Between MDPs and semi-MDPs: a framework for temporal abstraction in reinforcement learning
Artificial Intelligence
Application of neural networks to chemical process control
Computers and Industrial Engineering
PH and Pion Control in Process and Waste Streams
PH and Pion Control in Process and Waste Streams
Intelligent pH Control Using Fuzzy Linear Invariant Clustering
SSST '96 Proceedings of the 28th Southeastern Symposium on System Theory (SSST '96)
Roles of Macro-actions in Accelerating Reinforcement Learning TITLE2:
Roles of Macro-actions in Accelerating Reinforcement Learning TITLE2:
Hierarchical control and learning for markov decision processes
Hierarchical control and learning for markov decision processes
Temporal abstraction in reinforcement learning
Temporal abstraction in reinforcement learning
Learning to control at multiple time scales
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Brief MIMO fuzzy internal model control
Automatica (Journal of IFAC)
Application of reinforcement learning to autonomous heading control for bionic underwater robots
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
Model-free control based on reinforcement learning for a wastewater treatment problem
Applied Soft Computing
Reinforcement learning techniques for the control of wastewater treatment plants
IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation: new challenges on bioinspired applications - Volume Part II
Voting in multi-agent system for improvement of partial observations
KES-AMSTA'11 Proceedings of the 5th KES international conference on Agent and multi-agent systems: technologies and applications
| Hi-index | 0.00 |
The pH process dynamic often exhibits severe nonlinear and time-varying behavior and therefore cannot be adequately controlled with a conventional PI control. This article discusses an alternative approach to pH process control using model-free learning control (MFLC), which is based on reinforcement learning algorithms. The MFLC control technique is proposed because this algorithm gives a general solution for acid-base systems, yet is simple enough to be implemented in existing control hardware without a model. Reinforcement learning is selected because it is a learning technique based on interaction with a dynamic system or process for which a goal-seeking control task must be performed. This ''on-the-fly'' learning is suitable for time varying or nonlinear processes for which the development of a model is too costly, time consuming or even not feasible. Results obtained in a laboratory plant show that MFLC gives good performance for pH process control. Also, control actions generated by MFLC are much smoother than conventional PID controller.