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This paper presents the optimization algorithm based on convex linearization and a dual approach (OPTI module).
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The proposed method is based on convex relaxation technique.
The proofs are essentially based on convex and functional analysis.
Real-time control is also achievable with model predictive controller based on convex formulation.
The proposed switching control design is entirely based on convex combinations of subsystems transfer functions.
We propose to give stochastic bounds (both upper and lower bounds) based on convex order.
Methods based on convex analysis make it possible to investigate the convex vector space.
We propose less-complex and efficient suboptimal solution based on formulating exact linearization, linear approximation, and convexification techniques for the non-linear and/or non-convex objective functions and constraints.
The control strategy is based on feedback linearization and adjustment of two linear controllers.
Based on the linearization scheme presented previously, a linear state-space method can be applied to model gene networks.
A recently developed non-linear control design based on feedback linearization is extended with integral action.
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