Model-Free Predictive Control of Nonlinear Processes Based on Reinforcement Learning
Hitesh Shah,Madan Gopal +1 more
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TLDR
In this paper, a model-free predictive control (MFPC) framework is proposed to take care of both the issues of conventional MPC and the excessive computational burden associated with the control optimization.About:
This article is published in IFAC-PapersOnLine.The article was published on 2016-01-01 and is currently open access. It has received 28 citations till now. The article focuses on the topics: Model predictive control & Reinforcement learning.read more
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Reinforcement learning for batch bioprocess optimization
Panagiotis Petsagkourakis,Panagiotis Petsagkourakis,Ilya O. Sandoval,Eric Bradford,Dongda Zhang,Dongda Zhang,E.A. del Rio-Chanona +6 more
TL;DR: This work applied the Policy Gradient method from batch-to-batch to update a control policy parametrized by a recurrent neural network to address the challenges faced by batch processes.
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Reinforcement Learning for Batch Bioprocess Optimization.
Panagiotis Petsagkourakis,Ilya O. Sandoval,Eric Bradford,Dongda Zhang,Ehecatl Antonio del Rio Chanona +4 more
TL;DR: In this article, a Reinforcement Learning based optimization strategy for batch processes is proposed to address the problem of mismatch between plant-model mismatch and batch-to-batch control policy.
Journal ArticleDOI
Real-time optimization using reinforcement learning
TL;DR: This work introduces a novel methodology for real-time optimization of process systems using reinforcement learning (RL), where optimal decisions in response to external stimuli become embedded into a neural network in contrast to the conventional RTO methodology, where a process model is solved repeatedly for optimality.
Journal ArticleDOI
Data-based predictive control via direct weight optimization
TL;DR: A novel data-based predictive control scheme in which the prediction model is obtained from a linear combination of past system trajectories and the proposed controller optimizes the weights of this linear combination taking into account simultaneously performance and the variance of the estimation error.
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Chance Constrained Policy Optimization for Process Control and Optimization
Panagiotis Petsagkourakis,Ilya O. Sandoval,Eric Bradford,Federico Galvanin,Dongda Zhang,Ehecatl Antonio del Rio-Chanona +5 more
TL;DR: A chance constrained policy optimization (CCPO) algorithm which guarantees the satisfaction of joint chance constraints with a high probability - which is crucial for safety critical tasks.
References
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Journal ArticleDOI
Learning from delayed rewards
TL;DR: The invention relates to a circuit for use in a receiver which can receive two-tone/stereo signals which is intended to make a choice between mono or stereo reproduction of signal A or of signal B and vice versa.
Journal ArticleDOI
Model predictive control: past, present and future
Manfred Morari,Jay H. Lee +1 more
TL;DR: In this article, a theoretical basis for model predictive control (MPC) has started to emerge and many practical problems like control objective prioritization and symptom-aided diagnosis can be integrated into the MPC framework by expanding the problem formulation to include integer variables yielding a mixed-integer quadratic or linear program.
Journal ArticleDOI
Fuzzy inference system learning by reinforcement methods
TL;DR: Fuzzy Actor-Critic Learning (FACL) and Fuzzy Q-Learning are reinforcement learning methods based on dynamic programming (DP) principles and the genericity of these methods allows them to learn every kind of reinforcement learning problem.
Journal ArticleDOI
Online tuning of fuzzy inference systems using dynamic fuzzy Q-learning
Meng Joo Er,Chang Deng +1 more
TL;DR: A dynamic fuzzy Q-learning method that is capable of tuning fuzzy inference systems (FIS) online and a novel online self-organizing learning algorithm is developed so that structure and parameters identification are accomplished automatically and simultaneously based only on Q- learning.