论文标题
深入增强学习的基于学习的滑动模式控制设计，用于部分知名的非线性系统
A Deep Reinforcement Learning-based Sliding Mode Control Design for Partially-known Nonlinear Systems
论文作者
论文摘要
模型不确定性的存在为基于模型的控制设计带来了挑战，并且在应对非线性系统时，控制设计的复杂性进一步加剧。本文通过混合基于数据驱动的方法和基于模型的方法，为非线性系统提供了滑动模式控制（SMC）设计方法。首先，为非线性系统的可用（标称）模型设计了SMC。可用模型的闭环状态轨迹用于为部分已知的非线性系统状态构建所需的轨迹。接下来，使用深层策略梯度方法来应对系统动力学的未知部分，并调整滑动模式控制输出以实现所需的状态轨迹。最终通过数值示例检查了所提出的设计方法的性能（和生存能力）。
Presence of model uncertainties creates challenges for model-based control design, and complexity of the control design is further exacerbated when coping with nonlinear systems. This paper presents a sliding mode control (SMC) design approach for nonlinear systems with partially known dynamics by blending data-driven and model-based approaches. First, an SMC is designed for the available (nominal) model of the nonlinear system. The closed-loop state trajectory of the available model is used to build the desired trajectory for the partially known nonlinear system states. Next, a deep policy gradient method is used to cope with unknown parts of the system dynamics and adjust the sliding mode control output to achieve a desired state trajectory. The performance (and viability) of the proposed design approach is finally examined through numerical examples.
