Sliding Mode Control for Flexible-link Manipulators Based on Adaptive Neural Networks

This paper mainly focuses on designing a sliding mode boundary controller for a single flexible-link manipulator based on adaptive radial basis function (RBF) neural network. The flexible manipulator in this paper is considered to be an Euler-Bernoulli beam. We first obtain a partial differential equation (PDE) model of single-link flexible manipulator by using Hamiltons approach. To improve the control robustness, the system uncertainties including modeling uncertainties and external disturbances are compensated by an adaptive neural approximator. Then, a sliding mode control method is designed to drive the joint to a desired position and rapidly suppress vibration on the beam. The stability of the closed-loop system is validated by using Lyapunov’s method based on infinite dimensional model, avoiding problems such as control spillovers caused by traditional finite dimensional truncated models. This novel controller only requires measuring the boundary information, which facilitates implementation in engineering practice. Favorable performance of the closed-loop system is demonstrated by numerical simulations.

[1]  Wei He,et al.  Adaptive Neural Network Control of an Uncertain Robot With Full-State Constraints , 2016, IEEE Transactions on Cybernetics.

[2]  Suwat Kuntanapreeda,et al.  Hysteretic Nonlinearity Observer Design Based on Kalman Filter for Piezo-actuated Flexible Beams with Control Applications , 2014, Int. J. Autom. Comput..

[3]  Miroslav Krstic,et al.  PDE Boundary Control for Flexible Articulated Wings on a Robotic Aircraft , 2013, IEEE Transactions on Robotics.

[4]  Tairen Sun,et al.  Robust adaptive neural network control for environmental boundary tracking by mobile robots , 2013 .

[5]  Peien Kuo,et al.  Nonlinear output feedback control of a flexible link using adaptive neural network: controller design , 2013 .

[6]  J. Prevost,et al.  A coupled sliding-surface approach for the trajectory control of a flexible-link robot based on a distributed dynamic model , 2005 .

[7]  Z. Luo.  Direct strain feedback control of flexible robot arms: new theoretical and experimental results , 1993, IEEE Trans. Autom. Control..

[8]  Shuang Zhang,et al.  Control Design for Nonlinear Flexible Wings of a Robotic Aircraft , 2017, IEEE Transactions on Control Systems Technology.

[9]  P. Dixit,et al.  Output feedback control of flexible link manipulator using sliding modes , 2012, 2012 7th International Conference on Electrical and Computer Engineering.

[10]  Tairen Sun,et al.  Adaptive Control for Nonaffine Nonlinear Systems Using Reliable Neural Network Approximation , 2017, IEEE Access.

[11]  Linjun Zhang,et al.  Observer-based partial differential equation boundary control for a flexible two-link manipulator in task space , 2012 .

[12]  S. Yurkovich,et al.  A method for solution of the Euler-Bernoulli beam equation in flexible-link robotic systems , 1989, IEEE 1989 International Conference on Systems Engineering.

[13]  Jinkun Liu,et al.  Distributed piezoelectric vibration control for a flexible-link manipulator based on an observer in the form of partial differential equations , 2016 .

[14]  Shailaja Kurode,et al.  Fractional order sliding mode control for single link flexible manipulator , 2013, 2013 IEEE International Conference on Control Applications (CCA).

[15]  Christian Bohn,et al.  Modeling and dual loop sliding mode control of a two flexible-link robot to reduce the transient response , 2016, 2016 24th Mediterranean Conference on Control and Automation (MED).

[16]  Shuzhi Sam Ge,et al.  Improving regulation of a single-link flexible manipulator with strain feedback , 1998, IEEE Trans. Robotics Autom..

[17]  Changyin Sun,et al.  Adaptive Neural Impedance Control of a Robotic Manipulator With Input Saturation , 2016, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[18]  Linjun Zhang,et al.  Adaptive boundary control for flexible two-link manipulator based on partial differential equation dynamic model , 2013 .

[19]  Jinkun Liu,et al.  Nonlinear PDE observer design for a flexible two-link manipulator , 2012, 2012 American Control Conference (ACC).

[20]  Xuefang Li,et al.  Adaptive Boundary Iterative Learning Control for an Euler–Bernoulli Beam System With Input Constraint , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[21]  Zhijia Zhao,et al.  Adaptive boundary control of an axially moving belt system with high acceleration/deceleration , 2016 .

[22]  Wei He,et al.  Modeling and vibration control of a flexible aerial refueling hose with variable lengths and input constraint , 2017, Autom..

[23]  Jonathan Becedas,et al.  Sliding mode tracking control of a very lightweight single-link flexible robot robust to payload changes and motor friction , 2012 .

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

[25]  Li Zhang,et al.  Iterative learning control for flexible manipulator using fourier basis function , 2015, Int. J. Autom. Comput..

[26]  Jinguo Liu,et al.  Experiments on fuzzy sliding mode variable structure control for vibration suppression of a rotating flexible beam , 2015 .

[27]  Hongbo Zhou,et al.  Neural network-based sliding mode adaptive control for robot manipulators , 2011, Neurocomputing.

[28]  Fumitoshi Matsuno,et al.  Simple Boundary Cooperative Control of Two One-Link Flexible Arms for Grasping , 2009, IEEE Transactions on Automatic Control.

[29]  Feng-Fei Jin,et al.  The active disturbance rejection and sliding mode control approach to the stabilization of the Euler-Bernoulli beam equation with boundary input disturbance , 2013, Autom..

[30]  S. M. Hoseini,et al.  Neural Network Adaptive Output Feedback Control of Flexible Link Manipulators , 2013 .