论文标题
具有本地和全局约束的线性离散时间系统的强大自触发DMPC
Robust self-triggered DMPC for linear discrete-time systems with local and global constraints
论文作者
论文摘要
本文提出了一个强大的自触发分布式模型预测控制（DMPC）方案，该方案针对具有局部（未偶联）和全局（耦合）约束的离散时间线性系统系列。为了处理添加剂干扰，提出了基于管子的方法，以满足局部状态和控制约束。同时，给出了一种特殊的约束形式，以确保全球耦合约束。自触发机制有助于通过跳过微不足道的迭代步骤来减轻计算负担，从而确定某个采样瞬间以并行方式解决DMPC优化问题。 DMPC优化问题被构建为双重形式，并根据替代方向乘数法（ADMM）进行了分布求解，并具有一些已知的简化。显示了闭环系统的递归可行性和输入到州的稳定性，通过模拟示例证明了拟议方案的性能。
This paper proposes a robust self-triggered distributed model predictive control (DMPC) scheme for a family of Discrete-Time linear systems with local (uncoupled) and global (coupled) constraints. To handle the additive disturbance, tube-based method is proposed for the satisfaction of local state and control constraints. Meanwhile, A special form of constraints tightening is given to guarantee the global coupled constraints. The self-triggering mechanism help reduce the computation burden by skip insignificant iteration steps, which determine a certain sampling instants to solve the DMPC optimization problem in parallel ways. The DMPC optimization problem is constructed as a dual form, and solved distributedly based on the Alternative Direction Multiplier Method (ADMM) with some known simplifications. Recursive feasibility and input-to-state stability of the closed-loop system are shown, the performance of proposed scheme is demonstrated by a simulation example.
