The invention discloses an active suspension neural network adaptive control method and system, and the method comprises the steps: constructing a multi-layer bionic reference model of an active suspension, and carrying out the calculation based on the bionic reference model, and obtaining an expected path of the stroke of the active suspension; tracking errors of the suspension stroke are introduced, system uncertainty factors are approximated through a radial basis function neural network, an active suspension controller is designed, and the controller is used for controlling the stroke of the active suspension to accurately track the stroke expected track output by the bionic reference model. The control method does not depend on any linearization operation, an original nonlinear model is not limited, a nonlinear closed-loop system is converted into an all-wheel-drive system, and asymptotic convergence of the system state is guaranteed.
本发明公开了一种主动式悬架神经网络自适应控制方法及系统,包括:构建主动式悬架多层仿生参考模型,基于所述仿生参考模型计算得到主动式悬架行程的期望轨迹;引入悬架行程的跟踪误差,并通过径向基神经网络逼近系统不确定性因素,设计主动式悬架控制器,通过所述控制器控制主动式悬架的行程精确跟踪仿生参考模型输出的行程期望轨迹。本发明控制方法不依赖任何线性化操作,不对原非线性模型做任何限制,将非线性闭环系统转化为全驱系统,保证了系统状态的渐近收敛。
Active suspension neural network adaptive control method and system
一种主动式悬架神经网络自适应控制方法及系统
2025-01-17
Patent
Electronic Resource
Chinese
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