A novel adaptive feed-forward-PID controller of a SCARA parallel robot using pneumatic artificial muscle actuator based on neural network and modified differential evolution algorithm

This paper proposes a novel control system combining adaptively feed-forward neural controller and PID controller to control the joint-angle position of the SCARA parallel robot using the pneumatic artificial muscle (PAM) actuator. Firstly, the proposed inverse neural NARX (INN) model dynamically identifies all nonlinear features of the SCARA parallel PAM robot. Parameters of the inverse neural NARX model are optimized with the modified differential evolution (MDE) algorithm. Secondly, combining the inverse neural NARX model that provides a feed-forward control value from the desired joint position and the conventional PID controller applied to improve the precision and reject the steady state error in the joint position control. Finally, the new adaptive back-propagation (aBP) algorithm, based on Sugeno fuzzy system, proposed for online updating the weight values of the inverse neural NARX model as to adapt well to the disturbances and dynamic variations in its operation. Experimental tests confirmed the performance and merits of the proposed control scheme in comparison with the traditional control methods.

[1]  Saeed Tavakoli,et al.  Improved Cuckoo Search Algorithm for Feed forward Neural Network Training , 2011 .

[2]  조규진,et al.  링키지와 결합된 공압 인공근육을 이용한 손 외골격 제작 , 2013 .

[3]  Jun Wu,et al.  Tracking control of pneumatic artificial muscle actuators based on sliding mode and non-linear disturbance observer , 2010 .

[4]  Xiao Li,et al.  Implicit Generalized Predictive Control of Hip-Joint Rehabilitation Training Device Driven by Pneumatic Muscle Actuator , 2011 .

[5]  Amit Konar,et al.  Differential Evolution Using a Neighborhood-Based Mutation Operator , 2009, IEEE Transactions on Evolutionary Computation.

[6]  Pierre Lopez,et al.  Modeling and control of McKibben artificial muscle robot actuators , 2000 .

[7]  Jie Zhao,et al.  BP neural network tuned PID controller for position tracking of a pneumatic artificial muscle. , 2015, Technology and health care : official journal of the European Society for Engineering and Medicine.

[8]  Aiko Miyamura Ideta,et al.  Stability of feedback error learning method with time delay , 2006, Neurocomputing.

[9]  Joni-Kristian Kämäräinen,et al.  Differential Evolution Training Algorithm for Feed-Forward Neural Networks , 2003, Neural Processing Letters.

[10]  Zude Zhou,et al.  Modeling of pneumatic artificial muscle using a hybrid artificial neural network approach , 2015 .

[11]  Kyoung Kwan Ahn,et al.  Design and implementation of an adaptive recurrent neural networks (ARNN) controller of the pneumatic artificial muscle (PAM) manipulator , 2009 .

[12]  H. F. Schulte.  The characteristics of the McKibben artificial muscle , 1961 .

[13]  George Nikolakopoulos,et al.  Design and development of an exoskeletal wrist prototype via pneumatic artificial muscles , 2015 .

[14]  Janez Brest,et al.  Performance comparison of self-adaptive and adaptive differential evolution algorithms , 2007, Soft Comput..

[15]  Bertrand Tondu,et al.  A Seven-degrees-of-freedom Robot-arm Driven by Pneumatic Artificial Muscles for Humanoid Robots , 2005, Int. J. Robotics Res..

[16]  Christian Blum,et al.  An ant colony optimization algorithm for continuous optimization: application to feed-forward neural network training , 2007, Neural Computing and Applications.

[17]  Qingfu Zhang,et al.  Differential Evolution With Composite Trial Vector Generation Strategies and Control Parameters , 2011, IEEE Transactions on Evolutionary Computation.

[18]  Adam P. Piotrowski,et al.  Differential Evolution algorithms applied to Neural Network training suffer from stagnation , 2014, Appl. Soft Comput..

[19]  Sehraneh Ghaemi,et al.  Application of type-2 fuzzy logic system for load frequency control using feedback error learning approaches , 2014, Appl. Soft Comput..

[20]  Mohammad Bagher Menhaj,et al.  Training feedforward networks with the Marquardt algorithm , 1994, IEEE Trans. Neural Networks.

[21]  P. N. Suganthan,et al.  Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization , 2009, IEEE Transactions on Evolutionary Computation.

[22]  T. Noritsugu,et al.  Wearable power assist robot driven with pneumatic rubber artificial muscles , 2009, 2009 International Conference on Networking, Sensing and Control.

[23]  George Nikolakopoulos,et al.  Non-linear control of Pneumatic Artificial Muscles , 2013, 21st Mediterranean Conference on Control and Automation.

