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Adaptive Fuzzy Asymptotic Control of MIMO Systems With Unknown Input Coefficients Via a Robust Nussbaum Gain-Based Approach

机译:基于鲁棒Nussbaum增益的输入系数未知的MIMO系统的自适应模糊渐近控制

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摘要

This paper proposes an adaptive fuzzy asymptotic control method for multiple input multiple output (MIMO) nonlinear systems with unknown input coefficients, with a focus on handling unknown input nonlinearities and control directions. For all the existing Nussbaum gain-based approaches, it is difficult to investigate unknown input coefficients problem since multiple time-varying coefficients and disturbances coexist and should be simultaneously tackled in the stability analysis. To overcome the above difficulty, we propose a robust Nussbaum gain-based approach for the adaptive fuzzy asymptotic control of MIMO nonlinear systems. Benefiting from the proposed Nussbaum gain-based approach, bounded disturbances including unmodeled system dynamics and universal approximation errors are handled. Furthermore, the proposed approach helps extend the bounded fuzzy control result to the asymptotic convergence. Hence, both the control robustness and control accuracy are prompted within the frame of the developed Nussbaum gain approach. Finally, a simulation example is carried out to illustrate the effectiveness of the proposed control method.
机译:本文针对输入系数未知的多输入多输出(MIMO)非线性系统,提出了一种自适应模糊渐近控制方法,重点是处理未知的输入非线性和控制方向。对于所有现有的基于Nussbaum增益的方法,由于多个时变系数和扰动并存,并且在稳定性分析中应同时解决,因此很难研究未知的输入系数问题。为了克服上述困难,我们提出了一种鲁棒的基于Nussbaum增益的MIMO非线性系统自适应模糊渐近控制方法。受益于所提出的基于Nussbaum增益的方法,可处理有界干扰,包括未建模的系统动力学和通用逼近误差。此外,提出的方法有助于将有界模糊控制结果扩展到渐近收敛。因此,在开发的Nussbaum增益方法的框架内,既可以提高控制鲁棒性,又可以提高控制精度。最后,通过仿真实例说明了所提出的控制方法的有效性。

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