A fuzzy model predictive controller for stick‐slip type piezoelectric actuators

This work consists on new tuning of Model Predictive Controllers using Fuzzy Logic method. Tree relevant parameters are automatically adjusted the prediction horizon Np, the input weight R and the output weight Q. The proposed controller is implemented in an Artificial Pancreas and tested under realistic conditions in a commercial platform of simulation. The result of the simulations revealed the success of such a method to improve the controller’s performances compared to the previous ones.

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Model Predictive Controller Design for Two-wheeled Vehicle based on Graph Search and Machine Learning

IEEJ Transactions on Electronics Information and Systems ◽

10.1541/ieejeiss.133.342 ◽

2013 ◽

Vol 133 (2) ◽

pp. 342-349 ◽

Cited By ~ 1

Author(s):

Daisuke Toishi ◽

Eiji Konaka

Keyword(s):

Machine Learning ◽

Controller Design ◽

Graph Search ◽

Model Predictive Controller ◽

Wheeled Vehicle ◽

Predictive Controller

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Min-Max Model Predictive Controller for Trajectory Tracking of a Wheeled Mobile Robot with Slipping Effects

IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences ◽

10.1587/transfun.e94.a.680 ◽

2011 ◽

Vol E94-A (2) ◽

pp. 680-687 ◽

Cited By ~ 3

Author(s):

Yu GAO ◽

Kil To CHONG

Keyword(s):

Mobile Robot ◽

Trajectory Tracking ◽

Wheeled Mobile Robot ◽

Model Predictive Controller ◽

Predictive Controller

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Enlarging the region of stability in robust model predictive controller based on dual-mode control

Transactions of the Institute of Measurement and Control ◽

10.1177/01423312211012362 ◽

2021 ◽

pp. 014233122110123

Author(s):

Fatemeh Khani ◽

Mohammad Haeri

Keyword(s):

Stability Region ◽

Linear Matrix ◽

Input State ◽

Dual Mode ◽

Model Predictive Controller ◽

Mode Control ◽

Robust Model ◽

Output Constraints ◽

Predictive Controller ◽

The Stability

Industrial processes are inherently nonlinear with input, state, and output constraints. A proper control system should handle these challenging control problems over a large operating region. The robust model predictive controller (RMPC) could be an linear matrix inequality (LMI)-based method that estimates stability region of the closed-loop system as an ellipsoid. This presentation, however, restricts confident application of the controller on systems with large operating regions. In this paper, a dual-mode control strategy is employed to enlarge the stability region in first place and then, trajectory reversing method (TRM) is employed to approximate the stability region more accurately. Finally, the effectiveness of the proposed scheme is illustrated on a continuous stirred tank reactor (CSTR) process.

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