Application of constrained receding horizon predictive control on a brushless motor

The effect of constraints in generalized predictive control (GPC) is studied with application to the speed control of motor drives. The problem of minimising the GPC cost function subject to equality constraints is handled by programming a polynomial RST controller, designed by using the constrained receding horizon predictive control (CRHPC). The main difference to GPC consists in imposing constraints on the final output values, with the important result that theorems guarantee stable closed loop behaviours for particular sets of tuning parameters. This method applied on a benchmark including a brushless motor proves that the effective use of constraints enables better results for severe conditions of use