Experimental verification of model-free active vibration control approach using virtually controlled object

The purpose of this study is to develop a simple and practical controller design method without modeling controlled objects. In this technique, modeling of the controlled object is not necessary and a controller is designed with an actuator model, which includes a single-degree-of-freedom virtual structure inserted between the actuator and the controlled object. The parameters of the virtual structure are determined so that indirect active vibration suppression is effectively achieved by considering the frequency transfer function from the vibration response of the controlled object to that of the virtual structure. Since the actuator model, which includes a virtually controlled object, is a simple low-order system, a controller with high control performance can be designed by traditional model-based optimal control theory. In this research, a mixed H 2 / H ∞ controller is designed considering both control performance and robust stability. The effectiveness of the proposed method is validated experimentally. The robustness of the controller is demonstrated by applying the same controller to various structures.

[1]  Magdalene Marinaki,et al.  Fuzzy control optimized by PSO for vibration suppression of beams , 2010 .

[2]  Alok Madan,et al.  Vibration control of building structures using self-organizing and self-learning neural networks , 2005 .

[3]  Chuen-Jyh Chen,et al.  Structural Vibration Suppression by a Neural-Network Controller with a Mass-Damper Actuator , 2006 .

[4]  Jianping Yuan,et al.  Adaptive model-free constrained control of postcapture flexible spacecraft: a Euler–Lagrange approach , 2018 .

[5]  Zhang Pu-zhen Active noise control using a simplified fuzzy neural network , 2007 .

[6]  İlhami Yiğit Model free sliding mode stabilizing control of a real rotary inverted pendulum , 2017 .

[7]  Itsuro Kajiwara,et al.  Active vibration suppression of membrane structures and evaluation with a non-contact laser excitation vibration test , 2017 .

[8]  Cheng-Yuan Chang,et al.  Parallel neural network combined with sliding mode control in overhead crane control system , 2014 .

[9]  Jie Zhou,et al.  Improved PI neural network-based tension control for stranded wire helical springs manufacturing , 2017 .

[10]  Kelly Cohen,et al.  Enhancement of a tuned mass damper for building structures using fuzzy logic , 2013 .

[11]  Van-Binh Bui,et al.  Vibration control of uncertain structures with actuator saturation using hedge-algebras-based fuzzy controller , 2017 .

[12]  Itsuro Kajiwara,et al.  Experimental verification of a real-time tuning method of a model-based controller by perturbations to its poles , 2018, Mechanical Systems and Signal Processing.

[13]  Onur Avci,et al.  Active vibration control of flexible cantilever plates using piezoelectric materials and artificial neural networks , 2016 .

[14]  Itsuro Kajiwara,et al.  Vibration Control of Automotive Drive System With Nonlinear Gear Backlash , 2019, Journal of Dynamic Systems, Measurement, and Control.

[15]  P. Gahinet,et al.  H∞ design with pole placement constraints: an LMI approach , 1996, IEEE Trans. Autom. Control..

[16]  Sandor M. Veres,et al.  Model-free frequency domain iterative active sound and vibration control , 2003 .

[17]  Şahin Yildirim Vibration control of suspension systems using a proposed neural network , 2004 .

[18]  Wen Yu,et al.  Active vibration control of building structures using fuzzy proportional-derivative/proportional-integral-derivative control , 2015 .

[19]  Shuang Gao,et al.  Particle swarm optimization-based neural network control for an electro-hydraulic servo system , 2014 .

[20]  Lu Lu,et al.  Functional link artificial neural network filter based on the q-gradient for nonlinear active noise control , 2018, Journal of Sound and Vibration.

[21]  Heikki Handroos,et al.  Application of neural network in suppressing mechanical vibration of a permanent magnet linear motor , 2008 .

[22]  Paul C.-P. Chao,et al.  Boundary control of an axially moving string via fuzzy sliding-mode control and fuzzy neural network methods , 2003 .

[23]  P. Gahinet,et al.  H design with pole placement constraints , 2018 .

[24]  K. V. Gangadharan,et al.  Design and development of a model free robust controller for active control of dominant flexural modes of vibrations in a smart system , 2015 .

[25]  Manu Sharma,et al.  Optimization Criteria for Optimal Placement of Piezoelectric Sensors and Actuators on a Smart Structure: A Technical Review , 2010 .

[26]  Huachun Wu,et al.  Fuzzy control of a semi-active multiple degree-of-freedom vibration isolation system , 2015 .

[27]  Akio Nagamatsu,et al.  Motion and Vibration Control of Flexible-Link Mechanism with Smart Structure. , 2001 .

[28]  Jue Wang,et al.  Implementation of model-free motion control for active suspension systems , 2019, Mechanical Systems and Signal Processing.

[29]  Yang Yu,et al.  Semi-active control of magnetorheological elastomer base isolation system utilising learning-based inverse model , 2017 .

[30]  J. Der Hagopian,et al.  Fuzzy Modal Active Control of Flexible Structures , 2005 .

[31]  Jan Swevers,et al.  A model-free control structure for the on-line tuning of the semi-active suspension of a passenger car , 2007 .