论文标题
投入受限的MPC的参考调查员以较低的计算成本执行状态约束
Reference Governor for Input-Constrained MPC to Enforce State Constraints at Lower Computational Cost
论文作者
论文摘要
在本文中，基于输入约束模型预测控制器（MPC）开发了控制方案，以及将参考命令修改为执行约束的想法，通常是参考州长（RG）。所提出的方案（称为RGMPC）需要对MPC进行优化，并具有快速算法存在的输入约束，并且可以处理（可能是非线性）状态和输入约束。给出了确保RGMPC方案的递归可行性以及修改后命令与所需参考命令的有限时间收敛的递归可行性。与状态和输入约束的MPC相比，带有线性和非线性约束的航天器会合操作的仿真结果表明，RGMPC方案的平均计算时间较低。
In this paper, a control scheme is developed based on an input constrained Model Predictive Controller (MPC) and the idea of modifying the reference command to enforce constraints, usual of Reference Governors (RG). The proposed scheme, referred to as the RGMPC, requires optimization for MPC with input constraints for which fast algorithms exist, and can handle (possibly nonlinear) state and input constraints. Conditions are given that ensure recursive feasibility of the RGMPC scheme and finite-time convergence of the modified command to the the desired reference command. Simulation results for a spacecraft rendezvous maneuver with linear and nonlinear constraints demonstrate that the RGMPC scheme has lower average computational time as compared to state and input constrained MPC with similar performance.
