基于ReLU-ANN的MILP表示的约束反馈线性化控制
On the constrained feedback linearization control based on the MILP representation of a ReLU-ANN
AI总结：
本文研究利用ReLU人工神经网络近似反馈线性化中的复杂约束，转化为混合整数线性约束，集成于稳定控制架构，确保模型预测控制中的约束满足，并通过仿真验证。
摘要：
在这项工作中，我们探讨了整流线性单元人工神经网络在解决由反馈线性化映射产生的复杂约束这一棘手挑战中的有效性。我们的方法涉及一个全面的流程，包括通过回归过程近似约束。随后，我们将这些约束转化为混合整数线性约束的等价表示，并将其无缝集成到其他稳定控制架构中。其优势在于与线性控制设计的兼容性以及在模型预测控制设置中的约束满足性，即使对于预测轨迹也是如此。提供了仿真以验证所提出的约束重构。
英文摘要：
In this work, we explore the efficacy of rectified linear unit artificial neural networks in addressing the intricate challenges of convoluted constraints arising from feedback linearization mapping. Our approach involves a comprehensive procedure, encompassing the approximation of constraints through a regression process. Subsequently, we transform these constraints into an equivalent representation of mixed-integer linear constraints, seamlessly integrating them into other stabilizing control architectures. The advantage resides in the compatibility with the linear control design and the constraint satisfaction in the model predictive control setup, even for forecasted trajectories. Simulations are provided to validate the proposed constraint reformulation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
