Stability Issues in Model-Based Predictive Control with Output Constraints

response of the enhanced system; however, it is significantly reduced. Conclusions This paper has presented some enhancements that greatly improve the performance of predictive control. During the course of this research, the integrator was removed resulting in a more robust controller. Feed forward was then added to the controller yielding a closed loop system with increased speed; however, the resulting system may yield a finite steadystate error to step commands. To compensate for this deficiency, the intelligent integrator was developed and implemented on the predictive controller. The enhanced predictive controller developed in this research possesses desirable qualities that the standard predictive control does not have. References Background Predictive control (PC) is a model based controller currently under study by many different branches of engineering. Due to its dependency on large amounts of computer usage, it has traditionally been implemented on slow systems (time constants on the order minutes) and does not require enormous amounts of computer usage to run in real time. With the advent of more powerful computing facilities, predictive control has become feasible for use in faster, real time systems such as robots. Predictive control was developed to perform two tasks: 1) prediction of the process output over a future time interval, assuming that no further control action is taken; 2) optimization of the future output trajectory over a finite horizon based on given criteria by varying a finite number of control actions (Maurath et al., 1985a). Several design methodologies have been developed for the predictive control The PC attempts to force a system to track a desired trajectory by minimizing future predicted error over a finite preview horizon