Approximate dynamic programming-based control of distributed parameter systems

The objective of this work is to extend the approximate dynamic programming (ADP) framework to online control of distributed parameter systems. The ADP framework involves using suboptimal control policies to identify the relevant regions of the state space and to generate a cost-to-go function approximation applicable in this region. We present model-based value iteration and model-free Q-learning approaches for feedback control of an adiabatic plug flow reactor. The state dimension is reduced using appropriate model reduction and sensor placement techniques. We show that both the approaches provide better performance than the initial model predictive control and Proportional-Integral- Derivative (PID) controllers. Finally, an extension of ADP to the stochastic case with full state feedback is presented. Copyright © 2011 Curtin University of Technology and John Wiley & Sons, Ltd. Copyright © 2011 Curtin University of Technology and John Wiley & Sons, Ltd.