Neuro-Fuzzy-based Improved IMC for Speed Control of Nonlinear Heavy Duty Vehicles

A neuro-fuzzy based improved internal model control (I-IMC) is proposed for speed control of uncertain nonlinear heavy duty vehicle (HDV) as the standard IMC (S-IMC) can’t tackle the nonlinear systems effectively and degrades the performance of HDV system. Adaptive neuro-fuzzy inference system and artificial neural network with adaptive control are used for the design of I-IMC. The proposed control techniques are developed to achieve the better speed tracking performance and robustness of HDV system under the influence of road grade disturbance

Steady state and dynamic response of a state space observer based PMSM drive with different controllers

This paper deals with an investigation and evaluation of the performance of a state observer based Permanent Magnet Synchronous Motor (PMSM) drive controlled by PI (Proportional Integral), PID (Proportional Integral and Derivative), SMC (sliding mode control), ANN (Artificial neural network) and FLC (Fuzzy logic) speed controllers. A detailed study of the steady state and dynamic performance of estimated speed and angle is given to demonstrate the capability of the controllers

PV Output forecasting based on weather classification, SVM and ANN

211-217The expansion in solar power is expected to be dramatic soon. A number of solar parks with high capacities are being setup to harness the potential of this renewable resource. However, the variability of solar power remains an important issue for grid integration of solar PV power plants. Changing weather conditions have affected the PV output. Thus, developing methods for accurately forecasting solar PV output is essential for enabling large-scale PV deployment. This paper has proposed a model for forecasting PV output based on weather classification, using a solar PV plant in Maharashtra, India, as the sample system. The input data is first classified using RBF-SVM (Radial Basis Function Support Vector Machines) into three types based on weather conditions, namely, sunny, rainy and cloudy. Then, the neural network model corresponding to that weather type has been applied to forecast the solar PV output. The obtained results for the overall model is studied for its effectiveness and are compared with existing research

Speed control of wheeled mobile robot by nature-inspired social spider algorithm-based PID controller

: Mobile robot is an automatic vehicle with wheels that can be moved automatically from one place to another. A motor is built on its wheels for mobility purposes, which is controlled using a controller. DC motor speed is controlled by the proportional integral derivative (PID) controller. Kinematic modeling is used in our work to understand the mechanical behavior of robots for designing the appropriate mobile robots. Right and left wheel velocity and direction are calculated by using the kinematic modeling, and the kinematic modeling is given to the PID controller to gain the output. Motor speed is controlled by the PID low-level controller for the robot mobility; the speed controlling is done using the constant values Kd, Kp, and Ki which depend on the past, future, and present errors. For better control performance, the integral gain, differential gain, and proportional gain are adjusted by the PID controller. Robot speed may vary by changing the direction of the vehicle, so to avoid this the Social Spider Optimization (SSO) algorithm is used in PID controllers. PID controller parameter tuning is hard by using separate algorithms, so the parameters are tuned by the SSO algorithm which is a novel nature-inspired algorithm. The main goal of this paper is to demonstrate the effectiveness of the proposed approach in achieving precise speed control of the robot, particularly in the presence of disturbances and uncertainties

MOLECULAR DETECTION OF HUMAN RHINOVIRUS IN RESPIRATORY SAMPLES OF SWINE FLU NEGATIVE NORTH INDIAN CHILDREN WITH FLU-LIKE ILLNESS

