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.

中文翻译：

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多目标约束下的神经自适应容错控制及其在轮胎生产系统中的应用


许多制造系统不仅涉及非线性和非零扰动，而且还容易出现驱动故障和多个但可能相互冲突的目标，这使得潜在的控制问题变得有趣且具有挑战性。在本文中，我们提出了一种能够同时解决这些因素的神经自适应容错控制解决方案。为了应对多个目标约束，我们提出了一种方法来容纳这些多个目标，将它们都限制在一定的范围内，这与寻求共同最优的传统方法不同（这种方法甚至可能不存在，因为所有目标函数的复杂且相互冲突的目标要求）。通过引入一种新颖的障碍函数，我们将多重约束下的系统转换为无约束的系统，从而可以相应地导出无约束控制算法。采用深层次的信息化方法处理系统的不确定性和未知的驱动故障。此外，通过利用变换后的信号作为初始滤波器输入，我们将动态表面控制（DSC）集成到反步设计中，以完全消除可行性条件并避免离线参数优化。结果表明，采用所提出的神经自适应控制方案，不仅可以维持稳定的系统运行，而且每个目标函数都被限制在预先指定的区域内，该区域可以是不对称的和时变的。通过对轮胎生产线挤出机调速的仿真验证了算法的有效性。