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The design of automatic control laws is often based on the solution of optimization problems. Costs and constraints of these problems are mainly non convex, non smooth or non analytic. The classical approach is to simplify the problem so as to get a tractable and exactly solvable optimization problem. In this paper, a Particle Swarm Optimization method is used to solve complex initial problems. Examples...
This paper proposes an efficient evolutionary approach to the fixed-order and structured H∞ control design problem extended to the multiple plants case. By testing it on the classical example of a flexible plant, this evolutionary approach proves to be very efficient compared with other recent tools, especially in the case of a high number of plants; it can then be considered as an interesting alternative...
The robustness against parametric uncertainties can be studied using the structured singular value μ. In that case, a normalization of the uncertain parameters is performed, and the μ analysis provides the larger parallelepiped centered in the nominal and included in the stability domain. However, results depend on the initial normalization. In this paper, the normalization is optimized so as to get...
This paper proposes to revisit the μ-synthesis problem with a recent and efficient meta-heuristic called Quantum Particle Swarm Optimization (QPSO). This algorithm allows optimizing dynamic (or static) D-scalings without fitting them, which leads to robust performance controllers. This method has been applied successfully to a classical flexible plant control problem with a reasonable computation...
The goal of this paper is to control intake's manifold pressure of a downsized turbocharged internal combustion gasoline engine. Since intake's manifold pressure is an image of the torque that the engine develops, tracking an appropriated intake's manifold pressure reference implies tracking driver's reference torque. This paper proposes a novel control architecture based on Q-LPV Hinfin closed loop...
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