International Journal of Dynamics and Control

, Volume 7, Issue 4, pp 1434–1442 | Cite as

Impedance control on rack steering vehicle for inertia shaping on cornering track

  • Norsharimie Mat Adam
  • Addie IrawanEmail author
  • Fawzan Khaizuran Faudzi


Overdriven factor in a vehicle motion is one of the issues that need to be tackled for safety and energy efficiencies, especially in the cornering track. This issue is crucial especially for rack steering vehicle with non-holonomic configuration, whereby inertia will contribute to high collisions to the peer walls or offroad incidents. Therefore this study has taken the initiative to propose a dynamic control technique that considers the interaction between the vehicle and the terrain using impedance control. This control technique allows coping with the issue by indirectly shaping inertia forces. The proposed impedance control is derived by handling the vehicle dynamic developed and shaping the vehicle steering angle. For the study purposes, a rack steering four wheels vehicle (RT4WV) is used as a platform, and its dynamic model was derived for analysis. The implementation of torque feedback based impedance control for inertia shaping is emphasized on both vertical (x axis) and horizontal (y axis) of the vehicle body, during which inertia could happen. The kinodynamic input for the system control input is the difference between steering angular changes in which representing front wheel angular changes. This proposed dynamic control strategy is verified by simulating the derived impedance control on RT4WV system model with road terrain and aerodynamic frictions as disturbances. The result shows that the proposed impedance control able to reduce the inertia forces via shaping the steering angular input to the vehicle although there have both road terrain and aerodynamic frictions, especially in cornering tracks.


Rack steering four wheels vehicle Inertia control Compliance Impedance control Motion control 



This research and development are supported by the Ministry Of Higher Education Malaysia under the Fundamental Research Grant Scheme (FRGS) (Grant No. FRGS/1/2016/TK04/UMP/02/9) and Universiti Malaysia Pahang (UMP) Research Grant (RDU160147).


