A Set of Active Disturbance Rejection Controllers Based on Integrator Plus Dead-Time Models

The paper develops and investigates a novel set of constrained-output robust controllers with selectable response smoothing degree designed for an integrator-plus-dead-time (IPDT) plant model. The input-output response of the IPDT system is internally approximated by several time-delayed, possibly higher-order plant models of increasing complexity. Since they all contain a single integrator, the presented approach can be considered as a generalization of active disturbance rejection control (ADRC). Due to the input/output model used, the controller commissioning can be based on a simplified process modeling, similar to the one proposed by Ziegler and Nichols. This allows it to be compared with several alternative controllers commonly used in practice. Its main advantage is simplicity, since it uses only two identified process parameters, even when dealing with more complex systems with distributed parameters. The proposed set of controllers with increasing complexity includes the stabilizing proportional (P), proportional-derivative (PD), or proportional-derivative-acceleration (PDA) controllers. These controllers can be complemented by extended state observers (ESO) for the reconstruction of all required state variables and non-measurable input disturbances, which also cover imperfections of a simplified plant modeling. A holistic performance evaluation on a laboratory heat transfer plant shows interesting results from the point of view of the optimal least sensitive solution with smooth input and output.

[1]  Yi Huang,et al.  ADRC With Adaptive Extended State Observer and its Application to Air–Fuel Ratio Control in Gasoline Engines , 2015, IEEE Transactions on Industrial Electronics.

[2]  Alireza Alfi,et al.  A memetic algorithm applied to trajectory control by tuning of Fractional Order Proportional-Integral-Derivative controllers , 2015, Appl. Soft Comput..

[3]  Mariagrazia Dotoli,et al.  IoT Based Architecture for Model Predictive Control of HVAC Systems in Smart Buildings , 2020, Sensors.

[4]  Zhiqiang Gao,et al.  Modified active disturbance rejection control for time-delay systems. , 2014, ISA transactions.

[5]  Pavol Bistak,et al.  Asymmetries in the Disturbance Compensation Methods for the Stable and Unstable First Order Plants , 2020, Symmetry.

[6]  Radu-Emil Precup,et al.  An overview on fault diagnosis and nature-inspired optimal control of industrial process applications , 2015, Comput. Ind..

[7]  J. G. Ziegler,et al.  Optimum Settings for Automatic Controllers , 1942, Journal of Fluids Engineering.

[8]  Zhiqiang Gao,et al.  On the centrality of disturbance rejection in automatic control. , 2014, ISA transactions.

[9]  R. Madonski,et al.  Cascade extended state observer for active disturbance rejection control applications under measurement noise. , 2020, ISA transactions.

[10]  Y. TAKAHASHI,et al.  Parametereinstellung bei linearen DDC-Algorithmen , 1971 .

[11]  Mikuláš Huba,et al.  Performance measures, performance limits and optimal PI control for the IPDT plant , 2013 .

[12]  Baris Baykant Alagoz,et al.  FOPID Controllers and Their Industrial Applications: A Survey of Recent Results , 2018 .

[13]  Mikuláš Huba,et al.  Two Dynamical Classes of PI-Controllers for the 1st Order Loops , 1997 .

[14]  Sigurd Skogestad,et al.  Simple analytic rules for model reduction and PID controller tuning , 2003 .

[15]  G. Martin,et al.  Nonlinear model predictive control , 1999, Proceedings of the 1999 American Control Conference (Cat. No. 99CH36251).

[16]  Pavol Bistak,et al.  PID Control With Higher Order Derivative Degrees for IPDT Plant Models , 2021, IEEE Access.

[17]  Jingqing Han,et al.  From PID to Active Disturbance Rejection Control , 2009, IEEE Trans. Ind. Electron..

[18]  Alberto Leva,et al.  Extending Ideal PID Tuning Rules to the ISA Real Structure: Two Procedures and a Benchmark Campaign , 2011 .

[19]  Tore Hägglund,et al.  Robust PID Design Based on QFT and Convex–Concave Optimization , 2017, IEEE Transactions on Control Systems Technology.

[20]  Mehmet Önder Efe,et al.  Fractional Order Systems in Industrial Automation—A Survey , 2011, IEEE Transactions on Industrial Informatics.

[21]  Emilia Fridman,et al.  State and unknown input observers for nonlinear systems with delayed measurements , 2018, Autom..

[22]  Pavol Bistak,et al.  New Thermo-Optical Plants for Laboratory Experiments , 2014 .

[23]  Yogesh V. Hote,et al.  Polynomial Controller Design and Its Application: Experimental Validation on a Laboratory Setup of Nonideal DC–DC Buck Converter , 2020, IEEE Transactions on Industry Applications.

[24]  Yi Huang,et al.  On comparison of modified ADRCs for nonlinear uncertain systems with time delay , 2018, Science China Information Sciences.

[25]  Lei Guo,et al.  Disturbance-Observer-Based Control and Related Methods—An Overview , 2016, IEEE Transactions on Industrial Electronics.

[26]  Jie Ma,et al.  Robust intelligent control design for marine diesel engine , 2013 .

[27]  Wenhui Li,et al.  Speed Control for a Marine Diesel Engine Based on the Combined Linear-Nonlinear Active Disturbance Rejection Control , 2018 .

[28]  M. Sunwoo,et al.  Idle speed controller based on active disturbance rejection control in diesel engines , 2016 .

[29]  Pedro Mercader,et al.  A PI tuning rule for integrating plus dead time processes with parametric uncertainty. , 2017, ISA transactions.