Analysis of the hierarchical fuzzy control
using evolutionary algorithms
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The research presented examines the construction of a fuzzy logic controller for complex nonlinear system by control system decomposition into hierarchical fuzzy logic sub-systems. Evolutionary algorithm based methods are proposed to determine the control system for the hierarchical fuzzy system (HFS). Different HFS topologies for a given dynamical system (such as the inverted pendulum system) are investigated. For the inverted pendulum system, a single layer, two layered, three layered, and four layered HFS, with different variable input configuration is investigated. Effects of different inp...
The research presented examines the construction of a fuzzy logic controller for complex nonlinear system by control system decomposition into hierarchical fuzzy logic sub-systems. Evolutionary algorithm based methods are proposed to determine the control system for the hierarchical fuzzy system (HFS). Different HFS topologies for a given dynamical system (such as the inverted pendulum system) are investigated. For the inverted pendulum system, a single layer, two layered, three layered, and four layered HFS, with different variable input configuration is investigated. Effects of different input configurations on controller performance are examined and discussed. A new evolutionary algorithm based compositional method is proposed to control system over the whole set of user-defined initial conditions. The method addresses directly the problem of controlling the dynamical system from specific, user-defined initial conditions. The multiobjective evolutionary algorithm (MOEA) based compositional method is developed and tested on the example of the inverted pendulum system.
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