Vibration control of a pneumatic driven piezoelectric flexible manipulator using self-organizing map based multiple models

Abstract A kind of hybrid pneumatic-piezoelectric flexible manipulator system has been presented in the paper. A hybrid driving scheme is achieved by combining of a pneumatic proportional valve based pneumatic drive and a piezoelectric actuator bonded to the flexible beam. The system dynamics models are obtained based on system identification approaches, using the established experimental system. For system identification of the flexible piezoelectric manipulator subsystem, parametric estimation methods are utilized. For the pneumatic driven system, a single global linear model is not accurate enough to describe its dynamics, due to the high nonlinearity of the pneumatic driven system. Therefore, a self-organizing map (SOM) based multi-model system identification approach is used to get multiple local linear models. Then, a SOM based multi-model inverse controller and a variable damping pole-placement controller are applied to the pneumatic drive and piezoelectric actuator, respectively. Experiments on pneumatic driven vibration control, piezoelectric vibration control and hybrid vibration control are conducted, utilized proportional and derivative (PD) control, SOM based multi-model inverse controller, and the variable damping pole-placement controller. Experimental results demonstrate that the investigated control algorithms can improve the vibration control performance of the pneumatic driven flexible piezoelectric manipulator system.

[1]  Graham C. Goodwin,et al.  On sampled-data models for nonlinear systems , 2005, IEEE Transactions on Automatic Control.

[2]  Teuvo Kohonen,et al.  The self-organizing map , 1990, Neurocomputing.

[3]  Peter Eberhard,et al.  DYNAMIC ANALYSIS OF FLEXIBLE MANIPULATORS, A LITERATURE REVIEW , 2006 .

[4]  Vicente Feliu,et al.  Adaptive input shaping for single-link flexible manipulators using an algebraic identification , 2012 .

[5]  Amor Jnifene.  Active vibration control of flexible structures using delayed position feedback , 2007, Syst. Control. Lett..

[6]  Bin Wang,et al.  Genetic algorithm based active vibration control for a moving flexible smart beam driven by a pneumatic rod cylinder , 2012 .

[7]  Kumpati S. Narendra,et al.  Adaptive control using multiple models , 1997, IEEE Trans. Autom. Control..

[8]  Gary M. Bone,et al.  Accurate position control of a pneumatic actuator using on/off solenoid valves , 1997, Proceedings of International Conference on Robotics and Automation.

[9]  G. Alici,et al.  Modeling and inverse feedforward control for conducting polymer actuators with hysteresis , 2014 .

[10]  Alex Simpkins,et al.  System Identification: Theory for the User, 2nd Edition (Ljung, L.; 1999) [On the Shelf] , 2012, IEEE Robotics & Automation Magazine.

[11]  Philip Moore,et al.  A practical control strategy for servo-pneumatic actuator systems , 1999 .

[12]  Jan Wikander,et al.  Block-oriented approximate feedback linearization for control of pneumatic actuator system , 2004 .

[13]  Zhi-cheng Qiu,et al.  Vibration suppression of a pneumatic drive flexible manipulator using adaptive phase adjusting controller , 2015 .

[14]  Jinjun Shan,et al.  Slewing and vibration control of a single-link flexible manipulator by positive position feedback (PPF) , 2005 .

[15]  Jennie Si,et al.  Online learning control by association and reinforcement , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

[16]  Jeongho Cho,et al.  Modeling and inverse controller design for an unmanned aerial vehicle based on the self-organizing map , 2006, IEEE Transactions on Neural Networks.

[17]  Wayne J. Book,et al.  Modeling, design, and control of flexible manipulator arms: a tutorial review , 1990, 29th IEEE Conference on Decision and Control.

[18]  Gary M. Bone,et al.  Accurate position control of a pneumatic actuator using on/off solenoid valves , 1997 .

[19]  Carmelo di Castri,et al.  Exact modeling for control of flexible manipulators , 2012 .

[20]  Biao Huang,et al.  A decoupled multiple model approach for soft sensors design , 2011 .

[21]  Karel J. Keesman,et al.  System Identification: An Introduction , 2011 .

[22]  Graham C. Goodwin,et al.  Digital control and estimation : a unified approach , 1990 .

[23]  Bernard Widrow,et al.  Adaptive Inverse Control: A Signal Processing Approach , 2007 .

[24]  Nadine Gottschalk,et al.  Computer Controlled Systems Theory And Design , 2016 .

[25]  Horn-Sen Tzou,et al.  Design and testing of a hybrid polymeric electrostrictive/piezoelectric beam with bang–bang control , 2007 .

[26]  Masayoshi Tomizuka,et al.  Experimental flexible beam tip tracking control with a truncated series approximation to uncancelable inverse dynamics , 1994, IEEE Trans. Control. Syst. Technol..

[27]  Karl Johan Åström,et al.  Computer-controlled systems : theory and design (3. rev. Dover ed.) , 2011 .

[28]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[29]  Boris Rohal’-Ilkiv,et al.  Model Predictive Vibration Control: Efficient Constrained MPC Vibration Control for Lightly Damped Mechanical Structures , 2012 .