Model Free Adaptive Predictive Control for Main Stream Pressure System of Power Plant

The conventional PID control has been proven insufficient and incapable for this particular petro-chemical process. This paper proposes a nonlinear adaptive predictive functional control (NAPFC) algorithm based on the Takagi–Sugeno (T-S) model for average cracking outlet temperature (ACOT) of the ethylene cracking furnace. In this algorithm, in order to overcome the effect on system performance under model mismatch, the structure parameters of the T-S fuzzy model are confirmed, and the model consequent parameters are identified online using the forgetting factor least-square method. Prediction output is calculated according to the identified parameters instead of computing the Diophantine equation, thereby obtaining directly the predictive control law and avoiding the complex computation of the inverse of the matrix. Application results on ACOT of the ethylene cracking furnace show the proposed control strategy has strong tracking ability and robustness.

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Model Free Adaptive Predictive Control for Main Stream Pressure System of Power Plant

Cited by 8 publications

References 2 publications

Modified Model-Free Adaptive Control using Compact-Form Dynamic Linearization Technique

Modified Model-Free Adaptive Control using Compact-Form Dynamic Linearization Technique

Control of small pressurized water reactors under single-loop operation based on MFAPC theory

Nonlinear Adaptive Predictive Functional Control Based on the Takagi–Sugeno Model for Average Cracking Outlet Temperature of the Ethylene Cracking Furnace

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