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Adaptive RBF Neural Network Sliding Mode Control for a DEAP Linear Actuator

Volume 13, Number 4, July 2017 - Paper 7 - pp. 400-408
DOI: 10.23940/ijpe.17.04.p7.400408

Dehui Qiua, Yu Chena, Yuan Lib

aCollege of Information Engineering, Capital Normal University, Beijing 100048, China
bSchool of Automation, Beijing Institute of Technology, Beijing, 100081, China

(Submitted on January 14, 2017; Revised on May 14, 2017; Accepted on June 17, 2017)


Dielectric electro-active polymer (DEAP) is a new smart material named “artificial muscles”, which has a remarkable potential in the field of biomimetic robots. However, hysteresis nonlinearity widely exists in this material, which will reduce the performance of tracking precision and system stability. To deal with this situation, a radial basis function (RBF) neural network combined with sliding mode control algorithm is presented for a second-order DEAP linear actuator. Firstly, an inverse hysteresis operator based on Prandtl-Ishlinskii (P-I) model is used to eliminate hysteresis behavior. Secondly, an adaptive RBF neural network sliding mode controller is designed to obtain high tracking accuracy and keep system stability. The proposed algorithm makes the tracking error converge to zero and keeps the system globally stable in the case of external disturbances and parameter variations. Simulation results demonstrate that the proposed controller has the superiority to a pure sliding mode controller.


References: 18

1.    Bar-Cohen, Y., “Biologically Inspired Intelligent Robots using Artificial Muscles”, Strain, vol. 5051, no. 1, pp.19-24, 2003.
2.    Chen, H., Wang, Y., Sheng, J., Chang, L., Wang, Y., “Research of Electro-active Polymer and Its Application in Actuators”, Chinese Journal of Mechanical Engineering, vol. 49, no. 6, pp. 205-214, 2013.
3.    Chen, X., Hisayama, T., “Adaptive Sliding-mode Position Control for Piezo-actuated Stage”, IEEE Transactions on Industrial Electronics, vol. 55, no. 11, pp. 3927-3934, 2008.
4.    Chen, X., Su, C. Y., “Adaptive Control for Ionic Polymer-Metal Composite Actuators”, IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 46, no. 10, pp. 1468-1477, 2016.
5.    Edardar, M., Tan, X., Khalil, H. K., “Sliding-mode Tracking Control of Piezo-actuated Nanopositioners”, in Proceedings of American Control Conference, pp. 3825-3830, 2012.
6.    Habineza, D., Rakotondrabe, M., Gorrec, Y. L., “Bouc-Wen Modeling and Feedforward Control of Multivariable Hysteresis in Piezoelectric Systems: Application to a 3-DoF Piezotube Scanner”, IEEE Transactions on Control Systems Technology, vol. 23, no. 5, pp. 1797-1806, 2015.
7.    Krejci, P., Kuhnen, K., “Existence, Uniqueness and L∞-Stability of the Prandtl-Ishlinskii Hysteresis and Creep Compensator”, European Journal of Control, vol. 14, no. 5, pp. 409-417, 2008.
8.    Krejci, P., Kuhnen, K., “Inverse Control of Systems with Hysteresis and Creep”, in Proceedings of Conf. on Control Theory and Applications, pp. 185-192, 2001.
9.    Kuhnen, K., “Modeling, Identification and Compensation of Complex Hysteretic Nonlinearities: A Modified Prandtl-Ishlinskii Approach”, European Journal of Control, vol. 9, no. 9, pp. 407-418, 2003.
10.    Lian, J. W., Chen, H. Y., “Feedforward and Feedback Control for Piezoelectric-actuated Systems using Inverse Prandtl-Ishlinskii Model and Particle Swarm Optimization”, in Proceedings of Conf. on Advanced Mechatronic Systems, pp.313-318, 2014.
11.    Main, J. A., Garcia, E., “Piezoelectric Stack Actuators and Control System Design: Strategies and Pitfalls”, Journal of Guidance Control Dynamics, vol. 20, no. 3, pp. 479-485, 2012.
12.    Mayergoyz, I. D., “Mathematical Models of Hysteresis”, Physical Review Letters, vol. 22, no. 5, pp. 603-608, 1986.
13.    Riccardi, L., Naso, D., Turchiano, B., Janocha, H., “On PID Control of Dynamic Systems with Hysteresis using a Prandtl-Ishlinskii Model”, in Proceedings of American Control Conference, pp. 1670-1675, 2012.
14.    Rizzello, G., Naso, D., York, A., Seelecke, S., “Modeling, Identification, and Control of a Dielectric Electro-Active Polymer Positioning System”, IEEE Transactions on Control Systems Technology, vol. 23, no. 2, pp. 632-643, 2015.
15.    Song, Z., Long, Y., Sun, J., “General on Modeling and Control of Hysteresis Nonlinear System”, Journal of Naval Aeronautical and Astronautical University, vol. 29, no. 6, pp. 528-534, 2014.
16.    Wang, G., Chen, D., Chen, K., Zhang, Z., “The Current Research Status and Development Strategy on Biomimetic Robot”, Journal of Mechanical Engineering, vol. 51, no. 13, pp. 27-44, 2015.
17.    Zhang, J., Iyer, K., Simeonov, A., Yip, M. C., “Modeling and Inverse Compensation of Hysteresis in Supercoiled Polymer Artificial Muscles”, IEEE Robotics and Automation Letters, vol. 2, no. 2, pp. 773-780, 2017.
18.    Zheng, J., Wang, Q., Li, Y., “Adaptive Sliding Model Control for Linear Actuator with Hysteresis using a Prandtl-Ishlinskii Model”, in Proceedings of Conf. on Robotics and Biomimetics, pp. 2553-2557, 2015.


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