The physical modeling-based approaches tend to be over-simplistic and cannot forecast the complex dynamical phenomena, thus leading to non-negligible errors. It is not easy to measure some parameters precisely, and they are usually approximated roughly. However, this approximation reduces the modeling accuracy of the physical model, which is a common problem in complex systems research. It is well-known that neural networks are capable of encoding dynamic information. The vehicle can be accurately modeled by collecting data during its motion. However, purely data-driven approaches have low interpretability and cannot be used in commercial applications. In this work, we present a new hybrid modeling architecture. Based on the physical model, the deep learning method is introduced to expand the incomplete dynamics described by differential equations. Compared with the physical modeling-based and purely data-driven approaches, the proposed technique has lower modeling error and higher interpretability. We evaluate the performance of the hybrid model based on the collected data. The test results show that the proposed architecture successfully captures the vehicle dynamics and reduces the error caused by multi-step prediction compared to the data-driven models. The results also show that the proposed method has value for significant research and practical application.
In this paper, the authors investigate the issue of constructing and incorporating an accurate vehicle dynamic model for model predictive control (MPC) with an application to autonomous vehicle path following. To achieve the desired performance, MPC employs a precise dynamic model. However, the uncertainty of complex systems and their operating environments presents a challenge to the development of an adequately accurate vehicle dynamic model. This paper proposes a Deep Gaussian Process Regression (DGPR) method to improve model precision. Meanwhile, the learning model is incorporated into a novel MPC framework to enhance closed‐loop performance. High‐fidelity simulations using CarSim‐MATLAB demonstrate the validity of the proposed approach in terms of enhancing the path following performance and lateral stability under the condition of large curvature at medium to high speeds on roads with different friction coefficients when compared to the nominal MPC approach.
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