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This paper extends earlier work on nonlinear predictive control based on a representation of the nonlinear plant as quasi-LPV model. Since the scheduling parameters depend on state variables and inputs, they can be predicted. An efficient predictive scheme with guaranteed stability is proposed that involves solving a sequence of SOCP problems at each sampling period. Compared with previously reported...
A common challenge associated with designing a Linear Parameter-Varying (LPV) controller for a nonlinear plant is identification of a plant model in state-space form with static parameter dependency. Till date, the two most frequently used methods to solve this problem are special canonical forms of Input-Output (IO) and subspace (SS) identification. This paper compares both identification methods...
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