6 research outputs found
Scalable tube model predictive control of uncertain linear systems using ellipsoidal sets
This work proposes a novel robust model predictive control (MPC) algorithm
for linear systems affected by dynamic model uncertainty and exogenous
disturbances. The uncertainty is modeled using a linear fractional perturbation
structure with a time-varying perturbation matrix, enabling the algorithm to be
applied to a large model class. The MPC controller constructs a state tube as a
sequence of parameterized ellipsoidal sets to bound the state trajectories of
the system. The proposed approach results in a semidefinite program to be
solved online, whose size scales linearly with the order of the system. The
design of the state tube is formulated as an offline optimization problem,
which offers flexibility to impose desirable features such as robust invariance
on the terminal set. This contrasts with most existing tube MPC strategies
using polytopic sets in the state tube, which are difficult to design and whose
complexity grows combinatorially with the system order. The algorithm
guarantees constraint satisfaction, recursive feasibility, and stability of the
closed loop. The advantages of the algorithm are demonstrated using two
simulation studies.Comment: Submitted to International Journal of Robust and Nonlinear Contro
Computationally efficient robust MPC using optimized constraint tightening
A robust model predictive control (MPC) method is presented for linear,
time-invariant systems affected by bounded additive disturbances. The main
contribution is the offline design of a disturbance-affine feedback gain
whereby the resulting constraint tightening is minimized. This is achieved by
formulating the constraint tightening problem as a convex optimization problem
with the feedback term as a variable. The resulting MPC controller has the
computational complexity of nominal MPC, and guarantees recursive feasibility,
stability and constraint satisfaction. The advantages of the proposed approach
compared to existing robust MPC methods are demonstrated using numerical
examples.Comment: Submitted to the 61st IEEE Conference on Decision and Control 202
A distributed framework for linear adaptive MPC
Adaptive model predictive control (MPC) robustly ensures safety while reducing uncertainty during operation. In this paper, a distributed version is proposed to deal with network systems featuring multiple agents and limited communication. To solve the problem in a distributed manner, structure is imposed on the control design ingredients without sacrificing performance. Decentralized and distributed adaptation schemes that allow for a reduction of the uncertainty online compatibly with the network topology are also proposed. The algorithm ensures robust constraint satisfaction, recursive feasibility and finite gain â„“2 stability, and yields lower closed-loop cost compared to robust distributed MPC in simulations