With increasing and more stringent requirements for advanced vehicle integration, including vehicle dynamics and control, traditional control and optimization strategies may not qualify for many applications. This is because, among other factors, they do not consider the nonlinear characteristics of practical systems. Moreover, the vehicle wheel model has some inadequacies regarding the sideslip angle, road adhesion coefficient, vertical load, and velocity. In this paper, an adaptive neural wheel network is introduced, and the interaction between the lateral and vertical dynamics of the vehicle is analyzed. By means of nonlinear analyses such as the use of a bifurcation diagram and the Lyapunov exponent, the vehicle is shown to exhibit complicated motions with increasing forward speed. Furthermore, electric power steering (EPS) and active suspension system (ASS), which are based on intelligent control, are used to reduce the nonlinear effect, and a negotiation algorithm is designed to manage the interdependences and conflicts among handling stability, driving smoothness, and safety. Further, a rapid control prototype was built using the hardware-in-the-loop simulation platform dSPACE and used to conduct a real vehicle test. The results of the test were consistent with those of the simulation, thereby validating the proposed control. 1. Introduction When a car is travelling at a high speed, a slight variation of the steering could result in a crash. Moreover, steering instability is primarily caused by the lateral force acting on the steering wheels, which is particularly affected by the sideslip angle [1]. When a vehicle navigates a large radius curve, the change in the sideslip angle is small and linearly related to the change in the lateral force. However, for high lateral accelerations, the wheel characteristic is nonlinear and the lateral force varies nonlinearly, thereby reducing the steering stability [2]. Under these complex conditions and the accompanying uncertainties, there are variations in the vertical load and lateral force acting on the wheel, as well as the longitudinal force of the vehicle. It is therefore necessary to develop an effective nonlinear model and employ more accurate processes such as the use of a neural network model and the sliding mode to achieve control requirements [3–5]. Currently, vehicle dynamics is mostly studied by means of nonlinear dynamics. For example, Wu and Sheng [6] established the phase plane of the sideslip angle and the rate at which it changes, which afforded a better method for the quantitative
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