کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4976894 1451837 2018 20 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Vibration control of uncertain multiple launch rocket system using radial basis function neural network
ترجمه فارسی عنوان
کنترل لرزش چندین موشک پرتاب موشک غیرمستقیم با استفاده از شبکه عصبی با عملکرد شعاعی
کلمات کلیدی
کنترل لرزش، شبکه عصبی، سیستم مکانیکی مکانیکی، برآوردگر عدم اطمینان، سیستم موشک چند موشک، کنترل گشتاور محاسبه شده،
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر پردازش سیگنال
چکیده انگلیسی


- The derivation of dynamic equation for a motor-mechanism coupling MLRS.
- The development of a computed torque controller, where a RBFNN based estimator is used to adapt the uncertainties of the system.
- The application of the proposed controller to control the vibration of the MLRS.

Poor dispersion characteristics of rockets due to the vibration of Multiple Launch Rocket System (MLRS) have always restricted the MLRS development for several decades. Vibration control is a key technique to improve the dispersion characteristics of rockets. For a mechanical system such as MLRS, the major difficulty in designing an appropriate control strategy that can achieve the desired vibration control performance is to guarantee the robustness and stability of the control system under the occurrence of uncertainties and nonlinearities. To approach this problem, a computed torque controller integrated with a radial basis function neural network is proposed to achieve the high-precision vibration control for MLRS. In this paper, the vibration response of a computed torque controlled MLRS is described. The azimuth and elevation mechanisms of the MLRS are driven by permanent magnet synchronous motors and supposed to be rigid. First, the dynamic model of motor-mechanism coupling system is established using Lagrange method and field-oriented control theory. Then, in order to deal with the nonlinearities, a computed torque controller is designed to control the vibration of the MLRS when it is firing a salvo of rockets. Furthermore, to compensate for the lumped uncertainty due to parametric variations and un-modeled dynamics in the design of the computed torque controller, a radial basis function neural network estimator is developed to adapt the uncertainty based on Lyapunov stability theory. Finally, the simulated results demonstrate the effectiveness of the proposed control system and show that the proposed controller is robust with regard to the uncertainty.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Mechanical Systems and Signal Processing - Volume 98, 1 January 2018, Pages 702-721
نویسندگان
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