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Real-Time Control of a Rotary Inverted Pendulum using Robust LQR-based ANFIS Controller

机译:基于鲁棒LQR的ANFIS控制器的旋转倒立摆的实时控制

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摘要

From the last five decades, inverted pendulum (IP) has been considered as a benchmark problem in the control literature due to its inherit nature of instability, non-linearity and underactuation. Its applicability in wide range of practical systems, demands the need of a robust controller. It is found in the literature that wide range of controllers had been tested on this problem, out of which the most robust being sliding mode controller while the most optimal being linear quadratic regulator (LQR) controller. The former has a problem of discontinuity and chattering, while the latter lacks the property of robustness. To address the robustness issue in LQR controller, this paper proposes a novel robust LQR-based adaptive neural based fuzzy inference system controller, which is a hybrid of LQR and fuzzy inference system. The proposed controller is designed and implemented on rotary inverted pendulum. Further, to validate the robustness of proposed controller to parametric uncertainties, pendulum mass is varied. Simulation and experimental results show that as compared to LQR controller, the proposed controller is robust to variations in pendulum mass and has shown satisfactory performance.
机译:从过去的五十年来,由于其继承的不稳定性,非线性和疏松的性质,被认为是控制文献中的基准问题。其在广泛的实用系统中的适用性要求需要强大的控制器。它在文献中发现了广泛的控制器在这个问题上进行了测试,其中最强大的滑模控制器,而最佳的线性二次调节器(LQR)控制器。前者存在不连续性和喋喋不休的问题,而后者缺乏鲁棒性的财产。为了解决LQR控制器中的稳健性问题,本文提出了一种新型鲁棒基于LQR的自适应神经基于基于神经的模糊推理系统控制器,是LQR和模糊推理系统的混合。所提出的控制器在旋转倒立摆动上设计和实施。此外,为了验证所提出的控制器对参数不确定性的鲁棒性,可以改变摆动质量。仿真和实验结果表明,与LQR控制器相比,所提出的控制器对摆锤的变化具有稳健,并且表现出令人满意的性能。

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