论文标题
用于模型预测控制的快速梯度方法，具有输入速率和振幅约束
Fast Gradient Method for Model Predictive Control with Input Rate and Amplitude Constraints
论文作者
论文摘要
本文涉及在输入上具有速率和振幅限制的动态系统模型预测控制（MPC）问题的计算效率。我们建议使用快速梯度方法（FGM）来解决此问题，而不是增加基础有限摩尼的最佳控制问题的决策变量，其中使用Dykstra的算法解决了投影步骤。我们表明，相对于方法乘数的交替方向（ADMM），这种方法在减少了记忆使用量的同时大大减少了计算时间。我们的算法在C中实现，其性能使用多个示例证明。
This paper is concerned with the computing efficiency of model predictive control (MPC) problems for dynamical systems with both rate and amplitude constraints on the inputs. Instead of augmenting the decision variables of the underlying finite-horizon optimal control problem to accommodate the input rate constraints, we propose to solve this problem using the fast gradient method (FGM), where the projection step is solved using Dykstra's algorithm. We show that, relative to the Alternating Direction of Method Multipliers (ADMM), this approach greatly reduces the computation time while halving the memory usage. Our algorithm is implemented in C and its performance demonstrated using several examples.
