Implementation of Virtual Feedback Control of Industrial Processes via Soft Sensing Technique

  • Geetha ManiEmail author
Original Article


Hardware sensor devices are used for state measurements in many process industries but due to various disturbances and aging effect it causes inaccuracies in measurement. Hence, soft sensing technique is employed. Soft sensing is the method of determining constants and variables of any system according to the performance level based on the measurement from a process. In real-time all process variables cannot be tapped directly. Controlling of those variables are also tedious in nature. Further, by using simple feedback mechanism it cannot be controlled. Hence, there arises need for estimating the unmeasured state using suitable soft sensing techniques. The estimated values can be used as a feedback signal to the external PID controller. This leads to the design of virtual feedback control. In the feedback path, a hardware sensor is replaced with soft sensing technique namely an extended Kalman filter (EKF). But the main drawback of using conventional PID controllers in industries are both servo tracking and disturbance rejection cannot be achieved at the same time. Thus, enhanced PID (EPID) controllers are used to overcome the above demerit. The EPID controller has the capability to instantaneously track the set point variations and reject the disturbances simultaneously. The comparative performance analysis of both conventional PID and EPID integrated with EKF has done in simulated continuous stirred tank reactor non-linear process. The effectiveness of virtual feedback control using EPID has been demonstrated in the real-time level process.


CSTR EKF EPID PID Soft sensing Virtual feedback control 



This Research work was financially supported by the UGC Major Research Project scheme under the title Investigations on the application of state estimation in CSTR control and Grant order no. F. 39-874/2010(SR) dated 12.01.2011.


  1. 1.
    Doyle FJ (1998) Nonlinear inferential control for process applications. J Process Control 8:339–358CrossRefGoogle Scholar
  2. 2.
    Geetha M, Jerome J, Arun Kumar P (2014) Critical evaluation of non-linear filter configurations for the state estimation of continuous stirred tank reactor. Appl Soft Comput 25:452–460CrossRefGoogle Scholar
  3. 3.
    Geetha M, Balajee K, Jerome J (2012) Optimal tuning of virtual feedback PID controller for a continuous stirred tank reactor (CSTR) using particle swarm optimization (PSO) algorithm. In: Proceedings of IEEE international conference (ICAESM-2012), pp 100–105Google Scholar
  4. 4.
    Geetha M, Jerome J (2013) Implementation and performance analysis of an improved relay feedback auto-tuning PID controller. Aust J Basic Appl Sci 7(7):525–530Google Scholar
  5. 5.
    Taguchi H, Araki M (2000) Two-degree-of-freedom PID controllers—their functions and optimal tuning. IFAC Proc Vol 33(4):91–96CrossRefGoogle Scholar
  6. 6.
    Ang KH, Chong G, Li Y (2005) PID control system analysis, design, and technology. IEEE Trans Control Syst Technol 13(4):559–576. CrossRefGoogle Scholar
  7. 7.
    Ljung L (1999) System identification: theory for the user, 2nd edn. Prentice Hall, Upper Saddle River, New Jersey (ISBN-13: 978-0136566953) zbMATHGoogle Scholar

Copyright information

© The Korean Institute of Electrical Engineers 2019

Authors and Affiliations

  1. 1.School of Electrical EngineeringVellore Institute of TechnologyVelloreIndia

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