Approximate dynamic programming-based approaches for input-output data-driven control of nonlinear processes

Jong Min Lee

doi:10.1016/j.automatica.2005.02.006

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Approximate dynamic programming-based approaches for input-output data-driven control of nonlinear processes

2005-07-01

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Jong Min Lee

Jay H. Lee

DOI：10.1016/j.automatica.2005.02.006

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Abstract

En 中文

We propose two approximate dynamic programming (ADP)-based strategies for control of nonlinear processes using input-output data. In the first strategy, which we term 'J-Iearning,' one builds an empirical nonlinear model using closed-loop test data and performs dynamic programming with it to derive an improved control policy. In the second strategy, called 'Q-learning,' one tries to learn an improved control policy in a model-less manner. Compared to the conventional model predictive control approach, the new approach offers some practical advantages in using nonlinear empirical models for process control. Besides the potential reduction in the on-line computational burden, it offers a convenient way to control the degree of model extrapolation in the calculation of optimal control moves. One major difficulty associated with using an empirical model within the multi-step predictive control setting is that the model can be excessively extrapolated into regions of the state space where identification data were scarce or nonexistent, leading to performances far worse than predicted by the model. Within the proposed ADP-based strategies, this problem is handled by imposing a penalty term designed on the basis of local data distribution. A CSTR example is provided to illustrate the proposed approaches. (c) 2005 Elsevier Ltd. All rights reserved.

Keywords:

nonlinear model identification

nonlinear model predictive control

approximate dynamic programming

NARX model

reinforcement learning

Q-learning