The current methods of predictive controllers are
utilized for those processes in which the rate of output variations is
not high. For such processes, therefore, stability can be achieved by
implementing the constrained predictive controller or applying
infinite prediction horizon. When the rate of the output growth is
high (e.g. for unstable nonminimum phase process) the stabilization
seems to be problematic. In order to avoid this, it is suggested to
change the method in the way that: first, the prediction error growth
should be decreased at the early stage of the prediction horizon, and
second, the rate of the error variation should be penalized. The
growth of the error is decreased through adjusting its weighting
coefficients in the cost function. Reduction in the error variation is
possible by adding the first order derivate of the error into the cost
function. By studying different examples it is shown that using these
two remedies together, the closed-loop stability of unstable
nonminimum phase process can be achieved.
 J.A. Rossiter, J.R. Gossner, B. Kouvaritakis, "Infinite Horizon Predictive
Control", IEEE Transactions On Automatic Control, Vol. 41, No. 10,
 Y.I. Lee, W.H. Kwon, Y.H. Kim, "'Weighted Receding Horizon
Predictive Control and its Related GPC with Guaranteed Stability",
Proceedings of the 32nd Conference on Decision and Control, Texas,
 H.W. Gomma, D.H. Owens, "Time Varying Weighting Generalized
Predictive Control (TGPC) with prediction to performance, stability and
robustness", Proceedings of the 38th Conference on Decision and
Control, Phoenix, Arizona USA, December 1999.
 S. Weiland, A.A. Stoorvogel, A.A. Tiagounov, "End-Point
Parameterization and Guaranteed Stability for a Model Predictive
Control Scheme", Proceedings of the 40th IEEE Conference Decision
and Control, Orlando, Florida USA, December 2001.
 J.R. Gossner, B. Kouvaritakis, J.A. Rossiter, "Constrained Multivariable
cautious Stable Predictive Control", IEE Proceedings-D, Vol. 145, No.
5, September 1998.
 J.A. Rossiter, J.R. Gossner, B. Kouvaritakis, "Constrained cautious
stable predictive control", IEE Proceedings-D, Vol. 144, No. 4, July
 Li-F. Zhou, "The Mixed-Weights Least-Square Stable Predictive
Control for Constrained Multi-Object Multivariable System",
Proceedings of the Second International Conference on Machine
Learning and Cybernetic, Xian, November 2003.