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The method proposed here is based on assigning the maximum absolute value of the closed loop transfer function between setpoint and process output for a given linear time-invariant process model and a PI controller tuning.
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The actual temperature at some point in time (output or process variable) may vary from the desired level; the divergence between the setpoint and process variable gives rise to an error.
Here, a novel predictive feedback error compensation method is proposed to eliminate the permanent offset between the setpoint and the process output while the integrating system is affected by load disturbance.
A PID controller is an electrical element for reducing the error value between a desired setpoint and an actual measured process variable.
The difference between setpoint updating rate and the control frequency can be traced back to the multi-layered software architecture of an overall system.
The performance of the control method is investigated through a case study of the ethylene dichloride (EDC) vaporizer system under the setpoint tracking with unmeasured disturbances and process uncertainties.
The ratio between the amplitude setpoint and the free amplitude Asp/A0 was kept equal to 0.8.
A theoretical analysis and simulation results employing first order load and process transfer functions for load and setpoint changes are presented.
Both results from numerical simulations and experiments show that the proposed method is capable of controlling industrial processes with satisfactory performance under setpoint and load changes.
In the feedforward control system, the input signal is the difference between the steam pressure and its setpoint, and the outputs are the control rod reactivity and the feedwater flow rate.
No significant association between CD4+ T-cells or RNA setpoint and the C1604G or A1650G polymorphism was observed (data not shown).
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