Static output feedback—a survey
Automatica (Journal of IFAC)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Evolutionary Computation: Toward a New Philosophy of Machine Intelligence (IEEE Press Series on Computational Intelligence)
Design of mixed H2/H∞ control systems using algorithms inspired by the immune system
Information Sciences: an International Journal
A tabu search approach for the minimum sum-of-squares clustering problem
Information Sciences: an International Journal
A study of particle swarm optimization particle trajectories
Information Sciences: an International Journal
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Technical Communique: Static Output Feedback Stabilization: An ILMI Approach
Automatica (Journal of IFAC)
Effective vaccination policies
Information Sciences: an International Journal
Evolutionary algorithms for optimization problems with uncertainties and hybrid indices
Information Sciences: an International Journal
Original article: Design of robust electric power system stabilizers using Kharitonov's theorem
Mathematics and Computers in Simulation
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This paper presents a simple but effective tuning strategy for robust static output feedback (SOF) controllers with minimal quadratic cost in the context of multiple parametric uncertainties. Finding this type of controller is known to be computationally intractable using conventional techniques. This is mainly due to the non-convexity of the resulting control problem, which has a fixed structure. To solve this kind of control problem easily and directly, without using any complicated mathematical manipulations, we utilize Kharitonov's theorem and an evolutionary algorithm (EA) for the resolution of the underlying constrained optimization problem. Using Kharitonov's theorem, a family of bounded, robustly stable static output feedback controllers can be defined and EA is used to select the controller that ensures a minimal quadratic cost within this family. The resulting tuning strategy is applicable to both stable and unstable systems, without any limitations on the order of the process to be controlled. A numerical study was conducted to demonstrate the validity of the proposed tuning procedure.