Nabanita Adhikary
@nits.ac.in
Assistant Professor, Department of Electrical Engineering
National Institute of Technology SIlchar
RESEARCH INTERESTS
Control systems, robotics, machine learning
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Nabanita Adhikary, Andrzej Bartoszewicz, and Rajeeb Dey
Wiley
AbstractIn this article, a dissipativity based discrete time sliding mode controller is proposed for bilateral tele‐operation in presence of communication delays and packet losses. The controllers for both master and slave arm are designed to follow a predefined impedance dynamics with dissipative characteristics. The slave arm controller is designed as a two layered structure. The first layer uses the tracking error between master and slave to create a target velocity profile based on the user defined impedance dynamics. The second layer uses this target velocity profile, slave motion and the contact forces to design a reaching law based discrete time sliding mode controller. A model following reaching law approach is used to design the sliding mode control which alleviates the chattering in the input by removing the impact of the accumulated effects of the past disturbance from the system. Moreover, an adaptive tuning method is used for the switching gain of the sliding mode control so that the controller can be designed even when exact information on the uncertainty bounds are unknown. Numerical simulations are presented for Phantom Omni arm to show the efficacy of the proposed method.
Prince Kumar, Kunal Kumar, Aashish Kumar Bohre, Nabanita Adhikary, and Eshet Lakew Tesfaye
Springer Science and Business Media LLC
AbstractIncreased innovation on finding new ways to generate energy from different sources to meet the growing demand of consumers has led to various challenges in controlling the power network when it faces different disruptions. To address these challenges, a new approach has been proposed in this research paper, which combines a controller with a soft computing technique called Particle Swarm Optimization (PSO). The study considers a power system with four units, where three different energy sources are utilized and distributed across two areas. Each area has two power sources, with one area having a combination of thermal and gas power plants, and the other area consisting of a nuclear power plant and a gas power plant. Transmitting power from the nuclear power plant is particularly complex due to its high sensitivity to disturbances. Therefore, an intelligent and efficient controller is needed to ensure robust control in this type of power network that includes nuclear power. The paper also conducts a thorough analysis of the harmful emissions associated with electricity generation from the different power plants considered. The goal is to reduce the carbon footprint associated with power generation. The proposed work and analysis in the paper are implemented using the MATLAB/SIMULINK environment.
Prince Kumar, Kunal Kumar, Aashish Kumar Bohre, and Nabanita Adhikary
IEEE
Increased carbon footprint on earth has stressed on the utilization of green and clean sources of energy such as wind energy to satisfy the growing needs of enormous power. In this paper a hybrid power system which includes wind energy is proposed with intelligent controlling. In the current era of innovation, a lot of devices have been developed for better quality of life and hence the loading pattern of consumer cannot be predicted completely so power system need to be prepared for all random loading and complex environment and hence, this paper introduces an intelligent controller integrated with the firefly optimization technique, aimed at improving the resilience of a system when confronted with unpredictable random loads in a hybrid power system. The study presents a specially crafted fitness function, denoted as ITMWAE, designed to assess the performance of the hybrid power system incorporating a wind power plant. The experimentation and analysis presented in this work are conducted using the MATLAB/SIMULINK tool.
Prince Kumar, Kunal Kumar, Aashish Kumar Bohre, and Nabanita Adhikary
IEEE
Increased randomness in climatic condition due to global warming has stressed researchers to prepare the technology for harsh weather condition. In this paper wind power plant under different climatic conditions is considered for testing the resiliency of proposed power network with proposed controller. An intelligent controller hybridized with firefly optimization technique has been proposed in this paper which is helpful in enhancing the robustness of system in tackling the unknown random weather on proposed hybrid power system. In this paper, a uniquely designed fitness function i.e. ITMWAE (Integral of Time multiplied Magnified Weighted Absolute Error) is proposed for evaluating the performance of proposed power system with wind power plant. Proposed work in the current paper has been processed with the help of MATLAB/SIMULINK tool.
Rajdeep Bhattacharjee, Anindita Deb, and Nabanita Adhikary
IEEE
Solar energy is a popular renewable energy source that is integrated into the electricity grid due to fossil fuel depletion and environmental concerns. Solar photovoltaic (PV) systems convert sunlight into electricity and offer economic and environmental advantages. These systems consist of solar panels, inverters, and optional battery storage. The direct current (DC) electricity generated by solar panels is converted into alternating current (AC) by inverters for use in electronic devices and appliances. Battery storage is recommended for storing excess electricity. To maximize energy generation, tracking the maximum power point (MPP) of the PV system is crucial. Various maximum power point tracking (MPPT) algorithms, including artificial neural networks (ANN), have been developed to optimize energy extraction. ANN-based MPPT methods respond quickly and accurately to rapid changes in solar irradiation. Simulations comparing ANN-based MPPT with traditional perturbation and observation (P&O) algorithms demonstrate the superior performance of ANN-based methods in tracking MPP and minimizing power oscillation. MPPT energy harvesting systems can be designed and simulated using MATLAB or Simulink, and ANN-based Levenberg-Marquardt algorithms have shown promising results. Training the model with datasets of sun irradiance, temperature, and voltages can lead to accurate results with minimal error. The robustness of the algorithm has been proved by the effectiveness of overall data processing for MPPT energy optimization through the utilization of the Levenberg-Marquardt technique, trained on a dataset consisting of 1000 instances. The ANN-based Levenberg-Marquardt technique offers swift and precise response to solar irradiation changes while minimizing power oscillation under varying conditions.
