Driver characteristics have been the research focus for automotive control. Study on identification of driver characteristics is provided in this paper in terms of its relevant research directions and key technologies involved. This paper discusses the driver characteristics based on driver’s operation behavior, or the driver behavior characteristics. Following the presentation of the fundamental of the driver behavior characteristics, the key technologies of the driver behavior characteristics are reviewed in detail, including classification and identification methods of the driver behavior characteristics, experimental design and data acquisition, and model adaptation. Moreover, this paper discusses applications of the identification of the driver behavior characteristics which has been applied to the intelligent driver advisory system, the driver safety warning system, and the vehicle dynamics control system. At last, some ideas about the future work are concluded.

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This paper explores the iterative learning control (ILC) problem for two‐dimensional (2D) multi‐input multi‐output nonlinear parametric systems by taking all nonrepetitive uncertainties of stochastic initial shifts, different tracking tasks and nonuniform trial lengths into consideration. A 2D stochastic variable is defined with Bernoulli stochastic distribution for the first time to handle iteration‐varying trial lengths. The desired output is incorporated into the learning control law as a feedback to compensate the iterative changes of the tracking tasks. An iterative 2D parameter updating law is established using a new defined virtue tracking error to well address the systems uncertainties and varying trial lengths. Consequently, a novel 2D adaptive ILC (2D‐AILC) is presented by incorporating the control law and parameter updating law. The convergence is proved by introducing both the Lyapunov stability principle and the 2D key technique lemma into the repetitive 2D systems even though the dynamic evolution along with two‐dimensional directions makes it more difficult in the mathematic analysis. The simulation study tests the theoretical results: the presented 2D‐AILC scheme can still accomplish a tracking task exactly even though there exist random initial states, nonrepetitive reference trajectories, and iteration‐varying trial lengths.

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In this paper, an iterative learning recursive least squares (ILRLS) identification method is developed by considering a class of repetitive systems. First, considering a repetitive discrete-time system corrupted by white noise, we present a linear time-varying data model to describe the input-output dynamic behavior of the system in iteration domain. On this basis, two ILRLS methods are proposed taking both white noises and colored noises into consideration. With an extensive analysis, the two proposed methods are shown applicable to repetitive nonlinear discrete-time systems owing to their data-driven nature by which no explicit models are required. The proposed ILRLS methods are executed pointwisely along the iteration direction, and they can also deal with time-varying uncertainties. The results are proved and verified by mathematical analysis along with simulations. INDEX TERMS System identification, iterative learning recursive least squares, linear time-varying data model, repetitive discrete-time systems, data-driven approach.

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