论文标题
基于广义Lyapunov函数的灵活步骤模型预测控制
Flexible-step Model Predictive Control based on Generalized Lyapunov Functions
论文作者
论文摘要
我们基于普遍的离散时间控制Lyapunov函数的想法，提出了一种具有松弛稳定性标准的新型非线性模型预测控制（MPC）方案。这些功能需要在有限的时间窗口中满足平均下降，而不是每个时间步骤的下降。该方案的一个功能是，它允许以计算上有吸引力的方式在每次迭代中实现灵活数量的控制输入，同时保证递归可行性和稳定性。我们的灵活步骤实施的好处也在非独立系统的应用中证明，其中一步标准的实施可能因缺乏渐近收敛而遭受。
We present a novel nonlinear model predictive control (MPC) scheme with relaxed stability criteria, based on the idea of generalized discrete-time control Lyapunov functions. These functions need to satisfy an average descent over a finite window of time, rather than a descent at every time step. One feature of this scheme is that it allows for implementing a flexible number of control inputs in each iteration, in a computationally attractive manner, while guaranteeing recursive feasibility and stability. The benefits of our flexible-step implementation are also demonstrated in an application to nonholonomic systems, where the one-step standard implementation may suffer from lack of asymptotic convergence.
