Nonlinear Model Predictive Control of Robotic Systems with Control Lyapunov Functions
This work addresses the problem of ensuring stability and optimal control in robotics, offering a practical solution for real-time applications, though it is incremental as it builds on existing theoretical frameworks.
The paper tackled the challenge of implementing a unified Nonlinear Model Predictive Control (NMPC) and Control Lyapunov Function (CLF) controller in real-time on a robotic system with limited computational resources, achieving improved performance over baseline controllers by adding a prediction horizon and simplifying design with explicit stability constraints.
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.