In this paper, active power filter is modeled based on T-S fuzzy theory to reduce the computational burden of the traditional method, and the fuzzy controller is constructed according to the principle of parallel dist...
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In this paper, active power filter is modeled based on T-S fuzzy theory to reduce the computational burden of the traditional method, and the fuzzy controller is constructed according to the principle of parallel distributed compensation. In view of the fact that T-S model parameters is difficult to obtain and vulnerable to external disturbance and parameter perturbation, an adaptive fuzzy controller with parameter estimation(AFCPE) is designed to ensure parameter estimation error asymptotically converges to zero and improve the reliability of the system and the robustness to parameter variations. The parameters are derived from the Lyapunov stability analysis to guarantee tracking performance and stability of the closed-loop system. Simulation studies in the MATLAB/SimPower Systems Toolbox demonstrate that the proposed control methods offer a good behavior in both steady state and transient operation during balanced and unbalanced load.
In previous studies, several stable controller design methods for plants represented by a special Takagi-Sugeno fuzzy network (STSFN) have been proposed. In these studies, the STSFN is, however, derived directly from ...
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In previous studies, several stable controller design methods for plants represented by a special Takagi-Sugeno fuzzy network (STSFN) have been proposed. In these studies, the STSFN is, however, derived directly from the mathematical function of the controlled plant. For an unknown plant, there is a problem if STSFN cannot model the plant successfully. In order to address this problem, we have derived a learning algorithm for the construction of STSFN from input-output training data. Based upon the constructed STSFN, existing stable controller design methods can then be applied to an unknown plant. To verify this, stable fuzzy controller design by parallel distributed compensation (PDC) method is adopted. In PDC method, the precondition parts of the designed fuzzy controllers share the same fuzzy rule numbers and fuzzy sets as the STSFN. To reduce the controller rule number, the precondition part of the constructed STSFN is partitioned in a flexible way. Also, similarity measure together with merging operation between each neighboring fuzzy set are performed in each input dimension to eliminate the redundant fuzzy sets. The consequent parts in STSFN are designed by correlation measure to select only the significant input terms to participate in each rule's consequence and reduce the network parameters. Simulation results in the cart-pole balancing system have shown that with the proposed STSFN building approach, we are able to model the controlled plant with high accuracy and, in addition, can design a stable fuzzy controller with small parameter number.
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