Objectives: Flu-like illness may also be caused by different respiratory viruses other than influenza. Human rhinovirus (HRV) shows almost flu-likesymptoms. The purpose of this study is the molecular detection of HRV in throat swab of swine flu negative North Indian children during the years2012 and 2013. Reverse transcriptase (RT) - polymerase chain reaction (PCR) amplification of 5'non-coding region (NCR) was used for HRV detectionfollowed by cell culture isolation of HRV.Methods: PCR confirmed swine flu negative throat swab samples were collected from the Department of Microbiology, Sanjay Gandhi Post GraduateInstitute of Medical Sciences, Lucknow, Uttar Pradesh, India. The RNA isolation of samples was done using the QIAampViral RNA Mini Kit (Qiagen),followed by single step RT-PCR amplification (AgPath-ID, Life Technologies). All PCR positive HRV samples were cell cultured in HeLa and HEp-2 celllines for viral isolation.®Results: 135 swine flu negative throat swab samples were examined. Out of which 34 samples (25.2%) were found HRV positive by RT-PCR, while onlyfour samples (11.8%) were culture positive on HeLa cell line. Younger children (0-4 year) were found more susceptible to HRV infection. This studyindicated the highest prevalence of HRV (37.0%) during the months (September-October) of the Autumn season in 2012 and 57% in Winter-springseason (February-March) during 2013.Conclusion: HRV may be a cause of flu-like symptoms in swine flu suspected North Indian children with a higher rate during Autumn and Springseason. Molecular detection of HRV using RT-PCR is more sensitive than cell culture assay.Keywords: Human rhinovirus, Swine flu, Influenza-like illness, Lower respiratory tract infections

In silico identification of IgE-binding epitopes of osmotin protein.

The identification of B-cell epitopes is an important step to study the antigen- antibody interactions for diagnosis and therapy. The present study aimed to identify B- cell epitopes of osmotin using bioinformatic tools and further modify these regions to study the allergenic property. B-cell epitopes were predicted based on amino acid physicochemical properties. Three single point mutations M1, M2, and M3 and a multiple point mutant (M123) were selected to disrupt the IgE binding. These mutants were cloned, expressed and proteins purified to homogeneity. The IgE binding of the purified proteins was evaluated by ELISA and ELISA inhibition with patients' sera. Three regions of osmotin M1 (57-70 aa), M2 (72-85 aa) and M3 (147-165 aa) were identified as potential antibody recognition sites using in silico tools. The sequence similarity search of the predicted epitopes of osmotin using Structural Database of Allergenic proteins (SDAP) showed similarity with known allergens from tomato, kiwifruit, bell pepper, apple, mountain cedar and cypress. Mutants M1, M2 and M3 showed up to 72%, 60% and 76% reduction, respectively in IgE binding whereas M123 showed up to 90% reduction with patients' sera. The immunoblot of M123 mutant showed 40% reduction in spot density as compared to osmotin. All mutants showed decreased inhibition potency with M123 exhibiting lowest potency of 32% with osmotin positive pooled patients' sera. The three B- cell epitopes of osmotin predicted by in silico method correlated with the experimental approach. The mutant M123 showed a reduction of 90% in IgE binding. The present method may be employed for prediction of B- cell epitopes of allergenic proteins

Feedforward fuzzy logic control for permanent magnet synchronous motor drive

In this paper, a fuzzy logic speed controller in combination with feedforward fuzzy logic current controller is designed and modelled to control a permanent magnet synchronous motor (PMSM) drive for high performance drive applications. The fundamentals of fuzzy logic based control algorithms are illustrated in brief. The detailed performance of PMSM drive is studied applying various mechanical disturbances such as starting, speed reversal, flux weakening and load perturbations

Robust Stability Analysis of PMSM with Parametric Uncertainty using Kharitonov Theorem

The permanent magnet synchronous motors (PMSM) are used as servo motor for precise motion control and are used as generator to generate electrical energy driven by wind energy. There is large variation in inertia due to varying load and parametric uncertainty in PMSM. The design objective of this paper is to analytically determine the relative robust stability of PMSM with parametric uncertainty using Kharitonov theorem and Routh stability criterion. The conventional integral controller (IC) and two robust internal model controllers (IMCs) are used for relative robust stability analysis of speed control of PMSM. The frequency domain performance specifications like gain margin (GM) and phase margin (PM) are taken for relative robust stability analysis, and the effect of controllers on time domain performance specifications such as settling time (ST), rise time (RT) and overshoot (OS) is also studied

Observer based position and speed estimation of interior permanent magnet motor

This paper presents position sensorless interior permanent magnet synchronous motor drive using a discretized extended kalman filter algorithm (EKF). An observer based speed estimator which can be used for the state estimation of a non linear dynamic system in real time is presented here. Speed and position estimation of IPM is simulated using MATLAB and results of step variation in speed, load perturbation and flux weakening are presented to substantiate the proposed estimation of the speed