Robust control of vehicle multi‐target adaptive cruise based on model prediction. Issue 4 (16th November 2020)

• Record Type: Journal Article
• Authors: Zhou, Zibao; Zhu, Juping; Li, Yuansheng
• Is Part Of: Cognitive computation and systems. Volume 2:Issue 4(2020)
• Page Start: 254
• Page End: 261
• Publication Date: 2020-11-16
• Subjects: robust control -- control system synthesis -- adaptive control -- road vehicles -- optimisation -- predictive control -- vehicles -- vehicle dynamics -- feedback -- road traffic -- quadratic programming -- performance index; robust control -- vehicle multitarget adaptive cruise -- model prediction -- low utilisation -- current adaptive cruise control -- multiobjective adaptive cruise control -- model predictive control theory -- desired longitudinal acceleration -- online quadratic programming problem -- prediction error -- modelling mismatch -- control system -- error feedback correction mechanism -- vector management method -- nonfeasible solution -- hard constraints -- different work conditions -- focusing performance index -- constraint space varies -- different ACC modes -- slightly adjusting performance index -- combined work conditions -- preceding vehicle -- adaptability -- ACC system; Cognitive science -- Periodicals; Artificial intelligence -- Periodicals; Neurosciences -- Periodicals; Computer science -- Periodicals; Neurosciences; Computer science; Cognitive science; Artificial intelligence; Periodicals; Electronic journals; 006.3
• DOI: 10.1049/ccs.2020.0030
• Languages: English
• ISSNs: 2517-7567

Abstract

Abstract : On the issue of low utilisation and acceptance of current adaptive cruise control (ACC), a multi‐objective adaptive cruise control (MO‐ACC) algorithm is developed in this study. Based on model predictive control theory, comprehensively considering the coordination among various conflicting objectives, the decision of desired longitudinal acceleration is transformed into online quadratic programming (QP) problem. In order to compensate for prediction error caused by modelling mismatch, the robustness of control system is improved by introducing an error feedback correction mechanism. Meanwhile, vector management method is adopted to deal with the non‐feasible solution owing to hard constraints during the process of optimisation. Further, under different work conditions, the focusing performance index along with constraint space varies, and therefore different ACC modes are established to meet the demand of skilled driving groups by means of slightly adjusting performance index, constraint space as well as slack relaxation. The simulations show that under the combined work conditions of the preceding vehicle, the following vehicle can realise seamless switching among various working modes, and also is able to achieve the good expectation during vehicle following, which will help to enhance the adaptability of the ACC system to the complex road traffic environment.