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Neural-network-predictor-based control for an uncertain multiple launch rocket system with actuator delay

机译：具有执行器延迟的不确定多发火箭系统的基于神经网络预测器的控制

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Development of multiple launch rocket system (MLRS) has been restricted for several decades due to the poor dispersion characteristics of rockets, which is caused by the orientation of the MLRS departing from that intended. Hence, it is vital to maintain the angles of MLRS at a desired value via a proper control strategy. In this paper, a new neural network predictive control is developed for orienting control of the MLRS with actuator delay. First, the dynamic model of motor-mechanism coupling system is established using Lagrange method and field-oriented control theory. Then, for cancelling the effects of nonlinearities and uncertainties, the concept of feedback linearization and a dynamic recurrent neural network are introduced. In addition, a modified Smith predictor is employed to maintain the desirable orienting performance in the occurrence of actuator delay. For the stability analysis, Lyapunov's method is utilized to ensure uniform ultimate boundedness of the closed-loop system. The simulated and experimental results demonstrate the effectiveness of the proposed controller.
机译：多发火箭系统（MLRS）的开发由于火箭的分散特性差而受到了几十年的限制，这是由于MLRS的方向与预期不符而导致的。因此，至关重要的是通过适当的控制策略将MLRS的角度保持在所需值。在本文中，开发了一种新的神经网络预测控制，用于定向带有致动器延迟的MLRS​​的控制。首先，利用拉格朗日法和磁场定向控制理论建立了电机－机械耦合系统的动力学模型。然后，为了消除非线性和不确定性的影响，引入了反馈线性化的概念和动态递归神经网络。另外，在致动器延迟的发生中，使用改进的史密斯预测器来维持期望的定向性能。为了进行稳定性分析，利雅普诺夫方法被用来确保闭环系统的一致最终有界性。仿真和实验结果证明了该控制器的有效性。

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