Model predictive control (MPC) is a de facto standard control algorithm across the process industries. There remain, however, applications where MPC is impractical because an optimization problem is solved at each time step. We present a link between explicit MPC formulations and manifold learning to enable facilitated prediction of the MPC policy. Our method uses a similarity measure informed by control policies and system state variables, to “learn” an intrinsic parametrization of the MPC controller using a diffusion maps algorithm, which will also discover a low‐dimensional control law when it exists as a smooth, nonlinear combination of the state variables. We use function approximation algorithms to project points from state space to the intrinsic space, and from the intrinsic space to policy space. The approach is illustrated first by “learning” the intrinsic variables for MPC control of constrained linear systems, and then by designing controllers for an unstable nonlinear reactor.

中文翻译：

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对显式模型预测控制的一些综合学习考虑

模型预测控制（MPC）是整个过程工业的事实上的标准控制算法。但是，仍然存在MPC不可行的应用程序，因为在每个时间步都解决了优化问题。我们提出了明确的MPC公式与流形学习之间的联系，以促进对MPC政策的预测。我们的方法使用由控制策略和系统状态变量通知的相似性度量，以使用扩散图算法“学习” MPC控制器的固有参数，当以平滑，非线性的形式存在时，它还会发现低维控制定律。状态变量的组合。我们使用函数逼近算法将点从状态空间投影到内部空间，从内部空间投影到策略空间。