  This paper presents a learning- and scenario-based model predictive control
(MPC) design approach for systems modeled in linear parameter-varying (LPV)
framework. Using input-output data collected from the system, a state-space LPV
model with uncertainty quantification is first learned through the variational
Bayesian inference Neural Network (BNN) approach. The learned probabilistic
model is assumed to contain the true dynamics of the system with a high
probability and used to generate scenarios which ensure safety for a
scenario-based MPC. Moreover, to guarantee stability and enhance performance of
the closed-loop system, a parameter-dependent terminal cost and controller, as
well as a terminal robust positive invariant set are designed. Numerical
examples will be used to demonstrate that the proposed control design approach
can ensure safety and achieve desired control performance.
