In this paper, a fuzzy disturbance observer and a high-gain disturbance observer based on a variable structure controller are applied to deal with imprecise multi-shaft with web materials linkage systems taking into account the variation of the moment of inertia. Specifically, a high-gain disturbance observer and an adaptive fuzzy algorithm are separately applied to estimate system uncertainties and external disturbances. The high-gain disturbance observer is designed with auxiliary variables to avoid the amplification of the measurement disturbance, and the fuzzy disturbance observer has the advantage that it does not depend on model information. The convergence properties of the tracking error are analytically proven using Lyapunov’s theory. The obtained numerical results demonstrate the validity and the adaptive performance of the proposed control law in case the system is exposed to uncertainties and disturbances. Important remarks on the design process and performance benchmarks of the two observers are also demonstrated.

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A genetic algorithm (GA) is formulated to optimise the rule base of a fuzzy logic controller (FLC) in a solar power plant. The rule base embodies an empirical set of 49 'if-then' rules. The influence of each rule is scaled by a weighting factor which is encoded in the gene of a chromosome. The entire chromosome encodes all of the 49 weighting factors. Evaluation of the fitness of the chromosome is based on the response time of the plant. Considerably improvement of plant performance is shown after some 80 generations of evolution of the chromosome.

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