A novel intelligent fast terminal sliding mode control for a class of nonlinear systems: application to atomic force microscope

This paper addresses the problem of finite-time robust control using an intelligent fast terminal sliding mode control method for a class of nonlinear systems in presence of bounded uncertainty and disturbance. In the proposed method, adaptive neuro-fuzzy inference systems are utilized to determine some parameters in the nonlinear sliding surface based on the measured value of the sliding surface and the system error at each time and thereby variable nonlinear sliding surface is provided. Furthermore, an intelligent optimization algorithm namely honey bee algorithm is also applied to determine the optimal weights of the neuro-fuzzy network. Finite time convergence with increased speed and also chattering-free response are the main advantages of the proposed method compared with the conventional terminal sliding mode control methods. The proposed controller is applied to an atomic force microscope in attendance of uncertainty and disturbance. The simulation results demonstrate considerable success of the method, and show that the error reaches zero in a very short time without chattering.

[1]  Keigo Watanabe,et al.  An optimized Takagi-Sugeno type neuro-fuzzy system for modeling robot manipulators , 2006, Neural Computing & Applications.

[2]  Arie Levant,et al.  Homogeneity approach to high-order sliding mode design , 2005, Autom..

[3]  Vadim I. Utkin,et al.  Sliding Modes in Control and Optimization , 1992, Communications and Control Engineering Series.

[4]  Zahra Rahmani,et al.  Designing fuzzy logic controller based on combination of terminal sliding mode and state feedback controllers for stabilizing chaotic behaviour in rod-type plasma torch system , 2016 .

[5]  A. Sedigh,et al.  Training ANFIS system with DE algorithm , 2011, The Fourth International Workshop on Advanced Computational Intelligence.

[6]  Sendren Sheng-Dong Xu,et al.  Study of Takagi-Sugeno fuzzy-based terminal-sliding mode fault-tolerant control , 2014 .

[7]  S. Mobayen,et al.  An adaptive fast terminal sliding mode control combined with global sliding mode scheme for tracking control of uncertain nonlinear third-order systems , 2015 .

[8]  José Manoel Balthazar,et al.  Nonlinear Control System Applied to Atomic Force Microscope Including Parametric Errors , 2013 .

[9]  Xinghuo Yu,et al.  Terminal sliding mode control design for uncertain dynamic systems , 1998 .

[10]  Arie Levant,et al.  Principles of 2-sliding mode design , 2007, Autom..

[11]  Tzuu-Hseng S. Li,et al.  Fuzzy terminal sliding-mode controller for robotic manipulators , 2005, IEEE International Conference on Mechatronics, 2005. ICM '05..

[12]  Tianyou Chai,et al.  Neural-Network-Based Terminal Sliding-Mode Control of Robotic Manipulators Including Actuator Dynamics , 2009, IEEE Transactions on Industrial Electronics.

[13]  Xinghuo Yu,et al.  Fast terminal sliding-mode control design for nonlinear dynamical systems , 2002 .

[14]  Kazuo Tanaka,et al.  Intelligent nonsingular terminal sliding-mode control via perturbed fuzzy neural network , 2015, Eng. Appl. Artif. Intell..

[15]  Xinghuo Yu,et al.  Model reference adaptive control systems with terminal sliding modes , 1996 .

[16]  Keqiang Li,et al.  Terminal sliding mode control of automated car-following system without reliance on longitudinal acceleration information , 2015 .

[17]  K. S. Ramaswami,et al.  A Fusion Model Integrating ANFIS and Artificial Immune Algorithm for Forecasting Indian Stock Market , 2011 .

[18]  Rong-Jong Wai,et al.  Fuzzy-Neural-Network Inherited Sliding-Mode Control for Robot Manipulator Including Actuator Dynamics , 2013, IEEE Transactions on Neural Networks and Learning Systems.

[19]  M. Zak.  Terminal attractors for addressable memory in neural networks , 1988 .

