Intelligent bounds on modeling uncertainty: applications to sliding mode control
Intelligent bounds on modeling uncertainty: applications to sliding mode control
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DOI：
10.1109/tsmcc.2002.801350
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发表时间：
2002-05
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影响因子：
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通讯作者：
G. Buckner
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文献类型：
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作者：
G. Buckner
鲁棒控制技术，如滑模控制（SMC），需要一个动态模型的植物和边界上的建模不确定性，制定控制律与保证稳定性。虽然动态系统建模和模型参数估计的技术已经很好地建立起来，但估计不确定性界限的程序很少。在SMC设计的情况下，通常选择保守的全局边界以确保整个操作空间上的闭环稳定性。这种保守的“硬计算”方法的主要缺点是过多的控制活动和降低的性能，特别是在模型准确的操作空间区域。本文提出了一种新的估计动态系统不确定性界的方法。这种“软计算”方法使用一种独特的人工神经网络，2-Sigma网络，自适应地约束建模的不确定性。这种融合的智能不确定性界估计与传统的SMC的控制算法，是鲁棒性和自适应的结果。在磁悬浮系统上进行的仿真和实验演示证实了这些能力，并显示出出色的跟踪性能，而无需过多的控制活动。
Robust control techniques such as sliding mode control (SMC) require a dynamic model of the plant and bounds on modeling uncertainty to formulate control laws with guaranteed stability. Although techniques for modeling dynamic systems and estimating model parameters are well established, very few procedures exist for estimating uncertainty bounds. In the case of SMC design, a conservative global bound is usually chosen to ensure closed-loop stability over the entire operating space. The primary drawbacks of this conservative, "hard computing" approach are excessive control activity and reduced performance, particularly in regions of the operating space where the model is accurate. In this paper, a novel approach to estimating uncertainty bounds for dynamic systems is introduced. This "soft computing" approach uses a unique artificial neural network, the 2-Sigma network, to bound modeling uncertainty adaptively. This fusion of intelligent uncertainty bound estimation with traditional SMC results in a control algorithm that is both robust and adaptive. Simulations and experimental demonstrations conducted on a magnetic levitation system confirm these capabilities and reveal excellent tracking performance without excessive control activity.