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Performance of the MPC controller to track a varying speed reference is analyzed in further details.
The rotor position was calculated from the causal dynamic output feedback compensator and from the desired speed reference.
Based on the identified model parameters and predicted speed reference, the optimal control law of FOGPC is derived.
By means of this plant description, a low order robust controller can be designed to regulate the vehicle dynamics according to the personalized speed reference profile.
When the constant speed reference is applied, the DHO-PALC can achieve a faster learning convergence speed than the first-order periodic adaptive learning compensation (FO-PALC).
Using this plant description, very simple P and PI robust controller can be used to regulate the vehicle dynamics according to a prescribed speed reference profile.
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Artificial Bee Colony algorithm (ABC) was implemented to calculate and update optimal speed references.
Finally, the performance and the effectiveness of the designed method are tested experimentally throw a significant benchmark including different speed references and with significant robustness tests.
Design details of a high-speed reference voltage buffer which ensures precise settling of the DAC output voltage in the presence of bondwire inductances are provided.
In this paper, the objective of the ABFSM is to track the distance-speed reference trajectory of the crane control system.
The controller is formally shown to meet its objectives i.e. accurate motor speed-reference tracking, tight regulation of the DC link voltage and power factor correction.
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