Optimal Fuzzy Reasoning Research Articles

Filtered X least mean fourth‐driven intelligent control for power quality augmentation and dynamic stability reinforcement of grid intertie wind–photovoltaic systems

SummaryAn advanced topology comprising a wind‐turbine driven synchronous generator (SG) and solar photovoltaic (PV) array to address the power quality (PQ) concerns and the uncertainty in renewable energy generation is proposed in this paper. The fluctuations in wind speed and insolation are soaked up by utilizing successively interfaced power electronic converters (PECs), namely, SG side converter (SGC) and utility grid side converter (UGC) integrated with a DC link capacitor and a PV array. SGC is controlled by estimating the rotor position from the stator voltage and current measurement data and maintains unity power factor (UPF) at stator terminals. UGC is controlled by employing filtered X least mean fourth (FXLMF) control, which ensures superior convergence performance and reduced computational complexity. Besides, it performs operations like reactive power compensation, enhancement of PQ, load and power balancing during steady‐state and dynamic conditions. The notion of optimal fuzzy reasoning is instilled which generates a fuzzy scheme with inherent optimal‐tuning characteristics to bring about considerable improvement in system performance. A water cycle algorithm (WCA) tuned fuzzy logic controller (FLC) is employed for dynamic stability enhancement leading to the generation of accurate loss component, , which bolsters the ability of VSC to accurately extricate weight signals and the fundamental load current component. This improves regulation of DC link voltage during dynamic load, insolation and wind conditions. The veracity of the proposed model is verified under wind gusts and the bell‐shaped irradiance curve. Extensive simulation results obtained in MATLAB corroborate the efficacy of the system.

Some notes on optimal fuzzy reasoning methods

The optimal fuzzy reasoning (OFR) method proposed by Zhang and Cai (2004) is treated as a control problem and it is dramatically different from other existing fuzzy reasoning methods. The aim of this paper is to make some notes on OFR methods. We give a modification of an OFR method presented by Zhang and Cai. Some advantages and limitations of OFR methods are examined. We introduce several new OFR methods based on the Łukasiewicz implication, the Kleene-Dienes implication and the Zadeh implication. The reasoning consistency of the proposed methods is studied. The relationship between the OFR method and other fuzzy reasoning methods (e.g., the compositional rule of inference method and the pointwise optimizing fuzzy inference method) is examined.

A feedback based CRI approach to fuzzy reasoning

Fuzzy reasoning methods are extensively used in intelligent systems and fuzzy control. In our previous work, an explicit feedback mechanism is embedded into optimal fuzzy reasoning methods, and the resulting fuzzy reasoning methods are more robust than the optimal fuzzy reasoning methods. An interesting question is that, without the introduction of optimization goals, can the robustness of fuzzy reasoning methods be improved by embedding feedback mechanisms? This paper is intended to answer the question. By embedding feedback mechanisms into CRI approach, a new feedback based CRI (FBCRI) approach is proposed and three implementation methods with different strategies are obtained. Simulation results show that the feedback mechanisms are really useful for the improvement of the robustness of CRI methods. At last, to test the applicability of the proposed approach, it is applied to the solution of a real-time path planning problem for UAVs. The effectiveness and efficiency of an FBCRI based real-time path planning algorithm are verified by representative test examples, which show that embedding feedback information into the fuzzy reasoning process actually improve the quality of flight paths.

An Improved Robust Fuzzy-PID Controller With Optimal Fuzzy Reasoning

Many fuzzy control schemes used in industrial practice today are based on some simplified fuzzy reasoning methods, which are simple but at the expense of losing robustness, missing fuzzy characteristics, and having inconsistent inference. The concept of optimal fuzzy reasoning is introduced in this paper to overcome these shortcomings. The main advantage is that an integration of the optimal fuzzy reasoning with a PID control structure will generate a new type of fuzzy-PID control schemes with inherent optimal-tuning features for both local optimal performance and global tracking robustness. This new fuzzy-PID controller is then analyzed quantitatively and compared with other existing fuzzy-PID control methods. Both analytical and numerical studies clearly show the improved robustness of the new fuzzy-PID controller.

Optimal fuzzy reasoning and its robustness analysis

Fuzzy reasoning methods are extensively used in intelligent systems and fuzzy control. Most existing fuzzy reasoning methods follow rules of logical inference. In this article, fuzzy reasoning is treated as an optimization problem. The idea of optimal fuzzy reasoning is reviewed and three new optimal fuzzy reasoning methods are given by using new optimization objective functions. The robustness of fuzzy reasoning, that is, how errors in premises affect conclusions in fuzzy reasoning, is evaluated in a probabilistic or statistical context by using the Monte Carlo simulation method. Six optimal fuzzy reasoning methods are evaluated in comparison with the CRI method in terms of probabilistic robustness. © 2004 Wiley Periodicals, Inc. Int J Int Syst 19: 1033–1049, 2004.