Nonlinear model predictive control with regulable computational cost

Nonlinear model predictive control (NMPC) suffers from problems of closed loop instability and huge computational burden, which greatly limit its applications in real plants. In this paper, a new NMPC algorithm, whose stability is robust with respect to regulable computational cost, is presented. First, a new generalized pointwise min-norm (GPMN) control, as well as its analytic form considering a super-ball type input constraint, is given. Second, the GPMN controller is integrated into a normal NMPC algorithm as a structure of control input profile to be optimized, called GPMN enhanced NMPC (GPMN-ENMPC). Finally, a numerical example is presented and simulation results exhibit the advantage of the GPMN-ENMPC algorithm: computational cost can be regulated according to the computational resources with guaranteed stability.