.png){kind=icon}
.png){kind=icon}
Return
Ramp-Tracking Generalized Predictive Control System-Based on Second-Order Difference
.png){kind=icon}
PRE
AI
DOI：10.1109/TCSII.2020.3019028.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This brief proposes an adaptation of the Generalized Predictive Control (GPC) for ramp-reference tracking. The second-order difference operation and the plant model are used to get an augmented model with two embedded integrators and whose output is the tracking error. Differently from other GPC-based tracking algorithms, the proposed approach does not require information about the reference parameters, and the GPC prediction horizon is composed of the predicted errors instead of the expected plant outputs. Thus, the optimization function and the receding horizon strategy used in conventional GPC can be applied to get the control law. Simulation and experimental results prove that the proposed approach can successfully track constant and ramp references. The proposed method is applicable for single-input single-outputs plants. However, the mathematical background presented in this brief can be used in the development of new GPC strategies.
Keywords:
Mathematical model
Predictive models
Predictive control
Cost function
Circuits and systems
Prediction algorithms
Generalized predictive control
model predictive control
ramp reference
second-order difference
tracking
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
I
IF:
4.9
Papers:
8.8K
Citations:
2.5W
