33,923 research outputs found
Robust Adaptive Control Barrier Functions: An Adaptive & Data-Driven Approach to Safety (Extended Version)
A new framework is developed for control of constrained nonlinear systems
with structured parametric uncertainties. Forward invariance of a safe set is
achieved through online parameter adaptation and data-driven model estimation.
The new adaptive data-driven safety paradigm is merged with a recent adaptive
control algorithm for systems nominally contracting in closed-loop. This
unification is more general than other safety controllers as closed-loop
contraction does not require the system be invertible or in a particular form.
Additionally, the approach is less expensive than nonlinear model predictive
control as it does not require a full desired trajectory, but rather only a
desired terminal state. The approach is illustrated on the pitch dynamics of an
aircraft with uncertain nonlinear aerodynamics.Comment: Added aCBF non-Lipschitz example and discussion on approach
implementatio
Extremum Seeking-based Iterative Learning Linear MPC
In this work we study the problem of adaptive MPC for linear time-invariant
uncertain models. We assume linear models with parametric uncertainties, and
propose an iterative multi-variable extremum seeking (MES)-based learning MPC
algorithm to learn on-line the uncertain parameters and update the MPC model.
We show the effectiveness of this algorithm on a DC servo motor control
example.Comment: To appear at the IEEE MSC 201
Constrained Nonlinear Model Predictive Control of an MMA Polymerization Process via Evolutionary Optimization
In this work, a nonlinear model predictive controller is developed for a
batch polymerization process. The physical model of the process is
parameterized along a desired trajectory resulting in a trajectory linearized
piecewise model (a multiple linear model bank) and the parameters are
identified for an experimental polymerization reactor. Then, a multiple model
adaptive predictive controller is designed for thermal trajectory tracking of
the MMA polymerization. The input control signal to the process is constrained
by the maximum thermal power provided by the heaters. The constrained
optimization in the model predictive controller is solved via genetic
algorithms to minimize a DMC cost function in each sampling interval.Comment: 12 pages, 9 figures, 28 reference
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