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The developed controller is developed based on the nonlinear motion loading model.
First, based on the principle of computed torque control, a position controller is developed for the motor toggle servomechanism.
Then the constrained generalized predictive controller is developed to control the charging current.
A flocking controller is developed to control the mobile sensor nodes to track the target in the second stage.
Also, a decoupled adaptive sliding mode controller is developed to control the chaotic Lorenz system for comparison.
Then, on the basis of this model, a neural controller is developed in order to control the supercapacitor voltage.
An H∞ PID controller is developed based on optimal control theory and the parameters are derived analytically.
The second controller is developed using a genetic-based fuzzy control method.
An robust inferential controller is developed for hot strip rolling mill tension control.
Next, a TS-RFCMAC controller is developed based on parallel distributed compensation and adaptive control laws.
An optimal grade transition control system integrating off-line optimizer and nonlinear model predictive controller is developed for polymerization reactors.
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