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An Adaptive RBF Neural Guidance Law Surface to Air Missile Considering Target and Control Loop Uncertainties

机译:考虑到目标和控制回路不确定性的空气导弹自适应RBF神经指导法

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A new guidance law for medium range homing missiles is developed by invoking the adaptive neural control theory. This approach is novel in that an integrated guidance-control model is derived to consider target maneuvers and missile dynamic uncertainties, also an RBF neural network is employed to adaptively compensate for the mentioned model nonlinearities. The network weights are adapted using a Lyapunov-based design. In addition an adaptive compensator is used in conjunction with the adaptive intelligent system for tolerating of the approximation errors and disturbance effects. This proposal yields a guidance law that is robust against different target maneuvers and in comparison to other introduced modern approaches is far easier to implement and need to a few numbers of inputs. Presented proof for the stability of guidance-control loop and simulated results demonstrate the effective performance of designed guidance law.
机译:通过调用自适应神经控制理论,开发了一种新的中范围归巢导弹的新指导法。这种方法是新颖的,因为导出了一个集成的引导控制模型,以考虑目标演习和导弹动态不确定性,也使用RBF神经网络来自适应地补偿所提到的模型非线性。网络权重通过基于Lyapunov的设计进行调整。另外,自适应补偿器与自适应智能系统一起使用,以容忍近似误差和干扰效果。该提案产生了对不同目标操纵的强大的指导法,与其他引入的现代方法相比,更容易实施并且需要几个投入。引导控制回路稳定性和模拟结果的证明证明了设计的指导法的有效性能。

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