  Model predictive control (MPC) for nonlinear systems suffers a trade-off
between the model accuracy and real-time computational burden. One widely used
approximation method is the successive linearization MPC (SL-MPC) with EKF
method, in which the EKF algorithm is to handle unmeasured disturbances and
unavailable full states information. Inspired by this, an interpretative and
adaptive MPC (IA-MPC) method, is presented in this paper. In our IA-MPC method,
a linear state-space model is firstly obtained by performing the linearization
of a first-principle-based model at the initial point, and then this linear
state-space model is transformed into an equivalent ARX model. This
interpretative ARX model is then updated online by the EKF algorithm, which is
modified as a decoupled one without matrix-inverse operator. The corresponding
ARX-based MPC problem are solved by our previous construction-free, matrix-free
and library-free CDAL-ARX algorithm. This simple library-free C-code
implementation would significantly reduce the difficulty in deploying nonlinear
MPC on embedded platforms. The performance of the IA-MPC method is tested
against the nonlinear MPC with EKF and SL-MPC with EKF method in four typical
nonlinear benchmark examples, which show the effectiveness of our IA-MPC
method.
