从参数化模型优化到鲁棒PID增益调度
From Parametric Model-based Optimization to robust PID Gain Scheduling
AI总结：
本文提出将MPC的约束处理和优化功能与常规PID结合，通过线性反馈增益和PID增益调度结构提升系统鲁棒性，无需额外分析。
摘要：
在化工过程应用中，模型预测控制有效处理暂态操作中的输入和状态约束。然而，工业PID控制器直接操控执行器，在小扰动鲁棒性方面起关键作用。本文考虑将常规PID与MPC的约束处理和优化功能相结合。首先，回顾MPC框架，其在无约束区域使用线性反馈增益，该增益可以是任何现有的多回路PID设计，或基于本文提出的多变量系统稳定PI或PID设计。所得到的控制器是一种前馈PID映射，是一种无需调节PID以适应最优输入的简单形式。受约束的MPC参数化解进一步利用了熟悉的PID增益调度结构。通过PID设计实现稳态鲁棒性，从而避免了额外的鲁棒性分析。
英文摘要：
In chemical process applications, model predictive control effectively deals with input and state constraints during transient operations. However, industrial PID controllers directly manipulates the actuators, so they play the key role in small perturbation robustness. This paper considers the problem of augmenting the commonplace PID with the constraint handling and optimization functionalities of MPC. First, we review the MPC framework, which employs a linear feedback gain in its unconstrained region. This linear gain can be any preexisting multiloop PID design, or based on the two stabilizing PI or PID designs for multivariable systems proposed in the paper. The resulting controller is a feedforward PID mapping, a straightforward form without the need of tuning PID to fit an optimal input. The parametrized solution of MPC under constraints further leverages a familiar PID gain scheduling structure. Steady state robustness is achieved along with the PID design so that additional robustness analysis is avoided.
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