Lasso-mpc – Predictive Control With ℓ1-regularised Least Squares (springer Theses)
by Marco Gallieri /
2016 / English / PDF
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This thesis proposes a novel Model Predictive Control (MPC)
strategy, which modifies the usual MPC cost function in order to
achieve a desirable sparse actuation. It features an ℓ1-regularised
least squares loss function, in which the control error variance
competes with the sum of input channels magnitude (or slew rate)
over the whole horizon length. While standard control techniques
lead to continuous movements of all actuators, this approach
enables a selected subset of actuators to be used, the others being
brought into play in exceptional circumstances. The same approach
can also be used to obtain asynchronous actuator interventions, so
that control actions are only taken in response to large
disturbances. This thesis presents a straightforward and systematic
approach to achieving these practical properties, which are ignored
by mainstream control theory.
This thesis proposes a novel Model Predictive Control (MPC)
strategy, which modifies the usual MPC cost function in order to
achieve a desirable sparse actuation. It features an ℓ1-regularised
least squares loss function, in which the control error variance
competes with the sum of input channels magnitude (or slew rate)
over the whole horizon length. While standard control techniques
lead to continuous movements of all actuators, this approach
enables a selected subset of actuators to be used, the others being
brought into play in exceptional circumstances. The same approach
can also be used to obtain asynchronous actuator interventions, so
that control actions are only taken in response to large
disturbances. This thesis presents a straightforward and systematic
approach to achieving these practical properties, which are ignored
by mainstream control theory.