Abstract A learning-based nonlinear model predictive control (LBNMPC) method is proposed in this paper for general systems under system uncertainties and subject to state input constraints. The LBNMPC strategy decouples the robustness performance requirements by employing an additional learned introducing it into MPC framework along with nominal model. helps ensure closed-loop system’s safety s...

A recently growing literature discusses the topics of direct yaw moment control based on model predictive (MPC), and energy-efficient torque-vectoring (TV) for electric vehicles with multiple powertrains. To reduce energy consumption, available TV studies focus allocation layer, which calculates individual wheel torque levels to generate total reference longitudinal force moment, specified by h...

evolving models have found applications in many real world systems. in this paper, potentials of the evolving linear models (elms) in tracking control design for nonlinear variable structure systems are introduced. at first, an elm is introduced as a dynamic single input, single output (siso) linear model whose parameters as well as dynamic orders of input and output signals can change through ...