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A prototype of an autonomous irrigation controller consisting of an 8-bit MCU and a 12-bit AD converter was developed and tested in tomato greenhouses.
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For positioning of the platform, a combined robust controller consisting of a model reference control and a H∞ control is proposed.
A switching type controller consisting of two control modes are devised: a PID-type fuzzy controller consisting of a PI-type and a PD-type fuzzy controller, and a conventional PID.
The control strategy is based on an application of the controller consisting of a linear and nonlinear part.
In particular, we propose a three-step design procedure for the overall controller having the following features: On the coder side, it uses a controller consisting of a filter bank whose outputs are subband signals of the control input.
A hybrid controller consisting of a saturation controller and a position-feedback controller is shown to be robust and efficient in controlling both transient and steady-state vibrations.
Based on such principles, we developed a neuromechanical controller consisting of a modular neural network (MNN) and of virtual agonist antagonist muscle mechanisms (VAAMs).
A formation controller, consisting of a feedback linearization part and a sliding mode compensator, is designed to stabilize the overall system including the internal dynamics.
A two-layer controller consisting of a Fuzzy upper layer and a PI lower layer is designed for the control of tractor stability.
In this study, an iterative learning contouring controller consisting of a classical Proportional-Derivative (PD) controller and disturbance observer is proposed.
The pitch controller, consisting of a proportional-integral (PI) controller and a gain-scheduler for the 3.5 kW simulator, is designed to provide similar pitch responses to those of the target 2 MW wind turbine for the entire wind speed region above the rated.
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CEO of Professional Science Editing for Scientists @ prosciediting.com