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Cascade Adaptive MPC with Type 2 Fuzzy System for Safety and Energy Management in Autonomous Vehicles: A Sustainable Approach for Future of Transportation

Author

Listed:
  • Duong Phan

    (Division of Mechatronics, Mechanical Engineering Institute, Vietnam Maritime University, Haiphong 180000, Vietnam)

  • Ali Moradi Amani

    (School of Engineering, RMIT University, Melbourne, VIC 3083, Australia)

  • Mirhamed Mola

    (School of Electrical and Computer Engineering, Shiraz University, Shiraz 71348, Iran)

  • Ahmad Asgharian Rezaei

    (School of Engineering, RMIT University, Melbourne, VIC 3083, Australia)

  • Mojgan Fayyazi

    (School of Engineering, RMIT University, Melbourne, VIC 3083, Australia)

  • Mahdi Jalili

    (School of Engineering, RMIT University, Melbourne, VIC 3083, Australia)

  • Dinh Ba Pham

    (Division of Mechatronics, Mechanical Engineering Institute, Vietnam Maritime University, Haiphong 180000, Vietnam)

  • Reza Langari

    (Engineering Technology and Industrial Distribution (ETID), Texas A&M University (TAMU), College Station, TX 77843, USA)

  • Hamid Khayyam

    (School of Engineering, RMIT University, Melbourne, VIC 3083, Australia)

Abstract

A sustainable circular economy involves designing and promoting new products with the least environmental impact through increasing efficiency. The emergence of autonomous vehicles (AVs) has been a revolution in the automobile industry and a breakthrough opportunity to create more sustainable transportation in the future. Autonomous vehicles are supposed to provide a safe, easy-to-use and environmentally friendly means of transport. To this end, improving AVs’ safety and energy efficiency by using advanced control and optimization algorithms has become an active research topic to deliver on new commitments: carbon reduction and responsible innovation. The focus of this study is to improve the energy consumption of an AV in a vehicle-following process while safe driving is satisfied. We propose a cascade control system in which an autonomous cruise controller (ACC) is integrated with an energy management system (EMS) to reduce energy consumption. An adaptive model predictive control (AMPC) is proposed as the ACC to control the acceleration of the ego vehicle (the following vehicle) in a vehicle-following scenario, such that it can safely follow the lead vehicle in the same lane on a highway. The proposed ACC appropriately switches between speed and distance control systems to follow the lead vehicle safely and precisely. The computed acceleration is then used in the EMS component to find the optimal engine torque that minimizes the fuel consumption of the ego vehicle. EMS is designed based on two methods: type 1 fuzzy logic system (T1FLS) and interval type 2 fuzzy logic system (IT2FLS). Results show that the combination of AMPC and IT2FLS significantly reduces fuel consumption while the ego vehicle follows the lead vehicle safely and with a minimum spacing error. The proposed controller facilitates smarter energy use in AVs and supports safer transportation.

Suggested Citation

  • Duong Phan & Ali Moradi Amani & Mirhamed Mola & Ahmad Asgharian Rezaei & Mojgan Fayyazi & Mahdi Jalili & Dinh Ba Pham & Reza Langari & Hamid Khayyam, 2021. "Cascade Adaptive MPC with Type 2 Fuzzy System for Safety and Energy Management in Autonomous Vehicles: A Sustainable Approach for Future of Transportation," Sustainability, MDPI, vol. 13(18), pages 1-17, September.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:18:p:10113-:d:632260
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    References listed on IDEAS

    as
    1. Khayyam, Hamid & Naebe, Minoo & Milani, Abbas S. & Fakhrhoseini, Seyed Mousa & Date, Abhijit & Shabani, Bahman & Atkiss, Steve & Ramakrishna, Seeram & Fox, Bronwyn & Jazar, Reza N., 2021. "Improving energy efficiency of carbon fiber manufacturing through waste heat recovery: A circular economy approach with machine learning," Energy, Elsevier, vol. 225(C).
    2. Duong Phan & Alireza Bab-Hadiashar & Reza Hoseinnezhad & Reza N. Jazar & Abhijit Date & Ali Jamali & Dinh Ba Pham & Hamid Khayyam, 2020. "Neuro-Fuzzy System for Energy Management of Conventional Autonomous Vehicles," Energies, MDPI, vol. 13(7), pages 1-16, April.
    3. Phan, Duong & Bab-Hadiashar, Alireza & Lai, Chow Yin & Crawford, Bryn & Hoseinnezhad, Reza & Jazar, Reza N. & Khayyam, Hamid, 2020. "Intelligent energy management system for conventional autonomous vehicles," Energy, Elsevier, vol. 191(C).
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    Cited by:

    1. Mojgan Fayyazi & Paramjotsingh Sardar & Sumit Infent Thomas & Roonak Daghigh & Ali Jamali & Thomas Esch & Hans Kemper & Reza Langari & Hamid Khayyam, 2023. "Artificial Intelligence/Machine Learning in Energy Management Systems, Control, and Optimization of Hydrogen Fuel Cell Vehicles," Sustainability, MDPI, vol. 15(6), pages 1-38, March.
    2. Kawtar Rahaoui & Hamid Khayyam & Quoc Linh Ve & Aliakbar Akbarzadeh & Abhijit Date, 2021. "Renewable Thermal Energy Driven Desalination Process for a Sustainable Management of Reverse Osmosis Reject Water," Sustainability, MDPI, vol. 13(19), pages 1-15, September.
    3. Ziad Al-Saadi & Duong Phan Van & Ali Moradi Amani & Mojgan Fayyazi & Samaneh Sadat Sajjadi & Dinh Ba Pham & Reza Jazar & Hamid Khayyam, 2022. "Intelligent Driver Assistance and Energy Management Systems of Hybrid Electric Autonomous Vehicles," Sustainability, MDPI, vol. 14(15), pages 1-21, July.

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