Fuzzy logic controller with neurol network signal predictors for complex split-range control of a hybrid actuator
Abstract
This paper presents an integrated fuzzy logic controller (FLC) to address challenges associated with complex
split-range control of a two-valves-in-parallel hybrid actuator. Split-range hybrid valve (HV) control
aims to provide a more effective strategy to overcome flow problems associated with nonlinear valve
flow coefficients and discontinuities while extending rangeability. The main issue is the trade-off between
system performance, control scheme complexity, and the cost of extra equipment (multiple actuators).
The hybrid configuration facilitates a large and small valve in an overlapping operating regime, providing
the controller with some freedom to minimize the feedback error. The control challenge lies at the
control valve boundaries where nonlinear transitions in mass flow rate occur. The aim of the complex
split-range control is therefore to minimize the disturbance in the mass flow rate due to the mentioned
valve non-idealities during critical valve transitions. A novel split-range control scheme is devised that
comprises an integrated FLC, upper and lower boundary neural network (NN) signal predictors, a fuzzy
logic inference system (FLIS), and a crisp controller to quantify all the control decisions that cannot be
fuzzified. The NN predictors envisage the necessity for a large valve nonlinear transition in order to minimize
its effect on the flow rate of the HV. Expert knowledge is used as basis for parameter definitions in
the FLIS, thereby facilitating the implementation of control structures to address phenomena such as nonlinear
mass flow transitions, valve stiction, and poor valve resolution. Simulation results indicate that the
integrated FLC effectively coordinates and switches actuators for complex input requests, significantly
reducing nonlinear transitions in the total mass flow rate compared to PID control
URI
http://hdl.handle.net/10394/16213https://doi.org/10.1016/j.sna.2013.05.017
https://www.sciencedirect.com/science/article/pii/S092442471300263X
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- Faculty of Engineering [1122]
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