Soham Chakraborty, Amritesh Kumar, Nabanita Adhikary, and Aditya Narula
IEEE
This paper proposes a Non-Isolated high gain Multilevel Hybrid Switched Inductor Cascaded Boost DC-DC converter (MHSICB). The MHSICB circuit comprises a hybrid switched inductor cell, a multistage switched capacitor cell, and a cascaded boost DC-DC converter, which collectively enhance the overall voltage gain of the converter. The proposed MHSICB demonstrates the capability to achieve an output voltage 14 times higher than the input voltage. The paper provides a comprehensive mathematical analysis for each MODE of operation, elucidating the voltage and current stress experienced by the various components. The MHSICB design incorporates several advantages, including high voltage gain, reduced voltage stress on the active switch, and a relatively lower count of diodes, enabling a higher conversion ratio. A comparative assessment is conducted with other previous topologies reported in the literature. To regulate the output voltage of the MHSICB, a double-loop PI controller is implemented, and its performance is evaluated under sudden disturbances at the load and source sides. The viability and effectiveness of the MHSICB are validated through MATLAB simulation results.
Muhammad Usman Asad, Umar Farooq, Jason Gu, Rajeeb Dey, Nabanita Adhikary, Rupak Datta, and Chunqi Chang
ACTA Press
Prince Kumar, Kunal Kumar, Aashish Kumar Bohre, and Nabanita Adhikary
Informa UK Limited
Alankrita, A. Pati, N. Adhikary, S.K. Mishra, B. Appasani, and Taha Selim Ustun
Elsevier BV
Diptadip Das, Nabanita Adhikary, and Saurabh Chaudhury
IEEE
This paper presents a sensor fusion methodology for autonomous vehicles (AVs) using Light Detection and Ranging (LiDAR) and camera. Mostly sensors like camera or LiDAR is used only as the sensor for visual perception in AVs. But the hindrance comes during bad weather conditions, dim light or night time. To alleviate this problem, a method which combines both LiDAR and camera sensor using odometry is explored in this paper. The study also attempts to employ Extended Kalman Filter (EKF) to reduce error in position estimate of the vehicle.
Diptadip Das, Nabanita Adhikary, and Saurabh Chaudhury
IEEE
In this paper sensor fusion methodology along with odometry and motion estimation for autonomous vehicles (AVs) using Light Detection and Ranging (LiDAR) and camera is explored. Since during bad weather conditions, dim light or night time the sensor may not give very good readings, odometry can used in such situations. Odometry gives an estimation of change in position of given vehicle using data from sensors. The study also examines the impact of Extended Kalman Filter (EKF) to reduce error in position estimate of the vehicle both before and after using odometry through a KITTI data-set on vehicle motion.
Manash Jyoti Deori, Nabanita Adhikary, and Krishna Jyothi Pallacherla
Springer Singapore
Nabanita Adhikary, Rajeeb Dey, Muhammad Usman Asad, Jason Gu, Umar Farooq, and Rupak Dutta
Springer Singapore
Nabanita Adhikary and Jobin Mathew
Springer Singapore
Koceila Cherfouh, Jason Gu, Umar Farooq, Muhammad Usman Asad, Rajeeb Dey, Nabanita Adhikary, and Chunqi Chang
Springer Singapore
Muhammad Usman Asad, Jason Gu, Umar Farooq, Rajeeb Dey, Nabanita Adhikary, Rupak Datta, and Chunqi Chang
Springer Singapore
Nabanita Adhikary and Chitralekha Mahanta
Elsevier BV
Abstract This paper investigates the method of implementing dynamic controller on a manipulator which do not have direct drive joints. A previously proposed torque to position conversion method for servo actuated robot manipulators is used with an adaptive backstepping based sliding mode controller for dynamic trajectory tracking control. The proposed controller uses a nonsingular finite time sliding surface to achieve finite time stability as well as higher tracking performance. To avoid a structurally complex control law as well as obtain partial model independency of the controller, the soft nonlinearities of the manipulator are estimated using the time delay control philosophy where delayed signals are used to estimate the model nonlinearities. The entire system is validated using simulation and experimental studies.
Nabanita Adhikary and Chitralekha Mahanta
Elsevier BV
Abstract In this paper, a simple torque to position conversion method is proposed for position commanded servo actuators used in robot manipulators. The torque to position conversion is based on the low level controller of the servomotor. The proposed conversion law is combined with a backstepping sliding mode control method to realize a robust dynamic controller. The proposed torque based method can control a servomotor which can otherwise be operated only through position inputs. This method facilitates dynamic control for position controlled servomotors and it can be extended to position commanded robotic manipulators also. Simulation and experimental studies are conducted to validate the proposed torque to position conversion based robust control method.
Nabanita Adhikary and Chitralekha Mahanta
IEEE
Nabanita Adhikary and Chitralekha Mahanta
IEEE
In this paper, a hybrid impedance controller for a multi degrees of freedom (DoF) robot manipulator is developed using the backstepping sliding mode algorithm. Unlike the existing hybrid control method using sliding mode, backstepping is used in this paper to obtain a proportional-integral-derivative (PID) sliding surface. The PID sliding surface corresponds to the desired impedance dynamics of the manipulator both during free space and constraint space motion when the end-effector is in contact with the environment. The sliding gain of the controller is tuned adaptively to reduce the chattering and thereby limiting the usage of excessive control energy. A varying stiffness is used along the force controlled direction to improve the force tracking performance. Simulation results validate applicability of the proposed controller for multi DoF robot manipulators.
Nabanita Adhikary and Chitralekha Mahanta
ACM Press
Nabanita Adhikary and Chitralekha Mahanta
IEEE
Nabanita Adhikary and Chitralekha Mahanta
Elsevier BV