Forthcoming and Online First Articles

International Journal of Mechatronics and Manufacturing Systems

International Journal of Mechatronics and Manufacturing Systems (IJMMS)

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 Articles will not be published until the final proofs are validated by their authors.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 Mechatronics and Manufacturing Systems (2 papers in press)

Regular Issues

  • Robust Learning Tracking Control Design for Soft Actuators   Order a copy of this article
    by Adnan Alamili, Ali Al-Ghanimi, Mukhalad Al-nasrawi 
    Abstract: This paper proposes a recursive sliding-mode control strategy for motion-tracking control of ionic polymer-metal composite soft actuator systems. The suggested controller is distinctive in that it can continuously modify the closed-loop response to maintain system stability. Accordingly, Lyapunov criteria have been used to establish the stability of the provided control technique. Additionally, since controller design does not require any prior knowledge of parameter uncertainties or system hysteresis, it is appropriate for ionic polymer-metal composites since its model changes depending on working conditions. Simulation investigations are conducted to validate the performance of the developed controller. The results demonstrate superior performance compared to the conventional sliding mode control approach.
    Keywords: sliding mode control; SMC; ionic polymer-metal composites; soft actuator; robust learning control; tracking control.
    DOI: 10.1504/IJMMS.2023.10059313
     
  • A novel robust online sustainable adaptation dynamics control method for robot movement by wheel type   Order a copy of this article
    by Nguyen Minh Quang, Le Thi Phuong Thanh, Nguyen Tien Tung 
    Abstract: A novel robust control algorithm called reinforcement learning online sustainable adaptive dynamic control (RLOSADC) was developed to solve the problem of approximation for nonlinear systems with absolutely no information about internal dynamics. The proposed control model was built based on the new algorithm called optimise cooperation many nonlinear systems (OCMNO) with powerful features and convergence capabilities. New and unique features of the proposed model are shown through a highly flexible design and control procedure. The traditional robot dynamic model is transformed into a tight feedback nonlinear system model for designing “integrated” kinetic and dynamic control laws to overcome the disadvantages of the previous method. The RLOSADC model has been applied to cling control robust, sustainable adaptation for optimising the kinematic and dynamic clinging quality indicators for robot movement by wheel type (RMWT). Numerical and experimental simulation results on RMWT show the effectiveness of the proposed RLOSADC control model. Future studies can extend the RLOSADC model to control more general nonlinear systems such as nonlinear systems with unknown structures.
    Keywords: OCMNO; RLOSADC; RMWT; Adaptive control; Nonlinear system.
    DOI: 10.1504/IJMMS.2023.10059314
     

 

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