Model-Free Predictive Control of Nonlinear Processes Based on Reinforcement Learning

Controlling Nonlinear Processes, using Laguerre Functions Based Adaptive Model Predictive Control (AMPC) Algorithm

Laguerre function has many advantages such as good approximation capability for different systems, low computational complexity and the facility of on-line parameter identification. Therefore, it is widely adopted for complex industrial process control. In this work, Laguerre function based adaptive model predictive control algorithm (AMPC) was implemented to control continuous stirred tank rea...

Experience-based model predictive control using reinforcement learning

controlling nonlinear processes, using laguerre functions based adaptive model predictive control (ampc) algorithm

laguerre function has many advantages such as good approximation capability for different systems, low computational complexity and the facility of on-line parameter identification. therefore, it is widely adopted for complex industrial process control. in this work, laguerre function based adaptive model predictive control algorithm (ampc) was implemented to control continuous stirred tank rea...

Hybrid model predictive control of a nonlinear three-tank system based on the proposed compact form of piecewise affine model

In this paper, a predictive control based on the proposed hybrid model is designed to control the fluid height in a three-tank system with nonlinear dynamics whose operating mode depends on the instantaneous amount of system states. The use of nonlinear hybrid model in predictive control leads to a problem of mixed integer nonlinear programming (MINLP) which is very complex and time consuming t...

Learning-based model predictive control for Markov decision processes

We propose the use of Model Predictive Control (MPC) for controlling systems described by Markov decision processes. First, we consider a straightforward MPC algorithm for Markov decision processes. Then, we propose value functions, a means to deal with issues arising in conventional MPC, e.g., computational requirements and sub-optimality of actions. We use reinforcement learning to let an MPC...