Platooning vehicles are beneficial in comfortable driving, safety, and enhanced transportation efficiency. Platooning vehicles with cooperative adaptive cruise control (CACC) mode can potentially result in a small distance between vehicles with assured string stability. This CACC-based platoon control can be realized by vehicle-to-vehicle (V2V) or vehicle-to-everything (V2X) communication between vehicles. In this regard, the distributed control framework is widely used. It claims robustness to communication uncertainties since each vehicle has its controller. With the rapid research on V2X, the centralized framework is another option for the platooning vehicle control framework. This paper investigates and analyzes the centralized and distributed control framework under platoon uncertainty. In most of the papers, the platoon uncertainty is considered as road slope and vehicle dynamics model. In this paper, the platoon uncertainty refers to the platoon types, namely homogeneous and heterogeneous platoon. The analytical platoon stability is studied for both frameworks under different platoon types. It gives the minimum time gap boundary to guarantee string stability for both frameworks. The numerical string stability is performed by the simulation to verify the analytical. The simulation is done using the integrated PreScan and Matlab/Simulink. Then, the robustness of both frameworks is evaluated under latency and packet loss. Finally, the performance of both frameworks is also evaluated.
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. INDEX TERMSAdaptive cruise control, advanced driver assistance systems, cruise control, gain scheduling controller, linear parameter varying system, spacing control. NOMENCLATURE B r,max Maximum brake pressure. T h,max Maximum throttle. m, m Actual and estimated mass of the vehicle. δ safe , δ rel , δ 0 Safe distance, relative distance, and minimum distance between vehicles. v ref , v x,h Longitudinal reference velocity and actual of velocity of host vehicle. t g Time gap between vehicles. X a Position of target or other vehicles in front of the host vehicle. X hPosition of the host vehicle.The associate editor coordinating the review of this manuscript and approving it for publication was Michail Makridis .
Despite its popularity in industrial application, PID controller suffers parameters setting difficulty due to set point change, disturbance, and ageing. This paper proposed Self-tuning PID controller using Dahlin method for temperature control of a laboratory scale mini-chamber. Experimental results show that the proposed controller has better performance compared to the conventional PID controller in term of rise time and settling time. It also shows that the algorithm can compensate the changing environment and robust toward the existence of disturbance.
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