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Fast Distributed Model Predictive Control Method; Active Suspension Systems
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DOI：10.3390/s23063357.png)
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摘要
En 中文
In order to balance the per;
mance index and computational efficiency of the active suspension control system, this paper offers a fast distributed model predictive control (DMPC) method based on multi-agents;
the active suspension system. Firstly, a seven-degrees-of-freedom model of the vehicle is created. This study establishes a reduced-dimension vehicle model based on graph theory in accordance with its network topology and mutual coupling constraints. Then,;
engineering applications, a multi-agent-based distributed model predictive control method of an active suspension system is presented. The partial differential equation of rolling optimization is solved by a radical basis function (RBF) neural network. It improves the computational efficiency of the algorithm on the premise of satisfying multi-objective optimization. Finally, the joint simulation of CarSim and Matlab/Simulink shows that the control system can greatly minimize the vertical acceleration, pitch acceleration, and roll acceleration of the vehicle body. In particular, under the steering condition, it can take into account the safety, com;
t, and handling stability of the vehicle at the same time.
Keyword:
active suspension system
distributed model predictive control
multi-agent
RBF neural network
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期刊
IF:
3.5
论文数:
7.2W
被引数:
20.9W
机构
引用论文
Neural-Network Adaptive Output-Feedback Saturation Control for Uncertain Active Suspension Systems
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