Multivariable Disturbance Observer Based Advanced Feedback Control Design and Its Application to a Grinding Circuit

Design of advanced feedback control (AFC) for optimal process operation has been mostly based on a hierarchical structure for many years. Since many advanced control algorithms (such as the model predictive control) do not handle disturbances directly in their design phase, it is difficult to achieve satisfactory performance in controlling complex process operations in the presence of heavy disturbances and large uncertainties. Focused on this practical challenge, in this paper we propose a novel multivariable disturbance observer (MDOB) to improve the disturbance rejection performance of conventional AFCs. The MDOB formulation is based on the approximate inversion of the multivariable generalized system, which consists of the multivariable operation process and the lower level basic feedback control system. All the stable and realizable MDOBs are characterized in terms of time delays and nonminimum phase zeros of the open-loop generalized systems. In the proposed MDOB-based AFC, the advanced feedback controller acts as a presetting controller to generate the proper pre-setpoint for the lower level basic feedback control (BFC) system such that a desired setpoint tracking is achieved. The MDOB acts as a compensator to enhance the operational performance of the process by dynamically adjusting the setpoints of the BFC according to the observed disturbances and plant uncertainties. Theoretical analysis, simulation comparisons, and experimental evaluation using a hardware-in-loop simulation platform of a grinding circuit are given, showing the effectiveness, validity, and advantages of the proposed approach.

[1]  Toshio Fukuda,et al.  A nonlinear disturbance observer for multivariable systems and its application to magnetic bearing systems , 2004, IEEE Transactions on Control Systems Technology.

[2]  Tianyou Chai,et al.  DOB Design for Nonminimum-Phase Delay Systems and Its Application in Multivariable MPC Control , 2012, IEEE Transactions on Circuits and Systems II: Express Briefs.

[3]  Il Hong Suh,et al.  On the robustness and performance of disturbance observers for second-order systems , 2003, IEEE Trans. Autom. Control..

[4]  S. Joe Qin,et al.  A survey of industrial model predictive control technology , 2003 .

[5]  Seung-Ki Sul,et al.  Application of a Disturbance Observer for a Relative Position Control System , 2010 .

[6]  Tianyou Chai,et al.  Intelligence-Based Supervisory Control for Optimal Operation of a DCS-Controlled Grinding System , 2013, IEEE Transactions on Control Systems Technology.

[7]  Jun Yang,et al.  Disturbance rejection of dead-time processes using disturbance observer and model predictive control , 2011 .

[8]  Stephen J. Wright,et al.  Distributed MPC Strategies With Application to Power System Automatic Generation Control , 2008, IEEE Transactions on Control Systems Technology.

[9]  Hyungbo Shim,et al.  An almost necessary and sufficient condition for robust stability of closed-loop systems with disturbance observer , 2009, Autom..

[10]  Kouhei Ohnishi,et al.  Microprocessor-Controlled DC Motor for Load-Insensitive Position Servo System , 1985, IEEE Transactions on Industrial Electronics.

[11]  Zi-Jiang Yang,et al.  Robust Output Feedback Control of a Class of Nonlinear Systems Using a Disturbance Observer , 2011, IEEE Transactions on Control Systems Technology.

[12]  Mu-Tian Yan,et al.  Theory and application of a combined feedback–feedforward control and disturbance observer in linear motor drive wire-EDM machines , 2008 .

[13]  Tianyou Chai,et al.  Hybrid intelligent control for optimal operation of shaft furnace roasting process , 2011 .

[14]  Manfred Morari,et al.  Use of model predictive control and weather forecasts for energy efficient building climate control , 2012 .

[15]  Tianyou Chai,et al.  Model approximation of multiple delay transfer function models using multiple-point step response fitting , 2012 .

[16]  Karl Johan Åström,et al.  PID Controllers: Theory, Design, and Tuning , 1995 .

[17]  Piotr Tatjewski,et al.  Advanced control and on-line process optimization in multilayer structures , 2008, Annu. Rev. Control..

[18]  Qidi Wu,et al.  Optimal-setting control for complicated industrial processes and its application study , 2004 .

[19]  Sebastian Engell Feedback control for optimal process operation , 2007 .

[20]  Qing Wei Jia,et al.  Disturbance Rejection Through Disturbance Observer With Adaptive Frequency Estimation , 2009, IEEE Transactions on Magnetics.

[21]  Ian Postlethwaite,et al.  Multivariable Feedback Control: Analysis and Design , 1996 .

[22]  Chai Tianyou Research and development of optimal-setting software platform based on optimal operational control integrated system , 2013 .

[23]  Kyung-Soo Kim,et al.  Disturbance Observer for Estimating Higher Order Disturbances in Time Series Expansion , 2010, IEEE Transactions on Automatic Control.

[24]  Min-Sen Chiu,et al.  Decoupling internal model control for multivariable systems with multiple time delays , 2002 .

[25]  Riccardo Scattolini,et al.  Architectures for distributed and hierarchical Model Predictive Control - A review , 2009 .

[26]  Han-Xiong Li,et al.  Multivariable fuzzy supervisory control for the laminar cooling process of hot rolled slab , 2001, IEEE Trans. Control. Syst. Technol..

[27]  Shihua Li,et al.  Disturbance observer based multi-variable control of ball mill grinding circuits , 2009 .

[28]  Ioan Doré Landau,et al.  Adaptive regulation—Rejection of unknown multiple narrow band disturbances (a review on algorithms and applications) , 2011 .

[29]  Furong Gao,et al.  Analytical Two-Degrees-of-Freedom (2-DOF) Decoupling Control Scheme for Multiple-Input−Multiple-Output (MIMO) Processes with Time Delays , 2007 .