Main Article Content


Model mobile robot that used to this simulation is type car like vehicle steering. Mobile robot type car like vehicle steering is mobile robot that move using force of rear wheel and front rear of mobile robot functions as steering to control direction of mobile robot. The dynamic nonlinear model mobile robot is implemented to view influence disturbance of mobile robot to longitudinal direction mobile robot that used to planetary exploration in rough terrain. The model that used to simulation is nonlinear multivariable MIMO with 5 input and 7 output. The simulation has done by using Simulink of Matlab. The simulations were carried out by giving 4 conditions, namely without disturbance, with an incline angle of 30 (0.5236 rad), with a rough terrain angle of 28.6479 (+0.5 rad), and a combination of 30 incline angle and 28.6479 rough terrain angle. The simulation results with 3 mobile robots show accurate results.


Mobile Robot, Car Like Vehicle Steering, MIMO System, Nonlinear Multivariable System Rough Terrain.

Article Details

How to Cite
Emilliano E, Hindersah H. Modeling and Simulation Longitudinal Mobile Robotic with Rough Terrain and Ascent Angle Disturbance. Eksakta [Internet]. 2021May9 [cited 2021Jun.12];22(2):110-36. Available from:


  1. Beer, F.P. and Johnston Jr, E.R. (1995), Vector Mechanics for Engineers: DYNAMICS, McGraw-Hill, Inc., Singapore, 16 – 38.
  2. Hashem Zamanian, Farid Javidpour (2016), “Dynamic Modeling and Simulation of 4 Wheel Skid Steering Mobile Robot With Considering Tires and Lateral Slips”, International Journal of Scientific Research in Knowledge, February 2016
  3. Chapra, S. C. and Canale, R. P. (1988), Numerical Methods for Engineers, Second Edition, McGraw-Hill Inc., New York.
  4. Friedland, B. (1987), Control System Design: An Introduction to State-Space Methods, McGraw-Hill Book Co., Inc., Singapore.
  5. Jean, Slotine, J.E., and Li, W. (1991), Applied Nonlinear Control, Prentice-Hall, Inc., New Jersey.
  6. Johansson, R. (1993), System Modeling and Identification, Prentice-Hall, Inc., New Jersey.
  7. Ljung, L. and Glad, T. (1994), Modeling of Dynamic Systems, Prentice Hall, Inc., New Jersey.
  8. Ogata, K. (1997), Modern Control Engineering, Prentice Hall of India, India.
  9. Petkov, Christov, and Konstantinov. (1991), Computational Methods for Linear Control Systems, Prentice Hall International, Englewood Cliffs.
  10. Ramanata, P. (1998), Optimal Vehicle Path Generator Using Optimization Methods, Thesis submitted to the Faculty of the Virginia Polytechnic Institute and State University in partial fulfillment of the requirements for the degree of Master of Science in Mechanical Engineering, Virginia Polytechnic Institute and State University, 10 – 28.
  11. Raven, F.H. (1995), Automatic Control Engineering, McGraw-Hill, Inc., Singapore, 16 – 38.
  12. Yiyang Wang. (2002), Robust Model Predictive Control, A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor Philosophy (Chemical Engineering), University of Wisconsin Madison, 09-29.