Discrete-time Contraction-based Control of Nonlinear Systems with Parametric Uncertainties using Neural Networks
This addresses the need for robust control in industrial chemical processes with parametric uncertainties to operate at varying conditions for improved economy.
The paper developed a contraction theory-based control approach using neural networks for nonlinear chemical processes to achieve efficient offset-free tracking of time-varying references under full model uncertainty, without requiring controller redesign as references change.
In response to the continuously changing feedstock supply and market demand for products with different specifications, the processes need to be operated at time-varying operating conditions and targets (e.g., setpoints) to improve the process economy, in contrast to traditional process operations around predetermined equilibriums. In this paper, a contraction theory-based control approach using neural networks is developed for nonlinear chemical processes to achieve time-varying reference tracking. This approach leverages the universal approximation characteristics of neural networks with discrete-time contraction analysis and control. It involves training a neural network to learn a contraction metric and differential feedback gain, that is embedded in a contraction-based controller. A second, separate neural network is also incorporated into the control-loop to perform online learning of uncertain system model parameters. The resulting control scheme is capable of achieving efficient offset-free tracking of time-varying references, with a full range of model uncertainty, without the need for controller structure redesign as the reference changes. This is a robust approach that can deal with bounded parametric uncertainties in the process model, which are commonly encountered in industrial (chemical) processes. This approach also ensures the process stability during online simultaneous learning and control. Simulation examples are provided to illustrate the above approach.