R. Saravana Kumar

Bio: R. Saravana Kumar is an academic researcher from VIT University. The author has contributed to research in topics: Aluminium alloy & Extended Kalman filter. The author has an hindex of 4, co-authored 18 publications receiving 32 citations.

Analysis of Logic Gates for Generation of Switching Sequence in Symmetric and Asymmetric Reduced Switch Multilevel Inverter

Abstract: Analysis of logic gates for the switching sequence operation of reduced switch multilevel inverter (MLI) is introduced in this paper. Two variants of MLI with reduced switches are considered for the analysis of the logic gates to obtain proper output voltage level. One MLI is with the symmetrical voltage, and the other is with the asymmetrical voltage. The analysis of the proposed logical operation is presented through the single-phase seven-level output voltage for both symmetrical and asymmetrical inverters. Input pulse pattern for the operation of the logic gates are chosen from the multi carrier pulse width modulation (PWM) techniques as phase disposition (PD), phase opposition disposition (POD), and alternate POD (APOD). The analysis for the generation of the required pulse pattern to operate the switches in MLI is presented as logical equations. The simulation work is performed and evaluated with the MATLAB/Simulink. The real-time simulation is performed for the required pulse pattern and generated with the support of dSPACE 1104. The THD comparative analysis is analyzed with a modulation index and various PWM methods.

State Estimation and Sensor Bias Detection Using Adaptive Linear Kalman Filter

Abstract: For safe and economical process operations, state estimation of dynamic systems is an important prerequisite, since all the process variables are generally not measurable. Keeping this in view, a linear and non-linear observer has been developed to estimate the state of a noisy dynamic system. Also chemical processes have been suffering from several faults since many decades. Finding the system faults and isolating them is essential because if a fault occurs in the plant, there will be serious economic losses due to drop in productivity. To serve this purpose, an Adaptive Linear Kalman Filter (ALKF) and an Extended Kalman Filter (EKF) have been designed to detect and isolate the faults. Of all the faults, sensor faults are taken into consideration in this paper. Our task is to compare the results of two estimators and to identify the better one, in estimating the state of non-linear system along with bias detection. Simulation studies are also included to estimate the residuals of the estimators.

Soft Grippers for Automatic Crop Harvesting: A Review

Abstract: Agriculture 4.0 is transforming farming livelihoods thanks to the development and adoption of technologies such as artificial intelligence, the Internet of Things and robotics, traditionally used in other productive sectors. Soft robotics and soft grippers in particular are promising approaches to lead to new solutions in this field due to the need to meet hygiene and manipulation requirements in unstructured environments and in operation with delicate products. This review aims to provide an in-depth look at soft end-effectors for agricultural applications, with a special emphasis on robotic harvesting. To that end, the current state of automatic picking tasks for several crops is analysed, identifying which of them lack automatic solutions, and which methods are commonly used based on the botanical characteristics of the fruits. The latest advances in the design and implementation of soft grippers are also presented and discussed, studying the properties of their materials, their manufacturing processes, the gripping technologies and the proposed control methods. Finally, the challenges that have to be overcome to boost its definitive implementation in the real world are highlighted. Therefore, this review intends to serve as a guide for those researchers working in the field of soft robotics for Agriculture 4.0, and more specifically, in the design of soft grippers for fruit harvesting robots.

Robotic Green Asparagus Selective Harvesting

Abstract: Robotic harvesting in the open field demands innovative solutions in robot perception and mechanics to cope with environmental challenges. In this paper, a prototype robotic harvester that is able to cope with the challenges of selective harvesting of green asparagus is presented. The harvester is able to drive along an asparagus dam in the field, detect asparagus stalks, identify stalks that are ready for harvesting, and perform harvesting without damaging. These system abilities are enabled by a novel vision perception module and a novel harvesting mechanism. The perception module is based on three-dimensional-point cloud processing, and it is able to reliably and robustly detect the asparagus stalks, their positions, and dimensions. A novelty of the proposed harvesting mechanism is a multi-tools solution to increase harvesting productivity. The results of the first outdoor field tests demonstrate the applicability of the presented robotic harvester.

Amaran:An Unmanned Robotic Coconut Tree Climber and Harvester

Abstract: There is an acute shortage of human coconut tree climbers to harvest coconuts in India and other developing countries. The consequential diminished availability of skilled tree climbers, to pass along the trade secrets to the succeeding generations, highlights the exigent need for an unmanned coconut-tree-climber as an alternative solution. This article presents Amaran, a novel robotic coconut tree climber and harvester. Amaran is comprised of a harvester with a distinctive robotic arm (RA) and a cutter, which can be attached to a ring-shaped frame that can be fastened to a coconut tree. Amaran can be controlled, through a wireless interface, either by an operator on the ground or via a smartphone-based app. Once dispositioned in the vicinity of the coconut bunch, the cutter is commanded to harvest the coconuts by cutting the stem of the bunch attached to the tree. Verification of the Amaran robot's viability was confirmed by tests conducted on a coconut tree set up in the design lab. Subsequently, the design of the Amaran robot was validated by field tests on a coconut farm. In this article, we discuss the design of the Amaran body and harvester. Amaran's dexterity in climbing and harvesting coconuts can be challenged by height, circumference, and inclination of the tree. Experimental results show that Amaran can successfully climb trees up to 15.2 m tall, circumferences ranging from 0.66 to 0.92 m, and tree trunk inclinations of up to 30°.

Fault detection and identification using combination of EKF and neuro-fuzzy network applied to a chemical process (CSTR)

Abstract: In this paper, a new algorithm is proposed for fault detection and identification (FDI) in a class of nonlinear systems by combining the extended Kalman filter (EKF) and neuro-fuzzy networks (NFNs). There is an abundance of the literature on fault diagnosis ranging from model-based methods to data-driven approaches that have advantages and drawbacks. One may employ the advantages of different approaches to develop a high-efficient method for fault diagnosis. Initially, an EKF is designed to estimate the system output and to generate accurate residuals by a mathematical model of the process. Then, an NFN is designed for making decision using the mean value of the residuals. The network assigns a locally linear model to each faulty condition of the system. The validity of the models is determined based on the fuzzy rules. Combining the introduced EKF and the introduced NFN causes the proposed method to be independent of pre-designing a bank of observers in the model-based methods. Moreover, there is no need for extracting the features from the signals without any physical insight as well as computational complexity in the data-driven techniques. The effectiveness of the proposed FDI scheme is verified by applying it to a chemical plant as the case study, namely, continuous stirred tank reactor process. Simulation results show that the proposed methodology is very effective to detect and identify the faults of the system in different faulty modes.