Comments on "On the global stability of Delayed neural Networks"

Multiparametric quadratic and linear programming theory has been applied with success for implementing deterministic MPC controllers. In this note, we have proposed to apply the approximate multipara-metric convex programming solver of [18] to the robust MPC control scheme proposed in [7]. An explicit description of the control law is obtained for ease of implementation of robust MPC. The control law assures robust constraint handling and robust convergence to a given bounded set. Also, an alternative approach has been given in order to assure convergence to the origin. Global analytical model pre-dictive control with input constraints, " in Proc. Computation of the constrained infinite time linear quadratic regulator, " in Proc. Amer. On linear programming and robust model-predictive control using impulse-response, " Syst. Worst case formulations of model predictive control for systems with bounded parameters, " Automatica, vol. 3, no. 5, pp. 763–781, 1997. [10] P. Scokaert and D. Mayne, " Min-max feedback model predictive control for constrained linear systems, " IEEE Trans. control of constrained uncertain discrete-time linear systems, " IEEE Trans. Autom. " Feedback min-max model pre-dictive control using a single linear program: Robust stability and the explicit solution, " Int. Piecewise affinity of min-max mpc with bounded additive uncertainties and a quadratic criterion, " Automatica, vol. 42, pp. 295–302, 2006. [16] Z. Wan and M. Kothare, " An efficient off-line formulation of robust model predictive control using linear matrix inequalities, " Automatica, vol. 39, pp. 837–846, 2003. [17] C. Rowe and J. Maciejowski, " An algorithm for multi-parametric mixed integer semidefinite optimisation, " in Proc. 42th IEEE Conf. Abstract—In this note, we show that the theorem and its corollary given in the above paper are not correctly stated. In addition, a revised version is proposed in the light of the original idea.