A novel discrete-time neurodynamic algorithm for future constrained quadratic programming with wheeled mobile robot control

The problem of discrete-time dynamic constrained quadratic programming including equality and inequality constraints is formulated and investigated, simply termed future constrained quadratic programming (FCQP) problem in this paper. To obtain the optimal solution of such an FCQP problem in real time and with high precision, a novel discrete-time zeroing neurodynamic (DTZN) algorithm, which is developed by combining the corresponding continuous-time zeroing neurodynamic model and a five-step explicit linear multi-step (ELMS) rule, is proposed and termed ELMS-type five-step DTZN (5SDTZN) algorithm. Then, the convergence and precision of the ELMS-type 5SDTZN algorithm are analyzed theoretically. For comparison, three other DTZN algorithms as well as discrete-time gradient and varying-parameter neurodynamic algorithms are also presented. Afterward, through numerical verifications and comparisons, the efficacy and superiority of the ELMS-type 5SDTZN algorithm for solving the FCQP problem are illustrated. Finally, the applicability of the ELMS-type 5SDTZN algorithm for the coordinated repetitive motion control of a wheeled mobile robot with physical constraints is demonstrated.