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Neuroadaptive quantized PID sliding‐mode control for heterogeneous vehicular platoon with unknown actuator deadzone
Author(s) -
Guo Xianggui,
Wang Jianliang,
Liao Fang,
Teo Rodney Swee Huat
Publication year - 2018
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.4394
Subject(s) - control theory (sociology) , dead zone , platoon , actuator , headway , pid controller , sliding mode control , engineering , quantization (signal processing) , computer science , control engineering , simulation , control (management) , nonlinear system , algorithm , artificial intelligence , temperature control , oceanography , physics , quantum mechanics , geology
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.