Authors
Chao Yang, Yingmin Jia
Corresponding Author
Chao Yang
Available Online 1 September 2015.
DOI
https://doi.org/10.2991/jrnal.2015.2.2.1
Keywords
adaptive control, multiple-model design, nonlinear systems, asymptotic tracking
Abstract
In this paper, an adaptive multiple-model controller is developed for nonlinear systems in parametric-strict-feedback form. Unlike the previous results, a switching scheme is not required here to switch the most appropriate model into the controller design. The new scheme reduces the number of identification models and uses information provided by all the models more efficiently than previous results by using the convex combination of estimates of parameters. The method guarantees parameter convergence and global asymptotic stability of the closed-loop system. The global boundness of closed-loop signals and asymptotic convergence to zero of tracking error are proved. A simulation example is included to demonstrate the effectiveness of the obtained results.
Copyright
© 2013, the Authors. Published by ALife Robotics Corp. Ltd.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Chao Yang, Yingmin Jia
Corresponding Author
Chao Yang
Available Online 1 September 2015.
DOI
https://doi.org/10.2991/jrnal.2015.2.2.1
Keywords
adaptive control, multiple-model design, nonlinear systems, asymptotic tracking
Abstract
In this paper, an adaptive multiple-model controller is developed for nonlinear systems in parametric-strict-feedback form. Unlike the previous results, a switching scheme is not required here to switch the most appropriate model into the controller design. The new scheme reduces the number of identification models and uses information provided by all the models more efficiently than previous results by using the convex combination of estimates of parameters. The method guarantees parameter convergence and global asymptotic stability of the closed-loop system. The global boundness of closed-loop signals and asymptotic convergence to zero of tracking error are proved. A simulation example is included to demonstrate the effectiveness of the obtained results.
Copyright
© 2013, the Authors. Published by ALife Robotics Corp. Ltd.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).