论文标题
具有控制Lyapunov功能的机器人系统的非线性模型预测控制
Nonlinear Model Predictive Control of Robotic Systems with Control Lyapunov Functions
论文作者
论文摘要
具有控制Lyapunov函数（CLFS）的非线性模型预测控制（NMPC）的理论统一为实现最佳控制性能提供了一个框架，同时确保稳定性保证。在本文中，我们介绍了具有有限的计算资源的机器人系统上统一的NMPC和CLF控制器的第一个实时实现。这些局限性促使一组方法有效地将CLF稳定性约束纳入一般的NMPC公式。我们评估了与基线CLF和NMPC控制器相比，在仿真和硬件中，具有机器人Segway平台的性能。与基于CLF的控制器相比，预测范围的添加提供了性能优势，该控制器在及时以最佳的位置运行。此外，明确施加的稳定性约束消除了NMPC所需的困难成本功能和参数调整的需求。因此，统一控制器改善了每个隔离控制器的性能，并简化了总体设计过程。
The theoretical unification of Nonlinear Model Predictive Control (NMPC) with Control Lyapunov Functions (CLFs) provides a framework for achieving optimal control performance while ensuring stability guarantees. In this paper we present the first real-time realization of a unified NMPC and CLF controller on a robotic system with limited computational resources. These limitations motivate a set of approaches for efficiently incorporating CLF stability constraints into a general NMPC formulation. We evaluate the performance of the proposed methods compared to baseline CLF and NMPC controllers with a robotic Segway platform both in simulation and on hardware. The addition of a prediction horizon provides a performance advantage over CLF based controllers, which operate optimally point-wise in time. Moreover, the explicitly imposed stability constraints remove the need for difficult cost function and parameter tuning required by NMPC. Therefore the unified controller improves the performance of each isolated controller and simplifies the overall design process.
