Abstract
Differential braking and active steering have already been integrated to overcome their shortcomings. However, existing research mainly focuses on two-axle vehicles and controllers are mostly designed to use one control method to improve the other. Moreover, many experiments are needed to improve the robustness; therefore, these control methods are underutilized. This paper proposes an integrated control system specially designed for multi-axle vehicles, in which the desired lateral force and yaw moment of vehicles are determined by the sliding mode control algorithm. The output of the sliding mode control is distributed to the suitable wheels based on the abilities and potentials of the two control methods. Moreover, in this method, fewer experiments are needed, and the robustness and simultaneity are both guaranteed. To simplify the optimization system and to improve the computation speed, seven simple optimization subsystems are designed for the determination of control outputs on each wheel. The simulation results show that the proposed controller obviously enhances the stability of multi-axle trucks. The system improves 68% of the safe velocity, and its performance is much better than both differential braking and active steering. This research proposes an integrated control system that can simultaneously invoke differential braking and active steering of multi-axle vehicles to fully utilize the abilities and potentials of the two control methods.
Highlights
Rollover and lateral instability are two common issues of heavy-duty trucks, which can result in fatal accidents and massive losses
Differential braking (DB) systems can slow down the vehicle and make the driver feel safe under high-speed conditions
This paper proposes a novel integrated control system designed for a four-axle truck with fewer experiments
Summary
Rollover and lateral instability are two common issues of heavy-duty trucks, which can result in fatal accidents and massive losses. The phase plane analysis is widely discussed and applied for vehicle stability analysis and control [19,20,21] Empirical methods such as setting gains [22, 23] and fuzzy logic [18, 24] are employed in integrated control methods. They are distributed on the basis of the DB and AS analysis results and transferred by optimization systems to steering angles and braking forces to each wheel.
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