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This paper presents a new intelligent PID tuning scheme, whose PID tuner is constructed by the fusional structure of a cerebellar model articulation controller and a neural network.
We also propose and evaluate a novel fine-grained frequency tuning scheme where we vary the number of virtual-channels in a router dynamically.
However, this MV-Index based tuning scheme only takes into account only controller error variance unfortunately with no regard for manipulative variable activity.
Comparison with the best-known and expensive variants of DE over fourteen well-known numerical benchmarks and one real-life engineering problem reflects the superiority of proposed parameter tuning scheme in terms of accuracy, convergence speed, and robustness.
Finally, the optimal tuning scheme for the adaptive tuned vibration absorber group, which can be used to reduce the variable frequency vibrations of a powertrain system, is proposed, and corresponding numerical simulations are performed.
A comprehensive time-frequency analysis is then conducted to verify that the optimal tuning scheme for the adaptive tuned vibration absorber group can significantly reduce the variable frequency vibrations of a powertrain system.
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We compare the performance of these IFT-tuned PID controllers with the performance achieved by four classical PID tuning schemes that are widely used in industry.
The focus is then shifted to alternative tuning schemes that deviate from resonance conditions.
In this paper, this problem is tackled by designing a fuzzy controller that possesses different tuning schemes for both off-line and on-line tuning of fuzzy control elements.
To improve the control performance, the discontinuous gain of the sliding control is adjusted with fuzzy logic tuning schemes with respect to the sliding function and its rate of change.
Our simulations show that IFT always achieves a performance that is at least as good as that of the classical PID tuning schemes, and often dramatically better: faster settling time and less overshoot.
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