  Driven by the flexible manufacturing trend in the process control industry
and the uncertain nature of chemical process models, this article aims to
achieve offset-free tracking for a family of uncertain nonlinear systems (e.g.,
using process models with parametric uncertainties) with adaptable performance.
The proposed adaptive control approach incorporates into the control loop an
adaptive neural network embedded contraction-based controller (to ensure
convergence to time-varying references) and an online parameter identification
module coupled with reference generation (to ensure modelled parameters
converge those of the physical system). The integrated learning and control
approach involves training a state and parameter dependent neural network to
learn a contraction metric parameterized by the uncertain parameter and a
differential feedback gain. This neural network is then embedded in an adaptive
contraction-based control law which is updated by parameter estimates online.
As uncertain parameter estimates converge to the corresponding physical values,
offset-free tracking, simultaneously with improved convergence rates, can be
achieved, resulting in a flexible, efficient and less conservative approach to
the reference tracking control of uncertain nonlinear processes. An
illustrative example is included to demonstrate the overall approach. An
illustrative example is included to demonstrate the overall approach.
