In this paper, a new cooperative localization (CL) method for multiple autonomous underwater vehicles (AUVs) is proposed to address the problem of measurement outliers and communication packet loss caused by the harsh underwater environment. Combining the advantages of both the maximum correntropy criterion (MCC) and the adaptive neuro-fuzzy inference system (ANFIS), the quality of collected data can be improved by MCC and the ANFIS can be better trained. The efficacy of the proposed method in the CL of AUVs is verified by lake trial. The experimental results show that ANFIS can effectively obtain the location of AUVs based on the input data when the communication packet is lost and the combination of MCC and ANFIS provides better positioning accuracy and robustness. When the probability of measurement outliers is 2%, the proposed method reduces the averaged localization error by 80%, the standard deviation by 84%, and the maximum error difference by 73% compared with the CL method based on cubature Kalman filter(CKF). Finally, the effectiveness of this method is verified by various experiments under different measurement outliers probabilities.

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Cooperative Localization in Harsh Underwater Environment Based on the MC-ANFIS

Nowadays, multilevel voltage source inverters offer several advantages compared to their conventional two-level inverters. In these inverters, by synthesizing several levels of dc voltages, the staircase output waveform is produced. The structure of this waveform will have lower total harmonic distortion which leads to an approach to a desired sinusoidal waveform. Achieving higher output voltage and lower stress on power switches are other advantages of theses inverters. But in multilevel inverters the problem of common mode voltage which had been found in conventional two level inverters can still be considered as a major issue which leads to motor bearing failures. Therefore to eliminate these voltages proposing some methods seems to be necessary. This paper proposes a generalized method to generate pulse width modulation signals in multilevel inverters that have an odd number of levels. The main idea of this method to generate these signals for an n- level inverter is based on a freely selectable modulation method of an (N+1)/2 level imaginary inverter. This method which leads to eliminate common mode voltages of the n-level inverter can be extended to higher levels. Index Terms— Common mode voltage, Phase voltage, Line voltage, N level inverter. —————————— a ——————————

A Novel Scheme to Eliminate Common Mode Voltage in Multilevel Inverters

This paper aims to propose a new strapdown fiber optic gyrocompass (strapdown FOGC) using adaptive network-based fuzzy inference system (ANFIS). The strapdown FOGC is based on the principle of strapdown inertial navigation system and utilizes electromagnetic log (EM log) and damping equalizer to bound oscillatory errors existing in attitude and heading. As the introduction of damping technique, extra errors are aroused by EM log errors. To decrease the extra errors, ANFIS is utilized to adjust the damping ratio automatically in terms of the ship maneuver conditions. The simulation and trial results indicate that, compared with the conventional gyrocompass scheme, the proposed one can reduce the attitude and heading errors and improve the system performance effectively.

Strapdown fiber optic gyrocompass using adaptive network-based fuzzy inference system

A kind of marine strapdown attitude and heading reference system (AHRS) based on the principle of strapdown inertial navigation system (INS) is discussed here. With an electromagnetic (EM) log aided, the oscillations included in the attitude and heading errors are bounded by damping network. Furthermore, in order to decrease attitude and heading errors aroused by EM log measurements, we introduce an adaptive network-based fuzzy inference system to control the damping ratio automatically in terms of the vessel maneuvers conditions. The results of test demonstrate the validity of proposed method.

The use of adaptive network-based fuzzy inference system for marine AHRS

Thesis (Master, Electrical & Computer Engineering) -- Queen's University, 2016-01-18 09:42:45.133

Adaptive vison aided integrated navigation for dynamic unknown enviroments

Kalman filter is used to integrate the data from inertial navigation systems (INS) and global position systems (GPS) for vehicle navigation to provide position, velocity and attitude. However several drawbacks of Kalman filter such as noise, modeling of the system, initial noise parameter are restricted its application and implementation. In this paper the real time implementation of GPS/INS integration using a hybrid Adaptive Neuro Fuzzy Inference System (ANFIS) is proposed. The proposed system is trained during the availability of GPS signal to map the error between the GPS and the INS. Then it will be used to predict the error of the INS position components during GPS signal blockage. The data from GPS and INS are used to build a structured knowledge base consisting of behavior of the INS in some special scenarios of vehicle motion. In the absence of the GPS information, the system will perform its task only with the data from INS and with the trained data provided by ANFIS. The system is evaluated while considering several intentionally introduced GPS outages for periods of 50 seconds, with the position accuracy mostly below 0.2m. The simulated results show the advantages of the proposed method of ANFIS techniques for INS/GPS integration.

S. N. Sabreen

Papers

Real-time INS/GPS data fusion using hybrid adaptive network based fuzzy inference