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Some manifold learning considerations toward explicit model predictive control
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DOI：10.1002/aic.16881.png)
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Abstract
En 中文
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.
Keywords:
data mining
diffusion maps
machine learning
model predictive control
process control
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