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首页> 外文期刊>International Journal of Advanced Robotic Systems >Tracking error constrained robust adaptive neural prescribed performance control for flexible hypersonic flight vehicle
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Tracking error constrained robust adaptive neural prescribed performance control for flexible hypersonic flight vehicle

机译：跟踪误差约束柔性超音速飞行车辆的鲁棒自适应神经规定性能控制

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摘要

A robust adaptive neural control scheme based on a back-stepping technique is developed for the longitudinal dynamics of a flexible hypersonic flight vehicle, which is able to ensure the state tracking error being confined in the prescribed bounds, in spite of the existing model uncertainties and actuator constraints. Minimal learning parameter technique-based neural networks are used to estimate the model uncertainties; thus, the amount of online updated parameters is largely lessened, and the prior information of the aerodynamic parameters is dispensable. With the utilization of an assistant compensation system, the problem of actuator constraint is overcome. By combining the prescribed performance function and sliding mode differentiator into the neural back-stepping control design procedure, a composite state tracking error constrained adaptive neural control approach is presented, and a new type of adaptive law is constructed. As compared with other adaptive neural control designs for hypersonic flight vehicle, the proposed composite control scheme exhibits not only low-computation property but also strong robustness. Finally, two comparative simulations are performed to demonstrate the robustness of this neural prescribed performance controller.
机译：为柔性超声波飞行车辆的纵向动态开发了一种基于背级步进技术的鲁棒自适应神经控制方案，其能够确保现有的模型不确定性和现有的模型不确定性执行器限制。最小的学习参数技术的神经网络用于估计模型不确定性;因此，在线更新的参数的量大大降低，并且可以分配空气动力学参数的先前信息。利用辅助补偿系统的利用率，克服了执行器约束的问题。通过将规定的性能函数和滑模差分与神经背部步进控制设计过程组合，提出了一种复合状态跟踪误差约束自适应神经控制方法，构建了一种新型的自适应法。与其他自适应神经控制设计相比，所提出的复合控制方案不仅表现出低计算性能，而且具有强大的鲁棒性。最后，进行了两个比较模拟以证明这种神经规定的性能控制器的稳健性。

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