Sliding-mode and proportional-derivative-type motion control with radial basis function neural network based estimators for wheeled vehicles

Abstract

An obstacle avoidance problem of rear-steered wheeled vehicles in consideration of the presence of uncertainties is addressed. Modelling errors and additional uncertainties are taken into consideration. Controller designs for driving and steering motors are designed. A proportional-derivative-type driving motor controller and a sliding-mode steering controller combined with radial basis function neural network (RBFNN) based estimators are proposed. The convergence properties of the RBFNN-based estimators are proven by the Stone–Weierstrass theorem. The stability of the proposed control law is proven using Lyapunov stability analysis. The obstacle avoidance strategy utilising the sliding surface adjustment to an existing navigation method is presented. It is concluded that the driving velocity and steering-angle performances of the proposed control system are satisfactory.

Keywords:

• obstacle avoidance
• wheeled vehicle
• estimation
• sliding-mode control
• proportional derivative control
• radial basis function networks

Acknowledgements

This research was supported by the National Research Foundation of Korea funded by the Ministry of Education, Science and Technology, Korea (No. MEST-2012-R1A2A2A01046411) and the World Class University Program (No. R31-20004).

Additional information

Notes on contributors

Anugrah K. Pamosoaji

Anugrah K. Pamosoaji received his BEng and MEng degrees in Electrical Engineering from the Bandung Institute of Technology, Indonesia, in 2003 and 2006, respectively. He is currently working toward his PhD in the School of Mechanical Engineering, Pusan National University, Korea. His research interests include robotics, path planning, control systems and multivehicle systems.

Pham Thuong Cat

Pham Thuong Cat received his MS degree in Computer Engineering from the Budapest Technical University in 1972 and PhD in Control Engineering from the Hungarian Academy of Sciences (MTA) in 1977. From 1985 to 1988, he was a Postdoctoral Fellow at MTA SzTAKI, the Research Institute of Computer and Automation of MTA, and received his DSc degree in Robotics from the Hungarian Academy of Science in 1988. He is an Honorary Research Professor in Computational Sciences at MTA SzTAKI. Since 1979, he is involved in both research and teaching PhD courses at the Institute of Information Technology, Vietnamese Academy of Science and Technology. Dr Cat is also serving as the Editor-in-Chief of the Journal of Computer Science and Cybernetics of the Vietnamese Academy of Science and Technology. He is the Vice-President of the Vietnamese Association of Mechatronics. His research interests include robotics, control theory, cellular neural networks and embedded control systems. He co-authored four books and published over 140 papers on national and international journals and conference proceedings.

Keum-Shik Hong

Keum-Shik Hong received his BSME from Seoul National University in 1979, his MSME from Columbia University, New York, in 1987, and both an MS degree in Applied Mathematics and a PhD in ME from the University of Illinois at Urbana in 1991. His laboratory was designated as a National Research Laboratory by the MEST of Korea in 2003. In 2009, under the World Class University Program, he established the Department of Cogno-Mechatronics Engineering. Dr Hong also served as the Editor-in-Chief of the Journal of Mechanical Science and Technology (2008–2011), AE of Automatica (2000–2006) and Deputy Editor-in-Chief of the IJCAS (2003–2005). He was also the General Secretary of the Asian Control Association (2006–2008), and the Organizing Chair of the ICROS-SICE International Conference 2009, Japan. He has received many awards including the Best Paper Award from the KFSTS of Korea (1999), the Harashima Mechatronics Award (2003), the Automatica Certificate of Outstanding Service (06), the Presidential Award of Korea (2007), the ICROS Achievement Award (2009), the IJCAS Contribution Award (2010), the Best Teaching Professor Award (2010), the IJCAS Award (2011) and the Premier Professor Award (2011), among others. Dr Hong's current research interests include brain–computer interface, adaptive control and distributed parameter systems.