[20]  Saleh Mobayen,et al.  Fast terminal sliding mode controller design for nonlinear second-order systems with time-varying uncertainties , 2015, Complex..

[21]  D. Pham,et al.  THE BEES ALGORITHM, A NOVEL TOOL FOR COMPLEX OPTIMISATION PROBLEMS , 2006 .

[22]  Murti V. Salapaka,et al.  Dynamical analysis and control of microcantilevers , 1999, Autom..

[23]  Gangbing Song,et al.  Chattering-free fuzzy adaptive robust sliding-mode vibration control of a smart flexible beam , 2008 .

[24]  M. Sitti.  Atomic force microscope probe based controlled pushing for nanotribological characterization , 2004, IEEE/ASME Transactions on Mechatronics.

[25]  Igor Mezic,et al.  Complex Dynamics in a Harmonically Excited Lennard-Jones Oscillator: Microcantilever-Sample Interaction in Scanning Probe Microscopes , 2000 .

[26]  Aria Alasty,et al.  Controlling chaos in tapping mode atomic force microscopes using improved minimum entropy control , 2013 .

[27]  S. Mobayen,et al.  Adaptive finite-time tracking control of uncertain non-linear n-order systems with unmatched uncertainties , 2016 .

[28]  Hongbo Shi,et al.  Adaptive position tracking control of permanent magnet synchronous motor based on RBF fast terminal sliding mode control , 2013, Neurocomputing.

[29]  Jože Balič,et al.  Hybrid ANFIS-ants system based optimisation of turning parameters , 2009 .

[30]  Syuan-Yi Chen,et al.  Robust Nonsingular Terminal Sliding-Mode Control for Nonlinear Magnetic Bearing System , 2011, IEEE Transactions on Control Systems Technology.

[31]  N. Jalili,et al.  Vibration control of AFM tip for nano-manipulation using combined sliding mode techniques , 2007, 2007 7th IEEE Conference on Nanotechnology (IEEE NANO).

[32]  Moharam Habibnejad Korayem,et al.  Control of an atomic force microscopy probe during nano-manipulation via the sliding mode method , 2012 .

[33]  Mohammad Teshnehlab,et al.  Training ANFIS as an identifier with intelligent hybrid stable learning algorithm based on particle swarm optimization and extended Kalman filter , 2009, Fuzzy Sets Syst..

[34]  Abbas Erfanian,et al.  Adaptive fuzzy terminal sliding mode control for a class of MIMO uncertain nonlinear systems , 2011, Fuzzy Sets Syst..

[35]  Dongya Zhao,et al.  Adaptive motion/force control of constrained manipulators using a new fast terminal sliding mode , 2014, Int. J. Comput. Appl. Technol..

[36]  Hasan Komurcugil,et al.  Time-varying sliding-coefficient-based terminal sliding mode control methods for a class of fourth-order nonlinear systems , 2013 .

[37]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[38]  Aria Alasty,et al.  Linear optimal control of continuous time chaotic systems. , 2014, ISA transactions.

[39]  Xinghuo Yu,et al.  Terminal sliding mode control of MIMO linear systems , 1997 .

[40]  G. Bartolini,et al.  Chattering avoidance by second-order sliding mode control , 1998, IEEE Trans. Autom. Control..

[41]  John Y. Hung,et al.  Variable structure control: a survey , 1993, IEEE Trans. Ind. Electron..

[42]  Chin-Wang Tao,et al.  Adaptive fuzzy terminal sliding mode controller for linear systems with mismatched time-varying uncertainties , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[43]  Saleh Mobayen,et al.  Disturbance observer and finite-time tracker design of disturbed third-order nonholonomic systems using terminal sliding mode , 2017 .

[44]  Faa-Jeng Lin,et al.  Three-Degree-of-Freedom Dynamic Model-Based Intelligent Nonsingular Terminal Sliding Mode Control for a Gantry Position Stage , 2012, IEEE Transactions on Fuzzy Systems.