Abstract
A discrete time adaptive control methodology using multiple models representing a real system in different operating conditions is presented. In the proposed method, the real plant is identified simultaneously by several models with different structure and an adoptive control is carried out using a controller designed by a certain model selected by some rules. The selection rules of the suitable model for an operating condition are decided by theoretical and experiential knowledge about the parameter estimation and adaptive control. This method requires few a priori information about the controlled plant. Moreover, this method can be applied for stable or unstable, and minimum phase or non-minimum phase plants with variable characteristics depending the operating conditions. From the practical point of view, this control algorithm is characterized by a parallel structure, and it can easily implemented by multi microprocessor systems.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
