A Hybrid CKF-NNPID Controller for MIMO Nonlinear Control System
Article Sidebar
Main Article Content
Abstract
This paper presents a detailed study to demonstrate the online tuning dynamic neural network PID controller to improve a joint angle position output performance of 4- joint robotic arm. The proposed controller uses a new updating weight rule model of the neural network architecture using multi-loop calculation of the fusion of the gradient algorithm with the cubature Kalman filter (CKF) which can optimize the internal predicted state of the updated weights to improve the proposed controller performances, called a Hybrid CKF-NNPID controller. To evaluate the proposed controller performances, the demonstration by the Matlab simulation program is used to implement the proposed controller that connects to the 4-joint robotic arm system. In the experimental result, it shows that the proposed controller is a superior control method comparing with the other prior controllers even though the system is under the loading criteria, the proposed controller still potentially tracks the error and gives the best performances.
Article Details
Adna Sento, Department of Electronics Engineering, Faculty of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Thailand
Lecture at Thai-Nichi Institute of Technology.
Yuttana Kitjaidure, Department of Electronics Engineering, Faculty of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Thailand
Lecture at Department of Electronics Engineering, Faculty of Engineering King Mongkut’s Institute of Technology Ladkrabang
References
S. Cong and Y. Liang, “PID-Like Neural Network Nonlinear Adaptive Control for Uncertain
Multivariable Motion Control Systems,” in IEEE
Transactions on Industrial Electronics, vol. 56,
no.10, pp. 3872-3879, Oct. 2009.
H. Huy Anh, “Online tuning gain scheduling
MIMO neural PID control of the 2-axes pneumatic artificial muscle (PAM) robot arm,” Expert Systems with Applications, vol. 37, no. 9,
pp. 6547-6560, 2010.
W. Yu and J. Rosen, “Neural PID Control of
Robot Manipulators With Application to an Upper Limb Exoskeleton,” in IEEE Transactions
on Cybernetics, vol. 43, no. 2, pp. 673-684, April
V. Kumar, P. Gaur and A. Mittal, “ANN based
self tuned PID like adaptive controller design for
high performance PMSM position control,” Expert Systems with Applications, vol. 41, no. 17,
pp. 7995-8002, 2014.
F. Rossomando and C. Soria, “Design and Implementation of Adaptive Neural PID for Non
Linear Dynamics in Mobile Robots,” in IEEE
Latin America Transactions, vol. 13, no. 4, pp.
-918, April 2015.
J. L. Meza, R. Soto and J. Arriaga, “An Optimal Fuzzy Self-Tuning PID Controller for Robot
Manipulators via Genetic Algorithm,” Artificial
Intelligence, 2009. MICAI 2009. Eighth Mexican
ECTI TRANSACTIONS ON COMPUTER AND INFORMATION TECHNOLOGY VOL.10, NO.2 November 2016
International Conference on, Guanajuato, 2009,
pp. 21-26.
J. L. Meza, V. Santibanez, R. Soto and M. A.
Llama, “Fuzzy Self-Tuning PID Semiglobal Regulator for Robot Manipulators,” in IEEE Transactions on Industrial Electronics, vol. 59, no. 6,
pp. 2709-2717, June 2012.
J. Armendariz, V. Parra-Vega, R. GarcaRodrguez and S. Rosales, “Neuro-fuzzy selftuning of PID control for semiglobal exponential
tracking of robot arms,” Applied Soft Computing, vol. 25, pp. 139-148, 2014.
Y. Iiguni, H. Sakai and H. Tokumaru, “A realtime learning algorithm for a multilayered neural
network based on the extended Kalman filter,”
in IEEE Transactions on Signal Processing, vol.
, no. 4, pp. 959-966, Apr 1992.
I. Rivals and L. Personnaz, “A recursive algorithm based on the extended Kalman filter for
the training of feedforward neural models,” Neurocomputing, vol. 20, no. 1-3, pp. 279-294, 1998.
S. Haykin, Kalman filtering and neural networks.
New York: Wiley, 2001.
E. A. Wan and R. Van Der Merwe, “The unscented Kalman filter for nonlinear estimation,”
Adaptive Systems for Signal Processing, Communications, and Control Symposium 2000. ASSPCC. The IEEE 2000, Lake Louise, Alta.,
, pp. 153-158.
X. Wang and Y. Huang, “Convergence Study in
Extended Kalman Filter-Based Training of Recurrent Neural Networks,” in IEEE Transactions
on Neural Networks, vol. 22, no. 4, pp. 588-600,
April 2011.
H. Yang, J. Li and F. Ding, “A neural network
learning algorithm of chemical process modeling
based on the extended Kalman filter,” Neurocomputing, vol. 70, no. 4-6, pp. 625-632, 2007.
I. Arasaratnam and S. Haykin, “Cubature
Kalman Filters,” in IEEE Transactions on Automatic Control, vol. 54, no. 6, pp. 1254-1269,
June 2009.
D. Rumelhart, G. Hinton and R. Williams,
“Learning representations by back-propagating
errors,” Nature, vol. 323, no. 6088, pp. 533-536,
A. Sento and Y. Kitjaidure, “Neural network
controller based on PID using an extended
Kalman filter algorithm for multi-variable nonlinear control system,” 2016 Eighth International Conference on Advanced Computational
Intelligence (ICACI), Chiang Mai, 2016, pp. 302-
C. Sheng, J. Zhao, Y. Liu and W. Wang, “Prediction for noisy nonlinear time series by echo
state network based on dual estimation,” Neurocomputing, vol. 82, pp. 186-195, 2012.
B. Safarinejadian, M. A. Tajeddini and A.
Ramezani, “Predict time series using extended,
unscented, and cubature Kalman filters based
on feed-forward neural network algorithm,” Control, Instrumentation, and Automation (ICCIA), 2013 3rd International Conference on,
Tehran, 2013, pp. 159-164.
A. Mitchell, “Online Courses and Online Teaching Strategies in Higher Education,” Creative
Education, vol. 05, no. 23, pp. 2017-2019, 2014.
I. Arasaratnam and S. Haykin, “Nonlinear
Bayesian Filters for Training Recurrent Neural
Networks,” MICAI 2008: Advances in Artificial
Intelligence: 7th Mexican International Conference on Artificial Intelligence, pp. 12-33, 2008.
A. Chernodub, “Local control gradients criterion
for selection of neuroemulators for model reference adaptive neurocontrol,” Optical Memory
and Neural Networks, vol. 21, no. 2, pp. 126-131,
F. Yang, W. Sun, G. Lin and W. Zhang, “Prediction of Military Vehicles Drawbar Pull Based
on an Improved Relevance Vector Machine and
Real Vehicle Tests,” Sensors, vol. 16, no. 3, p.
, 2016.
J. Zarei and E. Shokri, “Convergence analysis
of nonlinear filtering based on cubature Kalman
filter,” in IET Science, Measurement & Technology, vol. 9, no. 3, pp. 294-305, 5 2015.
S. Wang, J. Feng and C. Tse, “Novel cubature
Kalman filtering for systems involving nonlinear
states and linear measurements,” AEU - International Journal of Electronics and Communications, vol. 69, no. 1, pp. 314-320, 2015.