Laboratory Controllers Information
Laboratory controllers are designed to monitor and/or control variables in laboratory or scientific experimentation, testing, or sample preparation. They are often used to control temperature, but may control other variables. Product specifications for laboratory controls include: number of inputs, number of outputs, input types, output types, and number of zones. The number of inputs is the total number of signals sent to the laboratory controller. The number of outputs is the sum of all outputs used to control, compensate or correct the laboratory process. Input types for a laboratory control include: direct current (DC) voltage, current loops, analog signals from thermocouples, thermistors, resistors or potentiometers, frequency inputs, and switch or relay inputs. Output types include analog voltage, current loops, switch or relay outputs, and pulses or frequencies. Some laboratory controllers can also receive inputs or send outputs in serial, parallel, Ethernet or other digital formats which indicate a process variable. Others use an industrial fieldbus protocol such as CANbus, SERCOS, or PROFIBUS® (PROFIBUS International).
Laboratory controllers use several different control techniques. Examples include limit control, linear control, PID control, feedforward control, fuzzy logic, and advanced or non-linear controls. Limit control establishes set points or limits that, when reached cause the laboratory controller to send a signal to stop or start a process variable. Linear control matches a variable input signal with a correspondingly variable control signal. Proportional, integral, and derivative (PID) control requires real-time system feedback. Feedforward control provides direct-control compensation from the reference signal. Fuzzy logic is a type of laboratory control in which variables can have imprecise values (as in partial truth) rather than a binary status (completely true or completely false). Advanced or nonlinear controls for laboratory controllers use algorithms such as adaptive gain and neural networking.