Quadratic Distance Approach to Robust Multi-Objective Control
Quadratic Distance To Robust Multi-Objective Control
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Most practical systems and control problems are pure multi-objective problems. Multi-objective or vector-objective optimization problem is characterized by the partial ordering of its solution space. This, unlike in single objective optimization problem, leads to the notion of non-inferiority and the Pareto-optimal solution set. As it has been observed that the vector-optimization problem translates to a scalar optimization problem if a functional that completely orders the solution space can be found. A very important question in the transformation of the vector optimization problem into a sc...
Most practical systems and control problems are pure multi-objective problems. Multi-objective or vector-objective optimization problem is characterized by the partial ordering of its solution space. This, unlike in single objective optimization problem, leads to the notion of non-inferiority and the Pareto-optimal solution set. As it has been observed that the vector-optimization problem translates to a scalar optimization problem if a functional that completely orders the solution space can be found. A very important question in the transformation of the vector optimization problem into a scalar optimization problem-form that needs to be answered is that of the equivalence of the scalar problem and the original vector problem. The book proposed a scalarization function which is a sum of squares of the objective functionals. This reduces the vector optimization problem to a quadratic distance problem or the intersection ellipsoid of minimum volume with the trade-off surface. This method has been applied to pure and robust multi-objective Linear Quadratic Regulator (LQR) problem, and to mixed-norm multi-objective problem.
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