Abstract
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.
Highlights
The expeditious technology development of advanced driverassistance systems (ADAS) delivers the driverless vehicle to be a reality
Concerning the above problems, this paper proposes the adaptive cruise control (ACC) system using a gain scheduling controller for the linear parameter varying (LPV) system, where the mass of vehicles varied during ACC operation and provides robustness analysis and LPV system’s performance
We focused on developing the ACC controller using the gain scheduling technique and analyzed the controller robustness under the LPV system
Summary
The expeditious technology development of advanced driverassistance systems (ADAS) delivers the driverless vehicle to be a reality. Chen et al proposed model-free and real-time ecological ACC (eco-ACC) based on action-dependent heuristic dynamic programming (ADHDP) controller to accomplish safety, comfortable driving, improve long-life battery, and energy consumption for car-following scenario [24]. (3) Most of the existing works considered scheduled variable as linear and angular velocity [6], [48], control signal saturation [45], vehicle’s velocity and road grade [46], [47], [49], and time gap [50]. Concerning the above problems, this paper proposes the ACC system using a gain scheduling controller for the LPV system, where the mass of vehicles varied during ACC operation and provides robustness analysis and LPV system’s performance. The proportional-derivative with low-pass filter (PD) gain scheduling is selected as the ACC controller strategy and tuned based on the linear model vehicle dynamics at the frozen points.
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