Adaptive Neural Network Feedforward Control for Dynamically Substructured Systems

The potential applications of dynamically substructured systems (DSSs) with both numerical and physical substructures can be found in diverse dynamics testing fields. In this paper, an adaptive feedforward controller based on a neural network (NN) is proposed to improve the DSS testing performance. To facilitate the NN compensation design, a modified DSS framework is developed so that the DSS control can be considered as a regulation problem with disturbance rejection. Then an adaptive NN feedforward compensation technique is proposed to cope with uncertainties and nonlinearities in the DSS physical substructure. The proposed NN technique generalizes the existing results in the literature, and it does not require any information of the plant model and disturbance model, which significantly simplifies its application on DSS. In particular, we propose a novel adaptive law for the NN online learning, where appropriate NN weight error information is derived and used to achieve improved performance. Real-time experimental results on a mechanical test rig demonstrate the improved performance by using the NN compensation strategy and the new adaptation law.

[1]  David J. Wagg,et al.  Substructuring of dynamical systems via the adaptive minimal control synthesis algorithm , 2001 .

[2]  Andrew Plummer,et al.  Model-in-the-Loop Testing , 2006 .

[3]  Guido Herrmann,et al.  Robust adaptive finite-time parameter estimation and control of nonlinear systems , 2011, 2011 IEEE International Symposium on Intelligent Control.

[4]  Shuzhi Sam Ge,et al.  Adaptive Neural Network Control of Hard Disk Drives With Hysteresis Friction Nonlinearity , 2011, IEEE Transactions on Control Systems Technology.

[5]  Pierre Léger,et al.  Comparison between real-time dynamic substructuring and shake table testing techniques for nonlinear seismic applications , 2010 .

[6]  Guang Li,et al.  A novel robust disturbance rejection anti-windup framework , 2011, Int. J. Control.

[7]  Martin S. Williams,et al.  Stability and Delay Compensation for Real-Time Substructure Testing , 2002 .

[8]  Frank L. Lewis,et al.  Feedforward control based on neural networks for disturbance rejection in hard disk drives , 2009 .

[9]  Chun-Liang Lin,et al.  Adaptive feedforward control for disturbance torque rejection in seeker stabilizing loop , 2001, IEEE Trans. Control. Syst. Technol..

[10]  Marios M. Polycarpou,et al.  Stable adaptive neural control scheme for nonlinear systems , 1996, IEEE Trans. Autom. Control..

[11]  Martin S. Williams,et al.  Laboratory testing of structures under dynamic loads: an introductory review , 2001, Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[12]  David P Stoten,et al.  Adaptive control of dynamically substructured systems: The single-input single-output case , 2006 .

[13]  Frank L. Lewis,et al.  Neural network compensation control for mechanical systems with disturbances , 2009, Autom..

[14]  Guido Herrmann,et al.  Application of a novel robust anti-windup technique to dynamically substructured systems , 2010, Proceedings of the 2010 American Control Conference.

[15]  Tao Zhang,et al.  Stable Adaptive Neural Network Control , 2001, The Springer International Series on Asian Studies in Computer and Information Science.

[16]  Peiman Maghami,et al.  Synthesis and Control of Flexible Systems with Component-Level Uncertainties , 2009 .

[17]  Stanislaw H. Zak,et al.  Combined observer-controller synthesis for uncertain dynamical systems with applications , 1988, IEEE Trans. Syst. Man Cybern..

[18]  Frank L. Lewis,et al.  Neural Network Control Of Robot Manipulators And Non-Linear Systems , 1998 .

[19]  Jing Na,et al.  Adaptive neural dynamic surface control for servo systems with unknown dead-zone , 2011 .

[20]  Marko Bacic,et al.  A novel controller design methodology for uncertain non-linear hardware-in-the-loop simulators , 2007, 2007 46th IEEE Conference on Decision and Control.

[21]  Guang Li,et al.  Model predictive control of dynamically substructured systems with application to a servohydraulically-actuated mechanical plant , 2010 .

[22]  Chen-Chung Liu,et al.  Adaptively controlling nonlinear continuous-time systems using multilayer neural networks , 1994, IEEE Trans. Autom. Control..

[23]  David J. Wagg,et al.  Control issues relating to real‐time substructuring experiments using a shaking table , 2005 .

[24]  Guang Li,et al.  Robotic Subsystem Testing Using an Adaptively Controlled Dynamically Substructured Framework , 2009 .

[25]  Jing Na,et al.  Adaptive feedforward control for dynamically substructured systems based on neural network compensation , 2011 .

[26]  Guang Li,et al.  Adaptive Control of Generalised Dynamically Substructured Systems , 2008 .

[27]  David J. Wagg,et al.  Rosenbrock‐based algorithms and subcycling strategies for real‐time nonlinear substructure testing , 2011 .

[28]  Guang Li,et al.  Synthesis and control of generalized dynamically substructured systems , 2009 .

[29]  W. Marsden.  I and J , 2012 .

[30]  X. Ren,et al.  Adaptive discrete neural observer design for nonlinear systems with unknown time‐delay , 2011 .

[31]  Kyung-Won Min,et al.  Real-time substructuring technique for the shaking table test of upper substructures , 2007 .

[32]  Guang Li,et al.  Application of Robust Antiwindup Techniques to Dynamically Substructured Systems , 2013, IEEE/ASME Transactions on Mechatronics.

[33]  Dimitry Gorinevsky,et al.  RBF network feedforward compensation of load disturbance in idle speed control , 1996 .