A plant is self-regulating if we with constant inputs can keep the controlled outputs within acceptable bounds. (Note that this definition may be applied to any layer in the control
system, so the plant may be a partially controlled process). “True” self-regulation is defined
as the case where no control is ever needed at the lowest layer (i.e. m is constant). It relies
on the process to dampen the disturbances itself, e.g. by having large buffer tanks. We rarely
have “true” self-regulation because it may be very costly.
Self - optimizing control is when an acceptable loss can be achieved using constant setpoints for the controlled variables (without the need to reoptimize when disturbances occur). “True” self-optimization is defined as the case where no re-optimization is ever needed (so cs can be kept constant always), but this objective is usually not satisfied. On the other hand, we must require that the process is self-optimizing within the time period between each re-optimization, or else we cannot use separate control and optimization layers.
A process is self-optimizing if there exists a set of controlled outputs(c) such that if
we with keep constant setpoints for the optimized variables (cs), then we can keep the loss within an acceptable bound within a specified time period. A steady-state analysis is usually sufficient to analyze if we have self-optimality. This is based on the assumption that the
closed-loop time constant of the control system is smaller than the time period between each
re-optimization (so that it settles to a new steady-state) and that the value of the objective
function ( ) is mostly determined by the steady-state behavior (i.e. there is no “costly”
dynamic behavior e.g. imposed by poor control).
Most of the terms given above are in standard use and the definitions mostly follow those of Skogestad and Postlethwaite (1996).