an instrument. For each target in the set of first targets, the uncompiled workflow is translated into an instance of a compiled first workflow comprising a linear temporal order of unit operations, each including execution instructions for an addressed instrument. A set of second targets is obtained and assigned a second uncompiled workflow. Compilation of the second uncompiled workflow for each second target produces a different instance of a compiled second workflow. Each second compiled workflow comprises a linear temporal order of unit operations, with each unit operation including execution instructions for an addressed instrument and specifying a time interval.

Systems and methods for automated workflow comprise assigning a set of first targets to an uncompiled first workflow. The uncompiled first workflow specifies a first set of process modules. Each such module is associated with a subset of unit operations. Each unit operation includes a time interval and specifies an instrument. For each target in the set of first targets, the uncompiled workflow is translated into an instance of a compiled first workflow comprising a linear temporal order of unit operations, each including execution instructions for an addressed instrument. A set of second targets is obtained and assigned a second uncompiled workflow. Compilation of the second uncompiled workflow for each second target produces a different instance of a compiled second workflow. Each second compiled workflow comprises a linear temporal order of unit operations, with each unit operation including execution instructions for an addressed instrument and specifying a time interval.

A control device includes a target value acquisition portion configured to acquire a target value of amount of control per predetermined computation cycle, a control portion configured to control the control target using the acquired target value, a target smoothing value calculation portion configured to calculate a target smoothing value in which a temporal change in the target value is slowed down, and a state determination portion configured to calculate a first difference that is a difference between the target value and the target smoothing value to determine that the amount of control is in a transient state when the calculated first difference is equal to or more than a predetermined first threshold value and to determine that the amount of control is in a non-transient state when the first difference is smaller than the first threshold value.

Techniques are described for controlling the power and/or matching the impedance of the output impedance of a high frequency power generator to the impedance of a load, in particular a plasma discharge. A control arrangement may include a control unit, to which a target value, an actual value, and a correction value is supplied, the control unit being set up to generate an adjustment value by taking into account the correction value. The control arrangement may also include a device for determining the correction value, to which a control value is supplied and which is set up to determine the correction value by taking into account the control value and a default value. In some embodiments, when the control value deviates from the default value, the correction value influences the control unit such that the actual value deviates from the target value in the adjusted state of the control unit.

Techniques are described for controlling the power and/or matching the impedance of the output impedance of a high frequency power generator to the impedance of a load, in particular a plasma discharge. A control arrangement may include a control unit, to which a target value, an actual value, and a correction value is supplied, the control unit being set up to generate an adjustment value by taking into account the correction value. The control arrangement may also include a device for determining the correction value, to which a control value is supplied and which is set up to determine the correction value by taking into account the control value and a default value. In some embodiments, when the control value deviates from the default value, the correction value influences the control unit such that the actual value deviates from the target value in the adjusted state of the control unit.

A method is provided, including providing an accelerometer responsive to motion of a closure forming part of an automatic closure system, providing an output of the accelerometer to first and second comparator circuits, the first comparator circuit having a first reference voltage such that the output of the first comparator circuit is indicative of a direction of closure motion and the second comparator circuit having an adjustable second reference voltage such that the output of the second comparator circuit is scaled relative to this adjustable second reference voltage to adjust the sensitivity of the output of the second comparator, providing a microcontroller coupled to the first and second comparator circuits, the microcontroller determining both a motion sequence of the closure and whether the closure has impacted an object, and providing an interface circuit for communicating the output from the microcontroller to a remote controller circuit.

Systems and methods are provided for using a Deep Reinforcement Learning (DRL) agent to provide adaptive tuning of process controllers, such as Proportional-Integral-Derivative (PID) controllers. The agent can monitor process controller performance, and if unsatisfactory, can attempt to improve it by making incremental changes to the tuning parameters for the process controller. The effect of a tuning change can then be observed by the agent and used to update the agent's process controller tuning policy. It has been unexpectedly discovered that providing adaptive tuning based on incremental changes in tuning parameters, as opposed to making changes independent of current values of the tuning parameters, can provide enhanced or improved control over a controlled variable of a process.

Systems and methods are provided for using a Deep Reinforcement Learning (DRL) agent to provide adaptive tuning of process controllers, such as Proportional-Integral-Derivative (PID) controllers. The agent can monitor process controller performance, and if unsatisfactory, can attempt to improve it by making incremental changes to the tuning parameters for the process controller. The effect of a tuning change can then be observed by the agent and used to update the agent's process controller tuning policy. It has been unexpectedly discovered that providing adaptive tuning based on incremental changes in tuning parameters, as opposed to making changes independent of current values of the tuning parameters, can provide enhanced or improved control over a controlled variable of a process.

A tape transport control system with enhanced regulation of tape tension and velocity over the entire length of the tape. For example, a closed-loop control system for controlling a tape transport includes circuitry adapted to output a signal representing a tape velocity at a tape head, circuitry adapted to output a signal representing a tape velocity at the first tape reel and a signal representing a tape velocity at the second tape reel, circuitry adapted to output a signal representing a tape tension, and circuitry adapted to, based on the received signals, generate control signals to control the tape velocity at least at one of the tape head, the first tape reel, the second tape reel, and the tension of the tape, using controller gains that depend on a longitudinal position of the tape, and to implement a system transfer function that is approximately independent of the longitudinal position.

A tape transport control system with enhanced regulation of tape tension and velocity over the entire length of the tape. For example, a closed-loop control system for controlling a tape transport includes circuitry adapted to output a signal representing a tape velocity at a tape head, circuitry adapted to output a signal representing a tape velocity at the first tape reel and a signal representing a tape velocity at the second tape reel, circuitry adapted to output a signal representing a tape tension, and circuitry adapted to, based on the received signals, generate control signals to control the tape velocity at least at one of the tape head, the first tape reel, the second tape reel, and the tension of the tape, using controller gains that depend on a longitudinal position of the tape, and to implement a system transfer function that is approximately independent of the longitudinal position.