This paper proposes a gap-metric-based
multiple model predictive
control (MMPC) method for nonlinear systems with a wide operating
range. The gap metric theory, integrated into a neighborhood estimation
algorithm, is utilized to partition the whole operating range into
subregions corresponding to operating points. A local controller is
then designed in each subregion and is composed of a constant feedback
control and a receding infinite-horizon control with an estimated
polyhedral stable region. To guarantee the global stability of the
whole system, we design a novel switching rule activating between
local controllers. The simulation study on a continuous stirred-tank
reactor (CSTR) is presented to validate the proposed methods.
This
paper presents a switched offline multiple model predictive
control procedure for nonlinear processes to ease the online computational
burden and reduce the number of submodels. We employ the gap metric
to characterize the dynamic difference between linear models and establish
a linear model bank to approximate the nonlinear system. Based on
the robust MPC algorithm, we develop an offline model predictive controller
for each submodel. The polyhedral invariant set is utilized to expand
the work scope of each local controller. In the offline part, a series
of discrete states are selected, the corresponding feedback gains
are precomputed, and associated polyhedral invariant sets are constructed.
In the online implementation, the control input is simply calculated
by calling the feedback gain according to the current state. A switching
rule is then designed to integrate the submodels and guarantee the
stability of the whole system. Finally, the corresponding simulation
example is presented to validate the efficiency of the presented algorithm.
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