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A neural network-based distributed adaptive approach combined with sliding mode technique is proposed for vehicle-following platoons in the presence of input saturation, unknown unmodeled nonlinear dynamics and external disturbances. A simple and straightforward strategy by adjusting only a single parameter is proposed to compensate for the effect of input saturation. Two spacing polices (i.e., traditional constant time headway (TCTH) policy and modified constant time headway (MCTH) policy) are used to guarantee string stability and maintain the desired spacing. Chebyshev neural networks (CNN) are used to approximate the unknown nonlinear functions in the followers on-line, and the implementation of the basis functions of CNN depends only on the leader's velocity and acceleration. Furthermore, unlike existing approaches, the nonlinearities of consecutive vehicles need not satisfy the matching condition. Finally, simulations are carried out to illustrate the effectiveness and advantage of the proposed methods, first using a numerical example, followed by a practical example of a high speed train platoon.

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.

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