Sharma, S.K. Sharma and McLoone, S.F. and Irwin, G.W.
Genetic algorithms for local controller network
IEE Proceedings on Control Theory and Applications, 152 (5).
Local Controller Networks (LCNs) provide nonlinear control by interpolating between a
set of locally valid, subcontrollers covering the operating range of the plant. Constructing such
networks typically requires knowledge of valid local models. This paper describes a new genetic
learning approach to the construction of LCNs directly from the dynamic equations of the plant, or
from modelling data. The advantage is that a priori knowledge about valid local models is not
needed. In addition to allowing simultaneous optimisation of both the controller and validation
function parameters, the approach aids transparency by ensuring that each local controller acts
independently of the rest at its operating point. It thus is valuable for simultaneous design of the
LCNs and identification of the operating regimes of an unknown plant. Application results from a
highly nonlinear pH neutralisation process and its associated neural network representation are
utilised to illustrate these issues.
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