| Issue |
Korean Journal of Chemical Engineering,
Vol.21, No.2, 338-344, 2004
Vol.21, No.2, 338-344, 2004
Simulation-Based Learning of Cost-To-Go for Control of Nonlinear Processes
In this paper, we present a simulation-based dynamic programming method that learns the ‘cost-to-go’ function in an iterative manner. The method is intended to combat two important drawbacks of the conventional Model Predictive Control (MPC) formulation, which are the potentially exorbitant online computational requirement and the inability to consider the future interplay between uncertainty and estimation in the optimal control calculation. We use a nonlinear Van de Vusse reactor to investigate the efficacy of the proposed approach and identify further research issues.
Keywords:
Nonlinear Model Predictive Control; Dynamic Programming; Stochastic Optimal Control; Reinforcement Learning; Neuro-dynamic Programming; Function Approximation
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