Filtered Predictive PI Controller for WirelessHART Networked Systems

Recently, increasing attention has been paid towards applying wireless technology for control. This is due to its advantages of flexibility, scalability, use of fewer cables and overall reduced operational cost compared to its wired counterpart. However, the technology is often affected by stochastic delay and high frequency noise. PIDs are ill-equipped to deal with these problems while model based controllers such as dead-time compensators (DTCs) like Smith predictor and internal model controllers (IMCs) are complex and require exact plant model for implementation.

[1]  Mikulas Huba.  Filter choice for an effective measurement noise attenuation in PI and PID controllers , 2015, 2015 IEEE International Conference on Mechatronics (ICM).

[2]  Alf J. Isaksson,et al.  Simulation of process control with WirelessHART networks subject to packet losses , 2008, 2008 IEEE International Conference on Automation Science and Engineering.

[3]  Igor Belai,et al.  Experimental Evaluation of a DO-FPID Controller with Different Filtering Properties , 2014 .

[4]  Tore Hägglund,et al.  Measurement noise filtering for PID controllers , 2014 .

[5]  Ron T. Ogan.  Hardware-in-the-Loop Simulation , 2015 .

[6]  Tore Hägglund,et al.  Robustness margins separating process dynamics uncertainties , 2009, 2009 European Control Conference (ECC).

[7]  Alf J. Isaksson,et al.  Simulation of Process Control with WirelessHART Networks Subject to Clock Drift , 2008, 2008 32nd Annual IEEE International Computer Software and Applications Conference.

[8]  Ruth Bars,et al.  Robust Predictive PI Controller Based on First-Order Dead Time Model , 2008 .

[9]  C. L. Philip Chen,et al.  Measurement and Characterization of Channel Delays for Broadband Power Line Communications , 2014, IEEE Transactions on Instrumentation and Measurement.

[10]  Shouzhao Sheng,et al.  An adaptive attitude tracking control approach for an unmanned helicopter with parametric uncertainties and measurement noises , 2016 .

[11]  Eduardo F. Camacho,et al.  Dead-time compensators: A survey , 2008 .

[12]  Eduardo F. Camacho,et al.  Control of dead-time processes , 2007 .

[13]  F. G. Shinskey.  PID-Deadtime Control of Distributed Processes , 2000 .

[14]  Tran Duc Chung,et al.  Setpoint weighted wirelesshart networked control of process plant , 2016, 2016 IEEE International Instrumentation and Measurement Technology Conference Proceedings.

[15]  Tore Hägglund,et al.  Control signal constraints and filter order selection for PI and PID controllers , 2011, Proceedings of the 2011 American Control Conference.

[16]  Dan Simon,et al.  Analyzing control system robustness , 2002 .

[17]  Vijanth S. Asirvadam,et al.  Adopting EWMA Filter on a Fast Sampling Wired Link Contention in WirelessHART Control System , 2016, IEEE Transactions on Instrumentation and Measurement.

[18]  Tore Hägglund,et al.  Comparison Between Robust PID and Predictive PI Controllers with Constrained Control Signal Noise Sensitivity , 2012 .

[19]  M. Huba.  Comparing 2DOF PI and predictive disturbance observer based filtered PI control , 2013 .

[20]  Tore Hägglund.  A Predictive PI Controller for Processes with Long Dead Time , 1989 .

[21]  Miroslav R. Mataušek,et al.  A New Predictive PI Controller with Additonal Filtering , 2012 .

[22]  T.H. Lee,et al.  Deadtime compensation via setpoint variation , 2009, 2009 7th Asian Control Conference.

[23]  Julio E. Normey-Rico,et al.  Improving the robustness of dead-time compensating PI controllers , 1997 .

[24]  Tran Duc Chung,et al.  Predictive PI controller for wireless control system with variable network delay and disturbance , 2016, 2016 2nd IEEE International Symposium on Robotics and Manufacturing Automation (ROMA).