Abstract :
In Model Predictive Controller (MPC) design, the traditional approach of expanding the future control signal uses the forward
shift operator to obtain the linear-in-the-parameters relation for predicted output. As a consequence, in case of rapid sampling,
complicated process dynamics and/or high demands on closed-loop performance, satisfactory approximation of the control signal
requires a very large number of forward shift operators, and leads to poorly numerically conditioned solutions and heavy computational
load when implemented on-line. In this paper, by using a performance specification on the exponential change rate of the
control signal, a more appropriate expansion, related to Laguerre net-works, is introduced and analyzed. It is shown that the
number of terms used in the optimization procedure can be reduced to a fraction of that required by the usual procedure. By
relaxing the constraint on the exponential change rate of the control signal and allowing arbitrary complexity in describing the
trajectory, the proposed approach becomes equivalent to the traditional approach in MPC design. Closed-loop stability of the
proposed model predictive control system is analyzed by using terminal state variable constraints.
