Adaptive Cruise Control With Gain Scheduling Technique Under Varying Vehicle Mass

Muhammad Rony Hidayatullah, Jyh Ching Juang

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


Today, advanced driver-assistance systems (ADAS) come up with different abilities. One of them is the adaptive cruise control (ACC) system. The ACC system is a continuation of research on cruise control (CC) system, which integrates spacing control with the existing velocity control on the CC system. The vehicles with an ACC system guarantee traffic safety while at the same time ensure a well-driving sense. Many studies have demonstrated numerous control techniques applied as ACC controllers to accomplish uncertainty and perturbation issues. Nevertheless, most of the existing papers assumed the model vehicle dynamics as a linear time-invariant (LTI) system while designing the ACC controller. This paper proposed an ACC controller using the gain scheduling technique to deal with the model vehicle dynamics as a linear parameter varying (LPV) system. The passenger vehicle’s mass varies during ACC operation depending on how many passengers or loads on the vehicle’s trunks. Later, the vehicle’s mass is estimated by recursive least square (RLS) with a forgetting factor. Then, the disk margin is utilized to provide the high-level robustness at each operating or “frozen” point. The robustness performance will be analyzed using the worst-case gain metric while the uncertainty is modeled by integral quadratic constraints (IQC). The LPV system behavior, such as the rate vehicle’s mass, is also considered in the analysis. The effectiveness algorithm is validated through joint simulation between Matlab/Simulink and PreScan. The last, the comparison performance between gain scheduling and fixed gain ACC controller is evaluated.

Original languageEnglish
Pages (from-to)144241-144256
Number of pages16
JournalIEEE Access
Publication statusPublished - 2021

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)


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