Software Enabled Control

Chapter 12.2 - Real-Time Adaptation Of Mode Transition Controllers

12.2.   REAL-TIME ADAPTATION OF MODE TRANSITION
CONTROLLERS


We propose a scheme for the real-time adaptation of mode transition
controllers designed via the method of blending local mode controllers
(BLMC) [18-20]. The control objective is to adapt the blending gains portion
of the mode transition controllers such that the nonlinear plant state vector
tracks the desired transition trajectory from a start mode of operation to a
goal mode. The adaptation scheme is composed of a desired transition
model, an active plant model and an active controller model which is the
mode transition controller. The desired transition model, the active plant
model and the blending gains portion of the active controller model are
represented via a fuzzy neural network construct discussed in reference [12].
All three fuzzy neural models are trained offline, while the latter two models
are adapted online. The active plant model that incorporates local model
information is initially trained offline to capture the desired transition
trajectory and controls. Afterwards, the active plant model is online adapted
via structure and parameter learning to capture the input - output behavior of
the nonlinear system to be controlled. Likewise, the blending gains portion of
the mode transition controller is determined offline and is adapted online via
structure and parameter learning to track the desired transition trajectory.
The new blending gains to be developed by the mode transition controller
are determined from the control sensitivity matrix and the one-step-ahead
predictive output of the active plant model. The parameter learning for the
active plant and active controller models is based upon Kaczmarz’s algorithm
[21].

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