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Performance Analysis of Mars-Powered Descent-Based Landing in a Constrained Optimization Control Framework

Author

Listed:
  • Adnan Khalid

    (Department of Electrical Engineering, Sialkot Campus, University of Management and Technology Lahore, Sialkot 51310, Pakistan
    These authors contributed equally to this work.)

  • Mujtaba Hussain Jaffery

    (Electrical and Computer Engineering Department, COMSATS University Islamabad, Lahore 54000, Pakistan)

  • Muhammad Yaqoob Javed

    (Electrical and Computer Engineering Department, COMSATS University Islamabad, Lahore 54000, Pakistan)

  • Adnan Yousaf

    (Department of Electrical Engineering, Superior University, Lahore 54000, Pakistan)

  • Jehangir Arshad

    (Electrical and Computer Engineering Department, COMSATS University Islamabad, Lahore 54000, Pakistan
    These authors contributed equally to this work.)

  • Ateeq Ur Rehman

    (Department of Electrical Engineering, Government College University, Lahore 54000, Pakistan)

  • Aun Haider

    (Department of Electrical Engineering, Sialkot Campus, University of Management and Technology Lahore, Sialkot 51310, Pakistan)

  • Maha M. Althobaiti

    (Department of Computer Science, College of Computing and Information Technology, Taif University, Taif 21944, Saudi Arabia)

  • Muhammad Shafiq

    (Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Korea)

  • Habib Hamam

    (Faculty of Engineering, Université de Moncton, Moncton, NB E1A3E9, Canada
    Spectrum of Knowledge Production & Skills Development, Sfax 3027, Tunisia
    School of Electrical Engineering, Department of Electrical and Electronic Engineering Science, University of Johannesburg, Johannesburg 2006, South Africa)

Abstract

It is imperative to find new places other than Earth for the survival of human beings. Mars could be the alternative to Earth in the future for us to live. In this context, many missions have been performed to examine the planet Mars. For such missions, planetary precision landing is a major challenge for the precise landing on Mars. Mars landing consists of different phases (hypersonic entry, parachute descent, terminal descent comprising gravity turn, and powered descent). However, the focus of this work is the powered descent phase of landing. Firstly, the main objective of this study is to minimize the landing error during the powered descend landing phase. The second objective involves constrained optimization in a predictive control framework for landing at non-cooperative sites. Different control algorithms like PID and LQR have been developed for the stated problem; however, the predictive control algorithm with constraint handling’s ability has not been explored much. This research discusses the Model Predictive Control algorithm for the powered descent phase of landing. Model Predictive Control (MPC) considers input/output constraints in the calculation of the control law and thus it is very useful for the stated problem as shown in the results. The main novelty of this work is the implementation of Explicit MPC, which gives comparatively less computational time than MPC. A comparison is done among MPC variants in terms of feasibility, constraints handling, and computational time. Moreover, other conventional control algorithms like PID and LQR are compared with the proposed predictive algorithm. These control algorithms are implemented on quadrotor UAV (which emulates the dynamics of a planetary lander) to verify the feasibility through simulations in MATLAB.

Suggested Citation

  • Adnan Khalid & Mujtaba Hussain Jaffery & Muhammad Yaqoob Javed & Adnan Yousaf & Jehangir Arshad & Ateeq Ur Rehman & Aun Haider & Maha M. Althobaiti & Muhammad Shafiq & Habib Hamam, 2021. "Performance Analysis of Mars-Powered Descent-Based Landing in a Constrained Optimization Control Framework," Energies, MDPI, vol. 14(24), pages 1-14, December.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:24:p:8493-:d:704085
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    References listed on IDEAS

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    1. Shuo Zhang & Xuan Zhao & Guohua Zhu & Peilong Shi & Yue Hao & Lingchen Kong, 2020. "Adaptive trajectory tracking control strategy of intelligent vehicle," International Journal of Distributed Sensor Networks, , vol. 16(5), pages 15501477209, May.
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    Cited by:

    1. Bilal Masood & Song Guobing & Jamel Nebhen & Ateeq Ur Rehman & Muhammad Naveed Iqbal & Iftikhar Rasheed & Mohit Bajaj & Muhammad Shafiq & Habib Hamam, 2022. "Investigation and Field Measurements for Demand Side Management Control Technique of Smart Air Conditioners located at Residential, Commercial, and Industrial Sites," Energies, MDPI, vol. 15(7), pages 1-23, March.

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