Published July 31, 2024 | Version v1
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Self-tunable approximated explicit MPC: Heat exchanger implementation and analysis

Description

The tunable approximated explicit model predictive control (MPC) comes
with the benefits of real-time tunability without the necessity of solving the
optimization problem online. This paper provides a novel self-tunable control
policy that does not require any interventions of the control engineer during
operation in order to retune the controller subject to the changed working
conditions. Based on the current operating conditions, the autonomous tuning
parameter scales the control input using linear interpolation between the
boundary optimal control actions. The adjustment of the tuning parameter
depends on the current reference value, which makes this strategy suitable
for reference tracking problems. Furthermore, a novel technique for scaling
the tuning parameter is proposed. This extension provides to exploit
different ranges of the tuning parameter assigned to specified operating conditions.
The self-tunable explicit MPC was implemented on a laboratory
heat exchanger with nonlinear and asymmetric behavior. The asymmetric
behavior of the plant was compensated by tuning the controller’s aggressiveness,
as the negative or positive sign of reference change was considered in
the tuning procedure. The designed self-tunable controller improved control
performance by decreasing sum-of-squared control error, maximal overshoots/
undershoots, and settling time compared to the conventional control
strategy based on a single (non-tunable) controller.

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Dates

Accepted
2024-06-13