Neuroadaptive quantized PID sliding‐mode control for heterogeneous vehicular platoon with unknown actuator deadzone

Abstract

Summary This paper focuses on the problem of neuroadaptive quantized control for heterogeneous vehicular platoon when the follower vehicles suffer from external disturbances, mismatch input quantization, and unknown actuator deadzone. The PID‐based sliding‐mode (PIDSM) control technique is used due to its superior capability to reduce spacing errors and to eliminate the steady‐state spacing errors. Then, a neuroadaptive quantized PIDSM control scheme with minimal learning parameters is designed not only to guarantee the string stability of the whole vehicular platoon and ultimate uniform boundedness of all adaptive law signals but also to attenuate the negative effects caused by external disturbance, mismatch input quantization, and unknown actuator deadzone. Furthermore, optimizing the interspacing between consecutive vehicles is very important to reduce traffic congestion on highways, and a new modified constant time headway policy is proposed to not only increase traffic density but also address the negative effect of nonzero initial spacing, velocity, and acceleration errors. Compared with most existing methods, the proposed method does not linearize the system model and neither requires precise knowledge of the system model. Finally, the effectiveness and advantage of the proposed method are demonstrated by comparative simulation studies.

Event‐triggered practical finite‐time output feedback stabilization of a class of uncertain nonlinear systems

“…Finally, an example has been presented to demonstrate the validity of the proposed scheme. For future works, the proposed strategy can be extended to high‐order nonlinear systems or the actuator dead‐zone problem can be taken into account …”

“…For future works, the proposed strategy can be extended to high-order nonlinear systems 8 or the actuator dead-zone problem can be taken into account. 32…”

Distributed Integrated Sliding Mode-Based Nonlinear Vehicle Platoon Control with Quadratic Spacing Policy

“…To reach the desired platoon, many control theories have been applied to vehicle driving, including the consensus control [7], adaptive control [8][9][10], model predictive control [11], and the sliding mode control [12][13][14][15][16][17]. For example, a distributed consensus strategy with second-order dynamics is proposed to achieve the platooning of vehicles in [7], where the actuator saturation and absent velocity measurement are considered.…”

“…In these existing results, the sliding mode control method has attracted increasing interests due to its significant advantages in dealing with the external disturbances. For instance, the neuroadaptive quantized PID sliding mode control method for heterogeneous vehicle platoon is presented with external disturbances and unknown actuator dead-zone in [13]. e Pontryagin's minimum principle (PMP) based setpoint optimization and sliding mode control law are proposed for vehicle platoon in [14].…”

Autonomous platooning of multiple ground vehicles in rough terrain

“…In addition, a distributed nonlinear consensus delay‐dependent control algorithm was proposed for a connected vehicle platoons by incorporating the car‐following interactions between connected vehicles in Li Tang, Peeta, and Wang (2019b), and an inter‐vehicle distance control (IDC) system for the distributed autonomous platooning was proposed in J. Zhang, Feng, Yan, Qiao, and Wanga (2020). The adaptive control concept was directly utilized for the PID‐based sliding‐mode control of heterogeneous vehicular platoon in Guo, Wang, Liao, and Teo (2019) and for handling bidirectional interaction among vehicles and engine saturation constraints in Baldi, Liu, Jain, and Yu (2020), while an optimal control‐based CACC system was developed by enforcing a target time gap between platoon members in the space domain (Y. Zhang, Bai, Wang, & Hu, 2020). The cooperative control for a platoon of heterogeneous connected vehicles was proposed with directed acyclic interactions in Zheng, Bian, Li, and Li (2019), for incorporation of the consensus and car‐following interaction between connected vehicles in He, Li, Xu, and Hao (2019) and for the effect of medium access control (MAC) protocol and unreliable measurement on acceleration information in Wen and Guo (2019a).…”