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This paper originally proposes an optimal control system which consists of both feedforward and statefeedback controllers using a generalized linear quadratic Gaussian and loop transfer recovery (GLQG/LTR) method.
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For hybrid ANC schemes, the NMP path causes design difficulties to both feedforward and feedback control.
A fast learning neuroevolutionary algorithm for both feedforward and recurrent networks is proposed.
Good dynamic model estimation plays an important role for both feedforward and feedback control, fault detection, and system optimization.
A spatial spillover function is defined, which is valid for both feedforward and feedback control with scalar and vector control inputs.
In this paper, we propose a new state feedback controller using dynamic gain for input-delayed systems with high-order nonlinearity terms in both feedforward and non-feedforward forms.
Main objectives and possible challenges associated with the use of the developed model for the design of both feedforward and feedback controllers are also discussed.
A feedback linearization technique has been developed and implemented to control this nonlinear plant by merging both feedforward and feedback control.
As mentioned in the Introduction, visually guided reaching is controlled by both feedforward and feedback motor commands.
The E-DE, ED, MT, PV, and Tpv, defined in the Data analysis section, are related to both feedforward and feedback control.
Previous studies of visually guided reaching, on the other hand, have suggested that the reaching movement is controlled by both feedforward and feedback motor commands [9] [14].
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