41,053 research outputs found
Robust MPC for actuator-fault tolerance using set-based passive fault detection and active fault isolation
In this paper, an actuator fault-tolerant control (FTC) scheme is proposed, which is based on tube-based model predictive control (MPC) and set-theoretic fault detection and isolation (FDI). As a robust MPC technique, tube-based MPC, can effectively deal with system constraints and uncertainties with relatively low computational complexity. Set-based FDI can robustly detect and isolate actuator faults. Here, fault detection (FD) is passive by invariant sets, while fault isolation (FI) is active by tubes. Using the constraint-handling ability of MPC controllers, an active FI approach is implemented. A numerical example illustrates the effectiveness of the proposed approach.Postprint (author’s final draft
Robust receding horizon control for convex dynamics and bounded disturbances
A novel robust nonlinear model predictive control strategy is proposed for
systems with convex dynamics and convex constraints. Using a sequential convex
approximation approach, the scheme constructs tubes that contain predicted
trajectories, accounting for approximation errors and disturbances, and
guaranteeing constraint satisfaction. An optimal control problem is solved as a
sequence of convex programs, without the need of pre-computed error bounds. We
develop the scheme initially in the absence of external disturbances and show
that the proposed nominal approach is non-conservative, with the solutions of
successive convex programs converging to a locally optimal solution for the
original optimal control problem. We extend the approach to the case of
additive disturbances using a novel strategy for selecting linearization points
and seed trajectories. As a result we formulate a robust receding horizon
strategy with guarantees of recursive feasibility and stability of the
closed-loop system
An Improved Constraint-Tightening Approach for Stochastic MPC
The problem of achieving a good trade-off in Stochastic Model Predictive
Control between the competing goals of improving the average performance and
reducing conservativeness, while still guaranteeing recursive feasibility and
low computational complexity, is addressed. We propose a novel, less
restrictive scheme which is based on considering stability and recursive
feasibility separately. Through an explicit first step constraint we guarantee
recursive feasibility. In particular we guarantee the existence of a feasible
input trajectory at each time instant, but we only require that the input
sequence computed at time remains feasible at time for most
disturbances but not necessarily for all, which suffices for stability. To
overcome the computational complexity of probabilistic constraints, we propose
an offline constraint-tightening procedure, which can be efficiently solved via
a sampling approach to the desired accuracy. The online computational
complexity of the resulting Model Predictive Control (MPC) algorithm is similar
to that of a nominal MPC with terminal region. A numerical example, which
provides a comparison with classical, recursively feasible Stochastic MPC and
Robust MPC, shows the efficacy of the proposed approach.Comment: Paper has been submitted to ACC 201
Distributed Model Predictive Control Using a Chain of Tubes
A new distributed MPC algorithm for the regulation of dynamically coupled
subsystems is presented in this paper. The current control action is computed
via two robust controllers working in a nested fashion. The inner controller
builds a nominal reference trajectory from a decentralized perspective. The
outer controller uses this information to take into account the effects of the
coupling and generate a distributed control action. The tube-based approach to
robustness is employed. A supplementary constraint is included in the outer
optimization problem to provide recursive feasibility of the overall controllerComment: Accepted for presentation at the UKACC CONTROL 2016 conference
(Belfast, UK
Dynamic Tube MPC for Nonlinear Systems
Modeling error or external disturbances can severely degrade the performance
of Model Predictive Control (MPC) in real-world scenarios. Robust MPC (RMPC)
addresses this limitation by optimizing over feedback policies but at the
expense of increased computational complexity. Tube MPC is an approximate
solution strategy in which a robust controller, designed offline, keeps the
system in an invariant tube around a desired nominal trajectory, generated
online. Naturally, this decomposition is suboptimal, especially for systems
with changing objectives or operating conditions. In addition, many tube MPC
approaches are unable to capture state-dependent uncertainty due to the
complexity of calculating invariant tubes, resulting in overly-conservative
approximations. This work presents the Dynamic Tube MPC (DTMPC) framework for
nonlinear systems where both the tube geometry and open-loop trajectory are
optimized simultaneously. By using boundary layer sliding control, the tube
geometry can be expressed as a simple relation between control parameters and
uncertainty bound; enabling the tube geometry dynamics to be added to the
nominal MPC optimization with minimal increase in computational complexity. In
addition, DTMPC is able to leverage state-dependent uncertainty to reduce
conservativeness and improve optimization feasibility. DTMPC is demonstrated to
robustly perform obstacle avoidance and modify the tube geometry in response to
obstacle proximity
Robust Model Predictive Control via Scenario Optimization
This paper discusses a novel probabilistic approach for the design of robust
model predictive control (MPC) laws for discrete-time linear systems affected
by parametric uncertainty and additive disturbances. The proposed technique is
based on the iterated solution, at each step, of a finite-horizon optimal
control problem (FHOCP) that takes into account a suitable number of randomly
extracted scenarios of uncertainty and disturbances, followed by a specific
command selection rule implemented in a receding horizon fashion. The scenario
FHOCP is always convex, also when the uncertain parameters and disturbance
belong to non-convex sets, and irrespective of how the model uncertainty
influences the system's matrices. Moreover, the computational complexity of the
proposed approach does not depend on the uncertainty/disturbance dimensions,
and scales quadratically with the control horizon. The main result in this
paper is related to the analysis of the closed loop system under
receding-horizon implementation of the scenario FHOCP, and essentially states
that the devised control law guarantees constraint satisfaction at each step
with some a-priori assigned probability p, while the system's state reaches the
target set either asymptotically, or in finite time with probability at least
p. The proposed method may be a valid alternative when other existing
techniques, either deterministic or stochastic, are not directly usable due to
excessive conservatism or to numerical intractability caused by lack of
convexity of the robust or chance-constrained optimization problem.Comment: This manuscript is a preprint of a paper accepted for publication in
the IEEE Transactions on Automatic Control, with DOI:
10.1109/TAC.2012.2203054, and is subject to IEEE copyright. The copy of
record will be available at http://ieeexplore.ieee.or
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