Abstract
The concept of vehicle automation is a promising approach to achieve sustainable transport systems, especially in an urban context. Automation requires the integration of learning-based approaches and methods in control theory. Through the integration, a high amount of information in automation can be incorporated. Thus, a sustainable operation, i.e., energy-efficient and safe motion with automated vehicles, can be achieved. Despite the advantages of integration with learning-based approaches, enhanced vehicle automation poses crucial safety challenges. In this paper, a novel closed-loop matching method for control-oriented purposes in the context of vehicle control systems is presented. The goal of the method is to match the nonlinear vehicle dynamics to the dynamics of a linear system in a predefined structure; thus, a control-oriented model is obtained. The matching is achieved by an additional control input from a neural network, which is designed based on the input–output signals of the nonlinear vehicle system. In this paper, the process of closed-loop matching, i.e., the dataset generation, the training, and the evaluation of the neural network, is proposed. The evaluation process of the neural network through data-driven reachability analysis and statistical performance analysis methods is carried out. The proposed method is applied to achieve the path following functionality, in which the nonlinearities of the lateral vehicle dynamics are handled. The effectiveness of the closed-loop matching and the designed control functionality through high fidelity CarMaker simulations is illustrated.
Highlights
It was shown that the neural network-based matching method is able to modify the dynamics of the nonlinear system by computing an additional steering angle
The additional steering angle is computed by the neural network, which means that the goal is to evaluate the matching performance of the closed-loop
The whole simulation is made in CarMaker vehicle dynamics simulation software, in which the vehicle model is charged with nonlinearities
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. The reachability sets are computed for the controlled vehicle, using data-driven analyses and a machine learning algorithm, and the results are used in a model predictive approach, which guarantees the trajectory tracking [17]. In [18], a feedback linearization method is presented for the tracking performance based on neural networks. A novel model matching-based control approach is presented, using a machine learning–based algorithm. The nominal model of the system is considered to be known since the dynamics of the original system is adjusted to the nominal model In this way, the complexity of the control design process is reduced and the performance of the closed-loop system can be guaranteed without taking into account the uncertainties and the unmodeled dynamics of the system.
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