[24]  Chao Zhang,et al.  Fuzzy neural network control of the rehabilitation robotic arm driven by pneumatic muscles , 2015, Ind. Robot.

[25]  Bidyadhar Subudhi,et al.  Evolutionary Computing Approaches to System Identification , 2016 .

[26]  Alberto Tesi,et al.  On the Problem of Local Minima in Backpropagation , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[27]  Norman M. Wereley,et al.  Wind Tunnel Testing of a Helicopter Rotor Trailing Edge Flap Actuated via Pneumatic Artificial Muscles , 2011 .

[28]  Kyoung Kwan Ahn,et al.  A feedforward neural network fuzzy grey predictor-based controller for force control of an electro-hydraulic actuator , 2016 .

[29]  Liang Yu Su.  Micro-Vibration Mechanism of Micro-Gears Fault Diagnosis Based on Fault Characteristics and Differential Evolution Wavelet Neural Networks , 2014 .

[30]  Mohammad Teshnehlab,et al.  Recurrent fuzzy neural network by using feedback error learning approaches for LFC in interconnected power system , 2009 .

[31]  Benjamin K.S. Woods,et al.  Fatigue life testing of swaged pneumatic artificial muscles as actuators for aerospace applications , 2012 .

[32]  Yongji Wang,et al.  Modeling and control of McKibben artificial muscle enhanced with echo state networks , 2012 .

[33]  Xiangrong Shen,et al.  Nonlinear model-based control of pneumatic artificial muscle servo systems , 2010 .

[34]  Bidyadhar Subudhi,et al.  Nonlinear system identification using memetic differential evolution trained neural networks , 2011, Neurocomputing.

[35]  Dirk Lefeber,et al.  Pneumatic artificial muscles: Actuators for robotics and automation , 2002 .

[36]  R. Storn,et al.  Differential Evolution - A simple and efficient adaptive scheme for global optimization over continuous spaces , 2004 .

[37]  Jouni Lampinen,et al.  A Fuzzy Adaptive Differential Evolution Algorithm , 2005, Soft Comput..

[38]  J. Landaluze,et al.  Modelling in Modelica and position control of a 1-DoF set-up powered by pneumatic muscles , 2010 .

[39]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[40]  Silvia Curteanu,et al.  Neural networks and differential evolution algorithm applied for modelling the depollution process of some gaseous streams , 2014, Environmental Science and Pollution Research.

[41]  Erick Ball,et al.  Effects of Bladder Geometry in Pneumatic Artificial Muscles , 2016 .

[42]  Santosha K. Dwivedy,et al.  Control of pneumatic artificial muscle system through experimental modelling , 2012 .

[43]  Jun Nakanishi,et al.  Feedback error learning and nonlinear adaptive control , 2004, Neural Networks.

[44]  Adam Slowik,et al.  Application of an Adaptive Differential Evolution Algorithm With Multiple Trial Vectors to Artificial Neural Network Training , 2011, IEEE Transactions on Industrial Electronics.

[45]  Saku Kukkonen,et al.  Real-parameter optimization with differential evolution , 2005, 2005 IEEE Congress on Evolutionary Computation.

[46]  H. Tamura,et al.  An improved backpropagation algorithm to avoid the local minima problem , 2004, Neurocomputing.

[47]  Madhumita Panda,et al.  A Hybrid Differential Evolution and Back-Propagation Algorithm for Feedforward Neural Network Training , 2013 .

[48]  Kyoung Kwan Ahn,et al.  Synchronization controller for a 3-R planar parallel pneumatic artificial muscle (PAM) robot using modified ANFIS algorithm , 2013 .

[49]  N. Hoshimiya,et al.  Joint angle control by FES using a feedback error learning controller , 2005, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[50]  Taro Nakamura,et al.  Development of insertion-type peristalsis pump using pneumatic artificial muscles , 2015, Adv. Robotics.

[51]  Prasant Kumar Pattnaik,et al.  Artificial Neural Network trained by Particle Swarm Optimization for non-linear channel equalization , 2014, Expert Syst. Appl..

[52]  Ho Pham Huy Anh,et al.  Online tuning gain scheduling MIMO neural PID control of the 2-axes pneumatic artificial muscle (PAM) robot arm , 2010, Expert Syst. Appl..

[53]  Wei Zhao,et al.  Design and Experimental Research of Pneumatic Soft Humanoid Robot Hand , 2015, RiTA.

[54]  Hak-Keung Lam,et al.  Tuning of the structure and parameters of a neural network using an improved genetic algorithm , 2003, IEEE Trans. Neural Networks.