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Model predictive control using fuzzy decision functions.

J M da Costa Sousa¹, U Kaymak

¹Dept. of Mech. Eng., Lisbon Univ.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|February 5, 2008

Summary

This summary is machine-generated.

Fuzzy predictive control enhances model predictive control by integrating fuzzy decision making. This approach improves control performance by transparently incorporating fuzzy goals and constraints.

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Area of Science:

Control Engineering
Artificial Intelligence
Fuzzy Systems

Background:

Conventional model predictive control (MPC) faces challenges in transparently handling complex, multi-objective control problems.
Integrating fuzzy logic offers a framework to manage uncertainty and imprecision in control system goals and constraints.

Purpose of the Study:

To investigate the application and benefits of fuzzy decision making (FDM) within model predictive control (MPC).
To compare the performance of fuzzy MPC against conventional MPC.
To analyze the impact of different aggregation operators in FDM for control applications.

Main Methods:

Fuzzy predictive control formulation combining MPC with fuzzy multicriteria decision making.
Utilizing fuzzy set theory and decision functions to integrate fuzzy goals and constraints.
Experimental validation on a non-minimum phase unstable linear system and a nonlinear air-conditioning system.

Main Results:

Fuzzy MPC demonstrated improved performance compared to conventional MPC.
The transparent integration of fuzzy criteria via FDM enhanced control strategy.
The choice of aggregation operators significantly influences FDM effectiveness in control.

Conclusions:

Fuzzy decision making provides a robust framework for enhancing model predictive control.
The integration of fuzzy logic offers a transparent and effective method for handling complex control objectives.
Fuzzy MPC is a promising approach for improving the performance of control systems with uncertain or imprecise requirements.