Fuzzy Tracking Algorithm Research Articles

Using PV Fuzzy Tracking Algorithm to Charge Electric Vehicles

Due to the possible shortage of oil and gas, increasing the number of cars, global warming, air pollution, and outages, there is a special need for renewable energy sources and electric vehicles (EVs). The new battery-electric vehicles BEVs can be charged by the power grid. However, the existing fossil fuel power plant cannot provide enough power for this purpose, and the only choice is renewable energy sources (RECs). Comparing RECs, solar energy is abundant and accessible in any part of the world. Needless to state that a maximum power point tracking (MPPT) system is required in order to extract maximum power from solar modules. In this paper, a charging strategy is proposed via using a solar system, a boost converter, and a fuzzy tracking algorithm. The main research contribution of the presented paper is to charge an EV without putting stress on the power grid. The effectiveness of this approach is demonstrated by the MATLAB Simulink and LTSPICE results.

Fuzzy Tracking and Control Algorithm for an SSVEP-Based BCI System

Subjects with amyotrophic lateral sclerosis (ALS) consistently experience decreasing quality of life because of this distinctive disease. Thus, a practical brain-computer interface (BCI) application can effectively help subjects with ALS to participate in communication or entertainment. In this study, a fuzzy tracking and control algorithm is proposed for developing a BCI remote control system. To represent the characteristics of the measured electroencephalography (EEG) signals after visual stimulation, a fast Fourier transform is applied to extract the EEG features. A self-developed fuzzy tracking algorithm quickly traces the changes of EEG signals. The accuracy and stability of a BCI system can be greatly improved by using a fuzzy control algorithm. Fifteen subjects were asked to attend a performance test of this BCI system. The canonical correlation analysis (CCA) was adopted to compare the proposed approach, and the average recognition rates are 96.97% and 94.49% for proposed approach and CCA, respectively. The experimental results showed that the proposed approach is preferable to CCA. Overall, the proposed fuzzy tracking and control algorithm applied in the BCI system can profoundly help subjects with ALS to control air swimmer drone vehicles for entertainment purposes.

Fuzzy train tracking algorithm for the energy efficient operation of CBTC equipped metro lines

In CBTC (Communications-Based Train Control) operated metro lines, headway can be reduced by taking advantage of the system's train–track continuous communication, thus increasing transport capacity. When a train runs close enough to the preceding one, a tracking algorithm is triggered to control the distance between the trains. The following train receives the LMA (limit of movement authority) via radio, which is updated periodically as the preceding train runs.Besides transport capacity, one of railway operators’ main goals is the reduction of energy consumption, for environmental and economic reasons. This paper firstly shows that the existing basic CBTC tracking algorithm is not energy efficient, due to the application of short braking–traction cycles. It then proposes a new efficient algorithm that makes use of coast (null traction) command, and models the uncertainty associated with the speed of the preceding train. This fuzzy algorithm is compared to the basic one in terms of energy consumption, running time and steady state interval (resulting tracking interval).Simulation results show that the proposed fuzzy tracking algorithm provides important energy savings with minor influence on running time and steady state interval. This algorithm is suitable for implementation in on-board CBTC equipment to reduce the energy consumption of traffic operation.