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Development of Non-cooperative Distributed Adaptive MPC Schemes based on ARX Models parameterized using Orthonormal Basis Filters

机译：基于使用正交基础滤波器参数化的ARX模型的非协同分布式MPC方案的开发

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Adaptive model predictive control (AMPC) schemes can prove beneficial in the control of non-linear time varying systems due to their ability to maintain closed-loop performance in the face of large transitions in operating conditions. In this work, two non-cooperative distributed adaptive MPC (dAMPC) schemes are proposed that are based on ARX models parameterized using generalized orthonormal basis filters (GOBF). Poles of the GOBF models estimated through offline estimation are kept fixed and only the Fourier coefficients are updated recursively online thereby avoiding drift in the pole locations. The system under consideration is decomposed into a number of subsystems coupled through the inputs. A separate local AMPC controller is implemented for each subsystem. Each local controller optimizes its input trajectories assuming the input trajectories of its neighbors fixed to the last computed values, and then shares the computed inputs with the neighbors. We consider both sequential and iterative distributed control strategies for the dAMPC development. The efficacy of the proposed dAMPC schemes is demonstrated using simulation studies on an octuple tank process. When the system is subjected to large transitions in operating points, the proposed dAMPC schemes generate closed loop performances comparable to a centralized adaptive MPC scheme while significantly reducing the average computation time.
机译：自适应模型预测控制（AMPC）方案可以在控制非线性时间变化系统的控制方案中有益，因为它们在面对操作条件的大转换中保持闭环性能的能力。在这项工作中，提出了两个非协作分布式的自适应MPC（DAMPC）方案，其基于使用广义正式基础滤波器（GOBF）参数化的ARX模型。通过离线估计估计的GOBF模型的极点保持固定，并且仅在网上递归地更新傅里叶系数，从而避免杆位置的漂移。正在考虑的系统被分解为通过输入耦合的许多子系统。为每个子系统实现单独的本地AMPC控制器。每个本地控制器都优化其输入轨迹，假设其邻居的输入轨迹固定到最后计算值，然后与邻居共享计算的输入。我们考虑了DAMPC开发的顺序和迭代分布式控制策略。使用仿真研究表明了所提出的DAMPC方案的功效，在八层罐过程中进行了模拟研究。当系统在操作点中进​​行大转换时，所提出的DAMPC方案产生与集中式自适应MPC方案相当的闭环性能，同时显着降低平均计算时间。

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