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A Contraction-constrained Model Predictive Control for Nonlinear Processes using Disturbance Forecasts
Model predictive control (MPC) has become the most widely used advanced control method in process industry.
A Contraction-constrained Model Predictive Control for Multi-timescale Nonlinear Processes
Many chemical processes exhibit diverse timescale dynamics with a strong coupling between timescale sensitive variables.
Adaptive Contraction-based Control of Uncertain Nonlinear Processes using Neural Networks
This neural network is then embedded in an adaptive contraction-based control law which is updated by parameter estimates online.
Electrolyte Flow Rate Control for Vanadium Redox Flow Batteries using the Linear Parameter Varying Framework
In this article, an electrolyte flow rate control approach is developed for an all-vanadium redox flow battery (VRB) system based on the linear parameter varying (LPV) framework.
Contraction Analysis and Control Synthesis for Discrete-time Nonlinear Processes
Shifting away from the traditional mass production approach, the process industry is moving towards more agile, cost-effective and dynamic process operation (next-generation smart plants).
Discrete-time Contraction-based Control of Nonlinear Systems with Parametric Uncertainties using Neural Networks
In this paper, a contraction theory-based control approach using neural networks is developed for nonlinear chemical processes to achieve time-varying reference tracking.
Control Contraction Metric Synthesis for Discrete-time Nonlinear Systems
Flexible manufacturing has been the trend in the area of the modern chemical process nowadays.
