Integrated nonlinear robust adaptive control for active front steering and direct yaw moment control systems with uncertainty observer

Abstract

This paper presents an integrated nonlinear robust adaptive controller with uncertainty observer for active front wheel steering system and direct yaw moment control system. First, an integrated vehicle chassis control model is established as the nominal model with the additive and multiplicative uncertainties of the system. Secondly, an integrated nonlinear robust adaptive control law with the additive uncertainty observer is designed via Lyapunov stability theory to calculate the corrective yaw moment, and an adaptive law is designed based on projection correction method to online estimate and compensate the multiplicative uncertainty of the system. Then, the constrained optimal allocation problem of the corrective yaw moment is transformed into the nonlinear optimization problem, and the sequential quadratic programming method is used to solve the nonlinear optimization problem to coordinate active front wheel steering system and direct yaw moment control system. Finally, the performance of the proposed integrated nonlinear robust adaptive controller is verified via vehicle dynamics simulation software.

A single and double closed-loop compound anti-sway control method for double-pendulum bridge cranes locating at random position

“…where u 1 (i) and u 2 (i) are hook sway angle and payload sway angle in the time interval of i, respectively. The objective function is optimized by SQP method (Zhang et al, 2020). The initial values of k p1 , k d1 , k p2 , k d2 , k p3 and k d3 are 15, 10, 0.1, 0.01, 3 and 1, respectively.…”

Fractional robust finite time control of four-wheel-steering mobile robots subject to serious time-varying perturbations

“…Moreover, kinematics states in terms of longitudinal, lateral, and angular positions in the inertial frame are introduced in [15]. Second-order derivatives of kinematics states can be considered as the correspondence of the state variables  ,  and x v here [23]. In the inertial coordinate system, the absolute displacement offset is difficult to measure and not conducive to the practical implementation of the considered FMR.…”

“…By contrast, the accurate dynamic adjustment of the mobile robot chassis can be realized through dynamic control to improve the tracking performance [20][21][22]. As a widely employed control strategy, direct yaw moment control (DYMC) framework establishes the relationship between robot motion, driving force and yaw moment [23]. It has an excellent ability in stability control and tracking accuracy, which has attracted extensive attention in industrial FMR implementation.…”

Analysis of Intrinsic Mechanistic of Stability-Tracking Control for Distributed Drive Autonomous Electric Vehicle

“…In the design of the intelligent driving control system, the whole system is often divided into perception layer, decision-making and planning layer, motion control layer, execution layer and so on [23,24]. At present, in DDEVs, LSC is mainly implemented by a hierarchical mechanism, which is generally divided into three layers: vehicle motion state prediction layer, vehicle motion tracking layer, and torque distribution and executive layer [2,3,[25][26][27].…”