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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Neuroadaptive Fault-Tolerant Control Under Multiple Objective Constraints With Applications to Tire Production Systems
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Neuroadaptive Fault-Tolerant Control Under Multiple Objective Constraints With Applications to Tire Production Systems

机译：在多目标限制下具有神经视觉容错控制，用于轮胎生产系统的应用

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摘要

Many manufacturing systems not only involve nonlinearities and nonvanishing disturbances but also are subject to actuation failures and multiple yet possibly conflicting objectives, making the underlying control problem interesting and challenging. In this article, we present a neuroadaptive fault-tolerant control solution capable of addressing those factors concurrently. To cope with the multiple objective constraints, we propose a method to accommodate these multiple objectives in such a way that they are all confined in certain range, distinguishing itself from the traditional method that seeks for a common optimum (which might not even exist due to the complicated and conflicting objective requirement) for all the objective functions. By introducing a novel barrier function, we convert the system under multiple constraints into one without constraints, allowing for the nonconstrained control algorithms to be derived accordingly. The system uncertainties and the unknown actuation failures are dealt with by using the deep-rooted information-based method. Furthermore, by utilizing a transformed signal as the initial filter input, we integrate dynamic surface control (DSC) into backstepping design to eliminate the feasibility conditions completely and avoid off-line parameter optimization. It is shown that, with the proposed neuroadaptive control scheme, not only stable system operation is maintained but also each objective function is confined within the prespecified region, which could be asymmetric and time-varying. The effectiveness of the algorithm is validated via simulation on speed regulation of extruding machine in tire production lines.
机译：许多制造系统不仅涉及非线性和非凡的干扰，而且还涉及致动失败和多个但可能相互矛盾的目标，使得潜在的控制问题有趣和具有挑战性。在本文中，我们提供了一种能够同时解决这些因素的神经视觉容错控制解决方案。为了应对多种客观约束，我们提出了一种方法以适应这些多个目标，使它们全部限制在某些范围内，使其自身与寻求共同最佳的传统方法（可能甚至不存在所有客观职能的复杂和相互矛盾的客观要求。通过引入新的障碍功能，我们将系统在多个约束下转换为一个没有约束的，允许相应地导出非致命的控制算法。通过使用基于深生信息的方法处理系统不确定性和未知的致动失败。此外，通过利用变换信号作为初始滤波器输入，我们将动态表面控制（DSC）集成到BackStepping设计中以完全消除可行性条件，避免离线参数优化。结果表明，利用所提出的神经直视控制方案，不仅保持了稳定的系统操作，而且每个目标函数仅限于预先限定的区域内，这可能是不对称的和时变的。通过轮胎生产线挤出机的速度调节仿真验证了算法的有效性。

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