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Then, sufficient conditions for consensus and consensualization are presented, which include only four linear matrix inequality constraints.
The design problem of the robust controller is formulated as a minimization problem under the linear matrix inequality constraints.
Controller parameters are determined by the solution of a convex optimization problem subject to linear matrix inequality constraints.
The control design problem is transformed into a convex optimization problem with linear matrix inequality constraints, which can be solved efficiently with standard solvers.
Each iteration of the algorithm contains linear matrix inequality constraints only and can be effectively solved by the existing numerical software packages.
The controller parameters can be obtained through the numerical resolution of an optimization problem with linear matrix inequality constraints derived from Lyapunov's stability theory and the choice of an original Lyapunov function.
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This reformulation requires the inclusion of a matrix inequality constraint involving positive semidefiniteness.
The output feedback robust controller design may be reduced to the problem of finding a feasible point under Biaffine Matrix Inequality constraint.
where,, and the quadratic inequality constraint is converted to a linear matrix inequality constraint (LMI) using Schur-Complement theorem [16].
The controller design problem is presented as an optimization under linear matrix inequalities constraints.
The Proportional Integral observer gains are computed by solving the proposed conditions under Linear Matrix Inequalities constraints.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com