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The developed ESDRE control proves superior compared to the PID reference controller with auxiliary gravity compensator.
The controller is demonstrated to perform well in these, outperforming a reference controller which has previously been shown to give satisfactory control performance.
The proposed algorithm consists of an indicated mean effective pressure (IMEP) estimation model and model reference controller.
The concept of multimode switching is applied to activate either a 'bang bang' controller or an adaptive neuro-fuzzy model reference controller (ANFMRC).
Control architectures and methodologies selected for this study include a robust baseline augmented by a direct adaptive model reference controller with various recently developed modifications.
The feedback controller was optimized to ensure minimal course fluctuation and rudder activity under stochastic wave disturbances, while the reference controller provides a quick transient response when the desired course is changed.
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The two inner control levels are model nonlinear reference controllers (MNRC) while the last level of control relies on a modified Smith Predictor.
We prove that standard model reference controllers (state and output-feedback) for linear time invariant systems with a suitably chosen filter at the plant input admit a parameter independent realization.
In this work, a data-driven algorithm is proposed to directly design a gain-scheduled model-reference controller without need of identifying and transforming a model of the process.
Two adaptive control strategies were tested in the pilot bioreactor: a model reference adaptive controller with a linear reference model and an integral feedback controller with adaptive gain.
In this paper, a serial input neuron-based model reference adaptive controller is proposed for the control of a class of uncertain non-autonomous systems.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com