Forthcoming Articles

International Journal of Nonlinear Dynamics and Control

International Journal of Nonlinear Dynamics and Control (IJNDC)

Forthcoming articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

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International Journal of Nonlinear Dynamics and Control (4 papers in press)

Regular Issues

  • Convolution neural network-based mapping for the reliable detection of potholes in unstructured environment for the accidental free autonomous navigation   Order a copy of this article
    by Tanish Mavi, Rampal Grih Dhwaj Singh, Ankit Kumar, Digvijay Singh, Ravinder Singh 
    Abstract: Reliable autonomous navigation is a crucial problem in unmanned ground vehicle (UGV) and to achieve the reliability in the navigation, the efficient mapping is one of the prerequisites to be achieved. Unreliable mapping resulted in the SLAM problem that degrade the reliability of the autonomous navigation. The obstacle detection segment in the mapping has been improved a lot, however there are a few issues that still degrade the performance of the UGV. Feature detection is a significant segment in autonomous navigation, usually the potholes detection as feature is not considered that causes major accident during navigation. The proposed research work focuses on the detection of the potholes with a camera-based convolution neural network (CCNN) approach to modify the trajectory for the accidental free autonomous navigation of the unmanned vehicle. The proposed CCNN based approach is implemented in various simulated/real-time environments for the efficient mapping. With the implementation of prosed technique, mAP50/precision value of the custom train obstacle detection and segmentation neural network is 0.93 and 0.91 respectively that resulted in accuracy of 84% in detection of potholes and the number of collisions in the trials is reduces to 100%.
    Keywords: autonomous navigation; real-time; deep learning; computer vision; obstacle detection; neural network; pothole detection.
    DOI: 10.1504/IJNDC.2024.10066000
     
  • A review analysis of health monitoring of electric vehicle   Order a copy of this article
    by Arunesh Kumar Singh, Mohammad Aasim 
    Abstract: The future of transportation must include electromobility that increase the dependability of electric vehicles (EVs) by anticipating, identifying, and assessing electric power train malfunctions. The EVs are promoting because of green energy and pollution free as copared to ICE vehicles. The energy management system (EMS) is the main subsystem of the EVs. When running a cycle, the starting SOC and the distance travelled determine the optimal power distribution situation for plug-in hybrid electric vehicles. One of the main challenges in EMS development is determining the best power-sharing control, which necessitates complex computations and in-depth knowledge of future driving characteristics. Monitoring of the components/subsystems of EVs is essential for the smooth functioning and sustainability of EVs. In this paper, the various parameters of EVs/hybrid vehicles have been discussed that affects the health of the EVs.
    Keywords: electrical vehicle; EV; plug in hybrid electrical vehicles; PHEV; health monitoring; energy management system; EMS; EV health.
    DOI: 10.1504/IJNDC.2024.10066096
     
  • Condition monitoring of microbial fuel cells   Order a copy of this article
    by Abhinav K. Gautam, Anand Verma, Manish Singh, Yogesh K. Chauhan, Parul Singh 
    Abstract: A microbial fuel cell (MFC), is a new type of bioreactor that plays an important role in both energy recovery and waste-water purification. In this paper, we studied the method for condition monitoring of microbial fuel cells (MFCs), which generate sustainable energy. It addresses the benefits and drawbacks of existing approaches and suggests using physical techniques to forecast MFC performance. The review highlights the importance of condition monitoring for optimising MFC operation. In this project work, we are using river sand and drain sludge substrates for the microbial fuel cell (MFC), and boiling rices starch was used as a source of carbohydrates for the development of bacteria. The membrane was made of the salt bridge. As an electron acceptor, water was added to the cathode chamber.
    Keywords: microbial fuel cells; MFCs; condition monitoring; cell performance; salt bridge.
    DOI: 10.1504/IJNDC.2024.10068538
     
  • Robustness and chattering attenuation in sliding mode control: a comparative study   Order a copy of this article
    by Terkaa Joseph Shima, Hassan Abdullahi Bashir, John E. Oche, Godwin Mzaga Iyoo 
    Abstract: In this research, the performance of traditional SMC, ISMC, and STC have been compared with regards to robustness and chattering attenuation abilities. The effects of replacing the signum function with smoother functions in each of the SMC, ISMC and STC have also been examined. On evaluation using cart-inverted pendulum benchmark, comparative results show that ISMC offers best performance in terms of robustness and transient response while STC offers its best performance in terms of chattering reduction. Moreover, the use of either of the saturation function or the hyperbolic tangent function in place of the signum function effectively eliminates chattering in the control action in each of the SMC, ISMC and STC laws. Nevertheless, this tends to reduce the robustness of the control laws.
    Keywords: sliding mode control; SMC; disturbance rejection; chattering attenuation; cart-inverted pendulum system; CIP; underactuated systems.
    DOI: 10.1504/IJNDC.2025.10069957
     

 

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