A review on applications of soft computing in design and development of intelligent autonomous robots

Pratihar, Dilip Kumar; Pratihar, Bitan

doi:10.3233/his-170242

Cited by 7 publications

(6 citation statements)

References 73 publications

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“…On the other hand, TRAINSCG updates network weights and biases based on scaled conjugate gradient method. 33 The final algorithm and model used in the present work has been selected finally after several trials and literature review. 14,15,19,31 The feed-forward network is made up of neurons that are divided into three layers: input, hidden, and output, with weights connecting them.…”

Section: Optimization Methodologymentioning

confidence: 99%

The use of machine learning and metaheuristic algorithm for wear performance optimization of AISI 1040 steel and investigation of corrosion resistance

Agrawal

Mukhopadhyay

2022

Proceedings of the Institution of Mechanical Engineers, Part J:

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AISI 1040 steel offers a wide range of industrial applications due to its mechanical characteristics and applicability. The present work investigates the wear performance of AISI 1040 steel under dry sliding conditions and its optimization using combined machine learning (ML) and metaheuristic algorithm. Sliding wear test were carried out on a pin-on-disc tribometer by varying the load (10–100 N), sliding speed (0.5–1.5 m/s) and sliding distance (400–1000 m). The test parameters were varied at three levels. Experiments were carried out following combinations in Taguchi's L27 orthogonal array (OA). Artificial neural network (ANN) was used to model the process parameters with wear rate. The trained network was optimized using genetic algorithm (GA) to predict optimal wear rate. This methodology has been termed as ANN-GA method. The results were compared with conventional Taguchi based and regression based GA optimization. A significant reduction in the wear rate could be realized due to optimization using ANN-GA method. This work also examined the corrosion behaviour of AISI 1040 steel exposed to 3.5% NaCl, 3.5% NaOH, 0.5 M H2SO4 and 3.5% NaCl + 0.5 M H2SO4 to simulate various corrosive environments encountered in industrial applications. A nobler corrosion potential was obtained in 3.5% NaOH. Investigations of corroded samples showed pitting corrosion in 3.5% NaCl, 0.5 M H2SO4 as well as combined chloride and sulphate attack. On the other hand, negligible corrosion was observed in 3.5% NaOH.

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Section: Optimization Methodologymentioning

confidence: 99%

The use of machine learning and metaheuristic algorithm for wear performance optimization of AISI 1040 steel and investigation of corrosion resistance

Agrawal

Mukhopadhyay

2022

Proceedings of the Institution of Mechanical Engineers, Part J:

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“…GA is a population‐based meta‐heuristic algorithm that comprises a population of NP = 10 chromosomes in the D ‐dimensional search space, which at generation t is represented by Pop t =

\{ {{{\vec X}_1}( t ),{{\vec X}_2}( t ), \ldots ,{{\vec X}_{NP}}( t )} \}\;

(Congradac & Kulic, 2009; Mukhopadhyay et al., 2017; Pratihar, 2007). Each

{X_i}

is a real number instead of bit.…”

Section: Tools and Techniquesmentioning

confidence: 99%

Shelf life enhancement technique of Musa acuminata in a controlled environment and optimization of process parameters affecting shelf life using genetic algorithm

Ghosh,

De,

Chowdhury

2023

Journal of Food Science

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An economical and effective storage solution has been designed in this work for the storage of postharvest fruits and vegetables. Musa acuminata or banana has a shelf life of 5–6 days in open uncontrolled environment. This article reports a storage solution of M. acuminata in a controlled enclosure containing titanium oxide (TiO2)‐coated inner walls and irradiated with ultraviolet ray of band “C,” an air filtration unit, 5% by volume potassium permanganate (KMnO4) solution in a clay pot, grow lights, and activated charcoal granules. The same fruit was kept in an uncontrolled environment too. The percentages of dark spots on banana (M. acuminata) upon storage in controlled and uncontrolled environments have been estimated using an image‐processing algorithm. The prediction of dark spots was conducted using multi‐linear and multivariate polynomial regression. Experimentation with optimum process parameters obtained with genetic algorithm resulted in a shelf life extension of 6 days as compared to its storage in an uncontrolled environment. The setup can be used in vegetable and fruit markets for the extension of shelf life of postharvest perishable items in a compact and cost‐effective manner. The setup does not use any refrigeration process thereby decreasing energy requirement.

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“…In addition, the design process can be achieved manually or automated with the assistance of GA, and it is integrated into the FLC, aligning it with the system's Knowledge-Base using training set scenarios [14]. The FLC's accuracy in estimating input and output sets can be improved further through GA-based tuning.…”

Section: Implementation Of Dpfc Integrated To Genetic-flcmentioning

confidence: 99%

“…The FLC's accuracy in estimating input and output sets can be improved further through GA-based tuning. This GAbased optimization operates in a step-by-step manner, commencing with individual analysis and producing subsequent results [14][15][16]. In the course of the study, the population is analyzed using GA-based binary code, with the creation of executions being randomized.…”

Section: Implementation Of Dpfc Integrated To Genetic-flcmentioning

confidence: 99%

“…The mapping within the linear system is obtained by considering the values of variables for each individual string, and the deviation in absolute value is predicted through fitness analysis scenarios. The resulting population undergoes further modifications through reproduction, crossover, and mutation as part of the GA strings' deployment [14]. The proposed GA-FLC is depicted in Figure 6, and the triangular Modular Function of FLC is represented by Eq.…”

Section: Implementation Of Dpfc Integrated To Genetic-flcmentioning

confidence: 99%

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Power Quality Enhancement of Solarwind Grid Connected System Employing Genetic-Based Anfis Controller

2023

IRJMETS

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The increasing global demand for electricity has driven the exploration of renewable energy sources to generate power in an environmentally friendly manner. Solar photovoltaic systems and wind-based generators are considered significant sources of renewable energy and are referred to as Distributed Generation units due to their dispersed nature. These units use bidirectional converters to provide auxiliary services on both the grid side and the load side, regardless of the microgrid operation mode. Additionally, DC power generation units are converted into AC systems through inverters. These systems not only introduce voltage and current harmonics and power frequency deviations but also push the distribution system into precarious operating conditions. This underscores the need for advanced control strategies for microgrid architecture.As a result, power electronic converters introduce harmonics into the system, which can impact overall system performance. To address these emerging challenges, the authors have introduced an advanced custom power device called the Distributed Power Flow Controller (DPFC). In this study, the proposed solar-wind hybrid energy system is primarily evaluated using the DPFC mechanism. Subsequently, the system is further assessed using a Genetic Algorithm (GA)-based fuzzy logic controller and a GA-based Adaptive Neuro Fuzzy Inference System (ANFIS) controller for shunt control within the context of the DPFC mechanism. The validity of the results is confirmed through simulations using MATLAB/Simulink software.

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A review on applications of soft computing in design and development of intelligent autonomous robots

Cited by 7 publications

References 73 publications

The use of machine learning and metaheuristic algorithm for wear performance optimization of AISI 1040 steel and investigation of corrosion resistance

The use of machine learning and metaheuristic algorithm for wear performance optimization of AISI 1040 steel and investigation of corrosion resistance

Shelf life enhancement technique of Musa acuminata in a controlled environment and optimization of process parameters affecting shelf life using genetic algorithm

Power Quality Enhancement of Solarwind Grid Connected System Employing Genetic-Based Anfis Controller

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