6,863 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
Robust fault detection for vehicle lateral dynamics: Azonotope-based set-membership approach
© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksIn this work, a model-based fault detection layoutfor vehicle lateral dynamics system is presented. The majorfocus in this study is on the handling of model uncertainties andunknown inputs. In fact, the vehicle lateral model is affectedby several parameter variations such as longitudinal velocity,cornering stiffnesses coefficients and unknown inputs like windgust disturbances. Cornering stiffness parameters variation isconsidered to be unknown but bounded with known compactset. Their effect is addressed by generating intervals for theresiduals based on the zonotope representation of all possiblevalues. The developed fault detection procedure has been testedusing real driving data acquired from a prototype vehicle.Index Terms— Robust fault detection, interval models,zonotopes, set-membership, switched uncertain systems, LMIs,input-to-state stability, arbitrary switching.Peer ReviewedPostprint (author's final draft
Learning-based predictive control for linear systems: a unitary approach
A comprehensive approach addressing identification and control for
learningbased Model Predictive Control (MPC) for linear systems is presented.
The design technique yields a data-driven MPC law, based on a dataset collected
from the working plant. The method is indirect, i.e. it relies on a model
learning phase and a model-based control design one, devised in an integrated
manner. In the model learning phase, a twofold outcome is achieved: first,
different optimal p-steps ahead prediction models are obtained, to be used in
the MPC cost function; secondly, a perturbed state-space model is derived, to
be used for robust constraint satisfaction. Resorting to Set Membership
techniques, a characterization of the bounded model uncertainties is obtained,
which is a key feature for a successful application of the robust control
algorithm. In the control design phase, a robust MPC law is proposed, able to
track piece-wise constant reference signals, with guaranteed recursive
feasibility and convergence properties. The controller embeds multistep
predictors in the cost function, it ensures robust constraints satisfaction
thanks to the learnt uncertainty model, and it can deal with possibly
unfeasible reference values. The proposed approach is finally tested in a
numerical example
A unified framework for solving a general class of conditional and robust set-membership estimation problems
In this paper we present a unified framework for solving a general class of
problems arising in the context of set-membership estimation/identification
theory. More precisely, the paper aims at providing an original approach for
the computation of optimal conditional and robust projection estimates in a
nonlinear estimation setting where the operator relating the data and the
parameter to be estimated is assumed to be a generic multivariate polynomial
function and the uncertainties affecting the data are assumed to belong to
semialgebraic sets. By noticing that the computation of both the conditional
and the robust projection optimal estimators requires the solution to min-max
optimization problems that share the same structure, we propose a unified
two-stage approach based on semidefinite-relaxation techniques for solving such
estimation problems. The key idea of the proposed procedure is to recognize
that the optimal functional of the inner optimization problems can be
approximated to any desired precision by a multivariate polynomial function by
suitably exploiting recently proposed results in the field of parametric
optimization. Two simulation examples are reported to show the effectiveness of
the proposed approach.Comment: Accpeted for publication in the IEEE Transactions on Automatic
Control (2014
Evolutionary L∞ identification and model reduction for robust control
An evolutionary approach for modern robust control oriented system identification and model reduction in the frequency domain is proposed. The technique provides both an optimized nominal model and a 'worst-case' additive or multiplicative uncertainty bounding function which is compatible with robust control design methodologies. In addition, the evolutionary approach is applicable to both continuous- and discrete-time systems without the need for linear parametrization or a confined problem domain for deterministic convex optimization. The proposed method is validated against a laboratory multiple-input multiple-output (MIMO) test rig and benchmark problems, which show a higher fitting accuracy and provides a tighter L�¢���� error bound than existing methods in the literature do
A probabilistic interpretation of set-membership filtering: application to polynomial systems through polytopic bounding
Set-membership estimation is usually formulated in the context of set-valued
calculus and no probabilistic calculations are necessary. In this paper, we
show that set-membership estimation can be equivalently formulated in the
probabilistic setting by employing sets of probability measures. Inference in
set-membership estimation is thus carried out by computing expectations with
respect to the updated set of probability measures P as in the probabilistic
case. In particular, it is shown that inference can be performed by solving a
particular semi-infinite linear programming problem, which is a special case of
the truncated moment problem in which only the zero-th order moment is known
(i.e., the support). By writing the dual of the above semi-infinite linear
programming problem, it is shown that, if the nonlinearities in the measurement
and process equations are polynomial and if the bounding sets for initial
state, process and measurement noises are described by polynomial inequalities,
then an approximation of this semi-infinite linear programming problem can
efficiently be obtained by using the theory of sum-of-squares polynomial
optimization. We then derive a smart greedy procedure to compute a polytopic
outer-approximation of the true membership-set, by computing the minimum-volume
polytope that outer-bounds the set that includes all the means computed with
respect to P
Model-based sensor supervision inland navigation networks: Cuinchy-Fontinettes case study
In recent years, inland navigation networks benefit from the innovation of the instrumentation and
SCADA systems. These data acquisition and control systems lead to the improvement of the manage-
ment of these networks. Moreover, they allow the implementation of more accurate automatic control
to guarantee the navigation requirements. However, sensors and actuators are subject to faults due to
the strong effects of the environment, aging, etc. Thus, before implementing automatic control strate-
gies that rely on the fault-free mode, it is necessary to design a fault diagnosis scheme. This fault
diagnosis scheme has to detect and isolate possible faults in the system to guarantee fault-free data and
the efficiency of the automatic control algorithms. Moreover, the proposed supervision scheme could
predict future incipient faults that are necessary to perform predictive maintenance of the equipment. In
this paper, a general architecture of sensor fault detection and isolation using model-based approaches
will be proposed for inland navigation networks. The proposed approach will be particularized for the
Cuinchy-Fontinettes reach located in the north of France. The preliminary results show the effectiveness
of the proposed fault diagnosis methodologies using a realistic simulator and fault scenarios.In recent years, inland navigation networks bene¿t from the innovation of the instrumentation and SCADA systems. These data acquisition and control systems lead to the improvement of the management of these networks. Moreover, they allow the implementation of more accurate automatic control to guarantee the navigation requirements. However, sensors and actuators are subject to faults due to the strong effects of the environment, aging, etc. Thus, before implementing automatic control strategies that rely on the fault-free mode, it is necessary to design a fault diagnosis scheme. This fault diagnosis scheme has to detect and isolate possible faults in the system to guarantee fault-free data and the efficiency of the automatic control algorithms. Moreover, the proposed supervision scheme could predict future incipient faults that are necessary to perform predictive maintenance of the equipment. In this paper, a general architecture of sensor fault detection and isolation using model-based approaches will be proposed for inland navigation networks. The proposed approach will be particularized for the Cuinchy-Fontinettes reach located in the north of France. The preliminary results show the effectiveness of the proposed fault diagnosis methodologies using a realistic simulator and fault scenarios.Peer ReviewedPostprint (author's final draft
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