PROJECT TITLE :

Controller dynamic linearisation-based model-free adaptive control framework for a class of non-linear system

ABSTRACT:

Without the explicit method identification, the authors propose a model-free adaptive management framework for unknown plant by using the concept of equivalent dynamic linearisation controller. The controller has linear incremental structure and its native dynamics is such as the best controller in theory. Hence, the problem of determining the structure of candidate controller is reworked to the matter of finding a sequence of local dynamic controllers to approximate the perfect controller. With the assistance of gradient info extracted from input and output (I/O) knowledge of the plant, the optimal controller parameter sequence is generated by minimising a user-outlined control criterion. This methodology offers a answer on how to see the candidate controller structure. The controller style, parameter tuning and controller validation are based mostly on I/O information of the plant. Hence, it may scale back the influence of internal disturbance or unmodelled dynamics. The effectiveness of the proposed technique is illustrated by the simulation of never-ending polymerisation reaction method in a very jacketed continuous stirred tank reactor system. Meanwhile, a simulation comparison is allotted to indicate the superiority of neural network knowledge model in model-free adaptive control framework.


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