Title: Adaptive self-recurrent wavelet neural network and sliding mode controller/observer for a slider crank mechanism
Authors: Ahmad Taher Azar; Fernando E. Serrano; Josep M. Rossell; Sundarapandian Vaidyanathan; Quanmin Zhu
Addresses: Robotics and Internet-of-Things Lab (RIOTU), Prince Sultan University, Riyadh, Saudi Arabia; Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt ' Universidad Tecnológica Centroamericana (UNITEC), Zona Jacaleapa, Tegucigalpa, San Pedro Sula, Honduras ' Department of Mathematics, Universitat Politécnica de Catalunya (UPC), Barcelona, Catalonia, Spain ' Research and Development Centre, Vel Tech University, Chennai, Tamil Nadu, India ' Department of Engineering Design and Mathematics, University of the West of England, Bristol, UK
Abstract: In this paper, a novel control strategy based on an adaptive Self-Recurrent Wavelet Neural Network (SRWNN) and a sliding mode controller/observer for a slider crank mechanism is proposed. The aim is to reduce the tracking error of the linear displacement of this mechanism while following a specified trajectory. The controller design consists of two parts. The first one is a sliding mode control strategy and the second part is an SRWNN controller. This controller is trained offline first, and then the SRWNN weights are updated online by the adaptive control law. Apart from the hybrid control strategy proposed in this paper, a velocity observer is implemented to replace the use of velocity sensors. The outcomes obtained in the numerical experiment section prove that the smallest tracking error is obtained for the linear and angular displacements in comparison with other strategies found in literature due to the uncertainty and disturbance rejection properties of the sliding mode and the self-recurrent wavelet neural network controllers.
Keywords: adaptive wavelet neural networks; sliding mode control; sliding mode observer; slider crank mechanism.
International Journal of Computer Applications in Technology, 2020 Vol.63 No.4, pp.273 - 285
Received: 24 Jul 2019
Accepted: 25 Dec 2019
Published online: 19 Oct 2020 *