From Software Enabled Control

12.2.5. Online Adaptation of Mode Transition Controllers

In this section, an adaptation scheme is proposed for the online customization
of mode transition controllers designed offline via the method of
blending local mode controllers. The control objective is to adapt the
blending matrices such that the plant output vector tracks the output vector
of a desired transition model. In order to apply the discrete-time adaptation
scheme to the continuous-time system, it is assumed that the sample rate has
been appropriately selected. Figure 12.2 shows the configuration for indirect
adaptive mode transition control. The adaptation scheme is composed of five
components: the desired transition model, the active plant model, the plant
adaptation mechanism, the active controller model, and the controller adaptation

Figure 12.2. Configuration for indirect adaptive mode transition control.

is incorporated into the consequent part of the fuzzy neural

model. Afterwards, the active plant model is adapted online via the plant
adaptation mechanism.

Plant Adaptation Mechanism
The active plant is adapted online to account for plant variations on a
real-time basis. At time instant tk, the adaptation of the active plant model is
accomplished by performing structurerparameter learning on the basis of the
current inputroutput data {(xpq(tk),upq (...

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Topics of Interest

12.3.   HOVER TO FORWARD FLIGHT EXAMPLE 12.3.1.   Model of Helicopter’s Forward Dynamics The proposed adaptation scheme is illustrated on the following model representing the...

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...

12.8.   CONCLUSION An adaptation scheme is proposed for the online adaptation of mode transition controllers designed via the blending local mode controllers approach. When the active plant model...

Editors’ Notes This chapter describes intelligent control approaches for two related problems. The first application discussed is mode transitioning: ensuring the smooth and stable change of...

12.2.2.   Learning Algorithm Using Local Model Information The learning algorithm employs both structure learning and parameter learning to determine the premise part structure and the...

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