论文标题
设计具有稳定性的非线性系统的系统级合成控制器
Designing System Level Synthesis Controllers for Nonlinear Systems with Stability Guarantees
论文作者
论文摘要
我们通过使用多项式近似动力学并应用系统级合成控制器来控制具有非线性动力学和完全致动的系统的方法。我们在不需要Lyapunov功能的情况下使用神经网络来展示如何使用神经网络对此类别的控制器进行优化。我们为域的使用范围提供了范围，该域使用了一类控制器的稳定性，并在优化的控制器产生的控制成本上给出了界限。然后，我们从数值上验证了我们的方法，并与反馈线性化相比显示出改进的性能 - 这表明SLS控制器能够利用动力学中的非线性，同时保证稳定性。
We introduce a method for controlling systems with nonlinear dynamics and full actuation by approximating the dynamics with polynomials and applying a system level synthesis controller. We show how to optimize over this class of controllers using a neural network while maintaining stability guarantees, without requiring a Lyapunov function. We give bounds for the domain over which the use of the class of controllers preserves stability and gives bounds on the control costs incurred by optimized controllers. We then numerically validate our approach and show improved performance compared with feedback linearization -- suggesting that the SLS controllers are able to take advantage of nonlinearities in the dynamics while guaranteeing stability.
