Combining model based and data-driven approaches: First order nonlinear plants control

The paper deals with a data-based robust tuning of a controller for nonlinear first order plants combining exact linearization based nonlinearity compensation with a nonlinear disturbance observer (NDOB) based integral action. A two-step experimental control design is carried out with the aim of the fastest possible monotonic setpoint step responses and of a well damped control signal courses. The first stage of an optimal and robust tuning procedure focuses on collecting and processing data about the process by a sequence of step responses. It is used for determining appropriate parameters of the stabilizing P control extended by a fixed offset. This may be extended by a second stage, when the proposed controller is used to generate new data sets for an optimal robust disturbance observer tuning. All procedures are illustrated by an electrical fan speed control example carried out in a Matlab/Simulink environment.

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

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

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

[4]  Alberto Isidori,et al.  Nonlinear control systems: an introduction (2nd ed.) , 1989 .

[5]  Wen-Hua Chen,et al.  Disturbance observer based control for nonlinear systems , 2004, IEEE/ASME Transactions on Mechatronics.

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

[7]  Johannes A.G.M. van Dijk,et al.  Disturbance Observers for Rigid Mechanical Systems: Equivalence, Stability, and Design , 2002 .

[8]  Vladimír Kučera,et al.  Diophantine equations in control - A survey , 1993, Autom..

[9]  Cédric Join,et al.  Model-free control and intelligent PID controllers: towards a possible trivialization of nonlinear control? , 2009, ArXiv.

[10]  C. Moog,et al.  Nonlinear Control Systems: An Algebraic Setting , 1999 .

[11]  Zhuo Wang,et al.  From model-based control to data-driven control: Survey, classification and perspective , 2013, Inf. Sci..

[12]  Cédric Join,et al.  Model-free control , 2013, Int. J. Control.

[13]  Tore Hägglund,et al.  Robust tuning procedures of dead-time conpensating controllers , 2001 .

[14]  Pavol Bistak,et al.  Comparing two approaches to nonlinear time-delayed 1st-order plants control , 2014, 2014 18th International Conference on System Theory, Control and Computing (ICSTCC).

[15]  Miroslav Halás,et al.  An algebraic framework generalizing the concept of transfer functions to nonlinear systems , 2008, Autom..

[16]  S. Skogestad Simple analytic rules for model reduction and PID controller tuning , 2004 .

[17]  M. Huba,et al.  Experimenting with a nonlinear Thermo-Opto-Mechanical System TOM1A , 2015 .

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

[19]  Pavol Bistak,et al.  P- AND PD- CONTROLLERS FOR I1 AND I2 MODELS WITH DEAD TIME , 1999 .

[20]  Wilson J. Rugh Research on Gain Scheduling: Quiet Past. Noisy Present! Symphonic Future? , 1998 .

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

[22]  Mikulás Huba Open flexible P-controller design , 2012, 2012 12th IEEE International Workshop on Advanced Motion Control (AMC).

[23]  Wen-Hua Chen,et al.  Disturbance attenuation and rejection for systems with nonlinearity via DOBC approach , 2005 .

[24]  Lei Guo,et al.  Anti-disturbance control theory for systems with multiple disturbances: a survey. , 2014, ISA transactions.

[25]  Yoichi Hori,et al.  Robust speed control of DC servomotors using modern two degrees-of-freedom controller design , 1991 .