A hybrid backstepping-like nonlinear control of a robot-trailer system

Singh, Aditya; Hung, John Y.; Selfridge, Bob

doi:10.1109/iecon.2012.6388800

Cited by 6 publications

(1 citation statement)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This work proposed the robust adaptive neural network (RANN)-based backstepping (Sadowska and Huijberts, 2013;Singh et al, 2012;Chih-Min and Hsin-Yi, 2014) control design for multi-input multi-output (MIMO) nonlinear systems with friction, dead-zone and backlash compensation. The RANN-based backstepping control technique is designed to stabilize the dynamics of nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

Adaptive neural network based backstepping control design for MIMO nonlinear systems with actuator nonlinearities

Jamil

Kongprawechnon

Raza

2016

Aircraft Eng & Aerospace Tech

View full text Add to dashboard Cite

2016),"A conservative sliding mesh coupling procedure for U-RANS flow simulations", Aircraft Engineering and Aerospace Technology, Vol. 88 Iss 1 pp. 151-158 http://dx.If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. AbstractPurpose -The purpose of the proposed research methodology is to control the trajectory tracking of EDRM and also to cancel out the effect of no-smooth nonlinearities, which affect the system performance badly. Design/methodology/approach -Robust adaptive neural network (RANN)-based backstepping control design methodology is presented in this paper. The proposed design methodology improves the trajectory tracking and running mean error. Findings -The running mean error results show that the convergence of the proposed RANN-based backstepping technique is very fast as compare to the conventional PD control and due to this proposed control technique, the EDRM follows its desired trajectory perfectly. Practical implications -The EDRM trajectory tracking performance increases which leads to a better working position of EDRM. Originality/value -The originality of this research article is 93 per cent. Backstepping control designMuhammad Usman Jamil et al.

show abstract

Section: Introductionmentioning

confidence: 99%