Society is experiencing massive growth of global industrialised populations, which is puttingincreasing pressure on western governments to pursue more persuasive means to maintain and increasetheir share of the world’s diminishing fossil fuel reserves. To combat this, there is a growing body ofenlightened researchers who are directing their abilities towards the development of alternative andpreferably renewable energy types of supply systems. Many of these real world systems exhibit varyingdegrees of non-linearity. An example of this is the significant variations in the dynamic characteristics ofa distributed collector field within a solar thermal power plant. Here a Sugeno-type fuzzy incrementalcontroller was tuned using an ANFIS (Adaptive Neural Fuzzy Inference System) to optimise the fuzzycontroller’s pre-clustered input membership functions, while a multiobjective genetic algorithm with anenhanced decision support system was used to fine tune the parameters of its first order outputmembership functions. The resulting solution choice produced an incremental fuzzy controller which wasused to successfully control the plant exclusively in its high nonlinear regions, i.e., where the oil flow fellbelow 5 litres per second. This allowed the plant to function in environments where local solar radiationconditions have always been regarded as marginal. A feedforward term was also used to control plantdisturbances caused by solar irradiation, mirror reflectivity etc.
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