论文标题
通过事件触发的模型更新，基于安全学习的反馈线性化跟踪跟踪控制控制
Safe Learning-Based Feedback Linearization Tracking Control for Nonlinear System with Event-Triggered Model Update
论文作者
论文摘要
基于学习的方法在处理复杂方案方面具有强大的功能。但是，在不确定的环境下使用基于学习的方法仍然具有挑战性，而系统的稳定性，安全性和实时性能则需要保证。在本文中，我们根据反馈线性化控制器提出了一种基于学习的跟踪控制方案，其中使用高斯流程（GPS）在线近似不确定的干扰。使用GPS给出的障碍的预测分布，应用对照Lyapunov函数（CLF）和基于控制屏障功能（CBF）二次程序，并保证了概率稳定性和安全性。此外，首先通过模型预测控制（MPC）基于线性化的动力学系统来优化轨迹，以进一步减少跟踪误差。我们还为GPS更新设计了一个事件触发因素，以提高效率，同时保证系统的稳定性和安全性。在数值模拟中说明了所提出的跟踪控制策略的有效性。
Learning-based methods are powerful in handling complex scenarios. However, it is still challenging to use learning-based methods under uncertain environments while stability, safety, and real-time performance of the system are desired to guarantee. In this paper, we propose a learning-based tracking control scheme based on a feedback linearization controller in which uncertain disturbances are approximated online using Gaussian Processes (GPs). Using the predicted distribution of disturbances given by GPs, a Control Lyapunov Function (CLF) and Control Barrier Function (CBF) based Quadratic Program is applied, with which probabilistic stability and safety are guaranteed. In addition, the trajectory is optimized first by Model Predictive Control (MPC) based on the linearized dynamics systems to further reduce the tracking error. We also design an event trigger for GPs updates to improve efficiency while stability and safety of the system are still guaranteed. The effectiveness of the proposed tracking control strategy is illustrated in numerical simulations.