  1. 1.
    Katrakazas C, Quddus M, Chen W-H, Deka L (2015) Real-time motion planning methods for autonomous on-road driving: state-of-the-art and future research directions. Transp Res Part C Emerg Technol 60:416–442CrossRefGoogle Scholar
  2. 2.
    Yu Z, Huang X, Wang J (2015) A least-squares regression based method for vehicle yaw moment of inertia estimation. In: American control conference (ACC), 2015, pp 5432–5437 Google Scholar
  3. 3.
    Yunong Y, Ha HM, Kim YK, Lee JM (2015) Balancing and driving control of a ball robot using fuzzy control. In: 12th international conference on ubiquitous robots and ambient intelligence (URAI), 2015, pp 492–494Google Scholar
  4. 4.
    Qian CL, Lee S, Ma C (2018) Robust formation maneuvers through sliding mode for multi-agent systems with uncertainties. IEEE/CAA J Autom Sin 5(1):342–351MathSciNetCrossRefGoogle Scholar
  5. 5.
    Zhang P, Du Y (2017) The robust maneuver flight control of hypersonic glide vehicles with input saturation using disturbance observer. In: IECON 2017—43rd annual conference of the IEEE industrial electronics society, pp 6584–6589Google Scholar
  6. 6.
    Fajri P, Prabhala VA, Ferdowsi M (2016) Emulating on-road operating conditions for electric-drive propulsion systems. IEEE Trans Energy Convers 31(1):1–11CrossRefGoogle Scholar
  7. 7.
    Cui M, Liu W, Liu H, Lü X (2016) Unscented Kalman filter-based adaptive tracking control for wheeled mobile robots in the presence of wheel slipping. In: 12th world congress on intelligent control and automation (WCICA), 2016, pp 3335–3340Google Scholar
  8. 8.
    Rajagopalan V, Meriçli Ç, Kelly A (2016) Slip-aware model predictive optimal control for path following. In: IEEE international conference on robotics and automation (ICRA), 2016, pp 4585–4590Google Scholar
  9. 9.
    Huang X, Wang J (2014) Real-time estimation of center of gravity position for lightweight vehicles using combined AKF–EKF method. IEEE Trans Veh Technol 63(9):4221–4231CrossRefGoogle Scholar
  10. 10.
    Xiao H et al (2017) Robust stabilization of a wheeled mobile robot using model predictive control based on neurodynamics optimization. IEEE Trans Ind Electron 64(1):505–516CrossRefGoogle Scholar
  11. 11.
    Jeong S, Kozai K (2017) Development of wheeled balancing wheelchair for lower limb disabled person: design of wheelchair platform. In: 17th international conference on control, automation and systems (ICCAS), 2017, pp 929–932Google Scholar
  12. 12.
    Deremetz M, Lenain R, Couvent A, Cariou C, Thuilot B (2017) Path tracking of a four-wheel steering mobile robot: a robust off-road parallel steering strategy. In: 2017 European conference on mobile robots (ECMR), 2017, pp 1–7Google Scholar
  13. 13.
    Wang Y, Deng W, Wu J, Zhu B, Zhang S (2014) Allocation-based control for four-wheel independently driven and braked electric vehicle considering actuators’ dynamic characteristics. In: 2014 IEEE international conference on systems, man, and cybernetics (SMC), 2014, pp 3354–3359Google Scholar
  14. 14.
    Li Q, Zhang Z, Zhao W (2012) Dynamic control for four-wheel independent drive electric vehicle. In: International conference on computer science and electronics engineering, 2012, vol 3, pp 252–256Google Scholar
  15. 15.
    Xiao F (2015) Optimal torque distribution for four-wheel-motored electric vehicle stability enhancement. In: IEEE international transportation electrification conference (ITEC), 2015, pp 1–9Google Scholar
  16. 16.
    Guazzelli PR, de Oliveira CM, de Castro AG, Pereira WC, de Aguiar ML (2016) Electric vehicle hardware-in-the-loop simulation with differentiator optimised by genetic algorithm. In: 12th IEEE international conference on industry applications (INDUSCON), 2016, pp 1–8Google Scholar
  17. 17.
    Motonaka K, Watanabe K, Maeyama S (2014) 3-Dimensional kinodynamic motion planning for an X4-Flyer using 2-dimensional harmonic potential fields. In: 14th international conference on control, automation and systems (ICCAS), 2014, pp 1181–1184Google Scholar
  18. 18.
    Masoud AA (2012) Motion planning with gamma-harmonic potential fields. IEEE Trans Aerosp Electron Syst 48(4):2786–2801CrossRefGoogle Scholar
  19. 19.
    Shenghao Z, Jinchun S (2011) Impedance control for vehicle driving with human operation under unstructured environment. In: International conference on internet computing and information services, 2011, pp 159–162Google Scholar
  20. 20.
    Qin M, Xiao N, Guo S, Guo P, Wang Y (2015) A proximal push force-based force feedback algorithm for robot-assisted vascular intervention surgery. In: IEEE international conference on mechatronics and automation (ICMA), 2015, pp 738–742Google Scholar
  21. 21.
    Guo S, Wang P, Guo J, Wei W, Ji Y, Wang Y (2013) A novel master-slave robotic catheter system for vascular interventional surgery. In: IEEE international conference on mechatronics and automation (ICMA), 2013, pp 951–956Google Scholar
  22. 22.
    Irawan A, Nonami K, Daud MR (2013) Optimal impedance control with TSK-type FLC for hard shaking reduction on hydraulically driven hexapod robot. In: Nonami K, Kartidjo M, Yoon KJ, Budiyono A (eds) Autonomous control systems and vehicles. Intelligent systems, control and automation: science and engineering, vol 65. Springer, TokyoGoogle Scholar
  23. 23.
    Boaventura T, Buchli J, Semini C, Caldwell DG (2015) Model-based hydraulic impedance control for dynamic robots. IEEE Trans Rob 31(6):1324–1336CrossRefGoogle Scholar
  24. 24.
    Salisbury JK (1980) Active stiffness control of a manipulator in cartesian coordinates. In: 19th IEEE conference on decision and control including the symposium on adaptive processes, 1980, pp 95–100Google Scholar
  25. 25.
    Hogan N (1984) Impedance control: an approach to manipulation. In: American control conference, 1984, pp 304–313Google Scholar
  26. 26.
    Komati B, Clevy C, Lutz P (2014) Force tracking impedance control with unknown environment at the microscale. In: IEEE international conference on robotics and automation (ICRA), 2014, pp 5203–5208Google Scholar
  27. 27.
    Yun SC, Parasuraman S, Ganapathy V (2011) Dynamic path planning algorithm in mobile robot navigation. In: IEEE symposium on industrial electronics and applications (ISIEA), 2011, pp 364–369Google Scholar
  28. 28.
    Koval MC, King JE, Pollard NS, Srinivasa SS (2015) Robust trajectory selection for rearrangement planning as a multi-armed bandit problem. In: IEEE/RSJ international conference on intelligent robots and systems (IROS), 2015, pp 2678–2685Google Scholar
  29. 29.
    Sintov A, Shapiro A (2015) A stochastic dynamic motion planning algorithm for object-throwing. In: IEEE international conference on robotics and automation (ICRA), 2015, pp 2475–2480Google Scholar
  30. 30.
    Spanogianopoulos S, Sirlantzis K (2015) Non-holonomic path planning of car-like robot using RRT* FN. In: 12th international conference on ubiquitous robots and ambient intelligence (URAI), 2015, pp 53–57Google Scholar
  31. 31.
    Xie C, van den Berg J, Patil S, Abbeel P (2015) Toward asymptotically optimal motion planning for kinodynamic systems using a two-point boundary value problem solver. In: IEEE international conference on robotics and automation (ICRA), 2015, pp 4187–4194Google Scholar
  32. 32.
    Moon C, Chung W (2015) Kinodynamic planner dual-tree RRT (DT-RRT) for two-wheeled mobile robots using the rapidly exploring random tree. IEEE Trans Ind Electron 62(2):1080–1090CrossRefGoogle Scholar
  33. 33.
    Bhoraskar A, Sakthivel P (2017) A review and a comparison of Dugoff and modified Dugoff formula with magic formula. In: International conference on nascent technologies in engineering (ICNTE), 2017, pp 1–4Google Scholar
  34. 34.
    Alam MM, Irawan A, Yin TY (2015) Buoyancy effect control in multi legged robot locomotion on seabed using integrated impedance-fuzzy logic approach. Indian J Geomarine Sci 44(12):1937–1945Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Robotics and Unmanned Systems (RUS) Research Group, Faculty of Electrical and Electronics EngineeringUniversiti Malaysia PahangPekanMalaysia

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