Dynamic Neural Network-Based Feedback Linearization Control of Antilock Braking Systems Incorporated with Active Suspensions
Dynamic Neural Network-Based Feedback Linearization Control of Antilock Braking Systems Incorporated with Active Suspensions
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基于动态神经网络的主动悬架防抱死制动系统反馈线性化控制
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
J. Pedro
中科院分区：
文献类型：
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作者：
Nikhil Ranchod;J. Pedro
提出了一种基于非线性动态神经网络的反馈线性化（DNNFBL）控制方案，用于汽车防抱死制动系统（ABS）和半车主动悬架系统（AVSS）。从第一性原理出发，建立了ABS/AVSS集成系统的高度非线性数学模型。采用拟牛顿算法对动态神经网络进行离线训练。仿真结果表明，DNNFBL控制器具有更快的响应时间，更大的功率被施加到执行器，并减少制动时间和距离相比，PID和PDF控制系统。
The paper presents the design of a nonlinear dynamic neural network-based feedback linearisation (DNNFBL) control scheme for an antilock braking system (ABS) incorporated with a half-car active vehicle suspension system (AVSS). Highly nonlinear mathematical model for the integrated ABS/AVSS was derived from first principles. The dynamic neural networks were trained offline using the quasi-Newton algorithm. Simulation results reveal that the DNNFBL controller exhibits faster response times, greater power being applied to the actuators, and reduced braking time and distance in comparison to the PID and PDF-controlled systems.