14,015 research outputs found
Fault tolerant model predictive control of open channels
Automated control of water systems (irrigation canals, navigation canals, rivers etc.) relies on the measured data. The control action is calculated, in case of feedback controller, directly from the on-line measured data. If the measured data is corrupted, the calculated control action will have a different effect than it is desired. Therefore, it is crucial that the feedback controller receives good quality measurement data. On-line fault detection techniques can be applied in order to detect the faulty data and correct it. After the detection and correction of the sensor data, the controller should be able to still maintain the set point of the system. In this paper this principle using the sensor fault masking is applied to model predictive control of open channels. A case study of a reach of the northwest of the inland navigation network of France is presented. Model predictive control and water level sensor masking is applied.Peer ReviewedPostprint (published version
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
Active Sampling-based Binary Verification of Dynamical Systems
Nonlinear, adaptive, or otherwise complex control techniques are increasingly
relied upon to ensure the safety of systems operating in uncertain
environments. However, the nonlinearity of the resulting closed-loop system
complicates verification that the system does in fact satisfy those
requirements at all possible operating conditions. While analytical proof-based
techniques and finite abstractions can be used to provably verify the
closed-loop system's response at different operating conditions, they often
produce conservative approximations due to restrictive assumptions and are
difficult to construct in many applications. In contrast, popular statistical
verification techniques relax the restrictions and instead rely upon
simulations to construct statistical or probabilistic guarantees. This work
presents a data-driven statistical verification procedure that instead
constructs statistical learning models from simulated training data to separate
the set of possible perturbations into "safe" and "unsafe" subsets. Binary
evaluations of closed-loop system requirement satisfaction at various
realizations of the uncertainties are obtained through temporal logic
robustness metrics, which are then used to construct predictive models of
requirement satisfaction over the full set of possible uncertainties. As the
accuracy of these predictive statistical models is inherently coupled to the
quality of the training data, an active learning algorithm selects additional
sample points in order to maximize the expected change in the data-driven model
and thus, indirectly, minimize the prediction error. Various case studies
demonstrate the closed-loop verification procedure and highlight improvements
in prediction error over both existing analytical and statistical verification
techniques.Comment: 23 page
Certainty Closure: Reliable Constraint Reasoning with Incomplete or Erroneous Data
Constraint Programming (CP) has proved an effective paradigm to model and
solve difficult combinatorial satisfaction and optimisation problems from
disparate domains. Many such problems arising from the commercial world are
permeated by data uncertainty. Existing CP approaches that accommodate
uncertainty are less suited to uncertainty arising due to incomplete and
erroneous data, because they do not build reliable models and solutions
guaranteed to address the user's genuine problem as she perceives it. Other
fields such as reliable computation offer combinations of models and associated
methods to handle these types of uncertain data, but lack an expressive
framework characterising the resolution methodology independently of the model.
We present a unifying framework that extends the CP formalism in both model
and solutions, to tackle ill-defined combinatorial problems with incomplete or
erroneous data. The certainty closure framework brings together modelling and
solving methodologies from different fields into the CP paradigm to provide
reliable and efficient approches for uncertain constraint problems. We
demonstrate the applicability of the framework on a case study in network
diagnosis. We define resolution forms that give generic templates, and their
associated operational semantics, to derive practical solution methods for
reliable solutions.Comment: Revised versio
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Multiobjective control of a four-link flexible manipulator: A robust H∞ approach
Copyright [2002] IEEE. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.This paper presents an approach to robust H∞ control of a real multilink flexible manipulator via regional pole assignment. We first show that the manipulator system can be approximated by a linear continuous uncertain model with exogenous disturbance input. The uncertainty occurring in an operating space is assumed to be norm-bounded and enter into both the system and control matrices. Then, a multiobjective simultaneous realization problem is studied. The purpose of this problem is to design a state feedback controller such that, for all admissible parameter uncertainties, the closed-loop system simultaneously satisfies both the prespecified H∞ norm constraint on the transfer function from the disturbance input to the system output and the prespecified circular pole constraint on the closed-loop system matrix. An algebraic parameterized approach is developed to characterize the existence conditions as well as the analytical expression of the desired controllers. Third, by comparing with the traditional linear quadratic regulator control method in the sense of robustness and tracking precision, we provide both the simulation and experimental results to demonstrate the effectiveness and advantages of the proposed approach
Sufficient Conditions for Feasibility and Optimality of Real-Time Optimization Schemes - II. Implementation Issues
The idea of iterative process optimization based on collected output
measurements, or "real-time optimization" (RTO), has gained much prominence in
recent decades, with many RTO algorithms being proposed, researched, and
developed. While the essential goal of these schemes is to drive the process to
its true optimal conditions without violating any safety-critical, or "hard",
constraints, no generalized, unified approach for guaranteeing this behavior
exists. In this two-part paper, we propose an implementable set of conditions
that can enforce these properties for any RTO algorithm. This second part
examines the practical side of the sufficient conditions for feasibility and
optimality (SCFO) proposed in the first and focuses on how they may be enforced
in real application, where much of the knowledge required for the conceptual
SCFO is unavailable. Methods for improving convergence speed are also
considered.Comment: 56 pages, 15 figure
Study on adaptive control of nonlinear dynamical systems based on quansi-ARX models
制度:新 ; 報告番号:甲3441号 ; 学位の種類:博士(工学) ; 授与年月日:15-Sep-11 ; 早大学位記番号:新576
Set-membership LPV model identification of vehicle lateral dynamics
Set-membership identification of a Linear Parameter Varying (LPV) model describing the vehicle lateral dynamics is addressed in the paper. The model structure, chosen as much as possible on the ground of physical insights into the vehicle lateral behavior, consists of two single-input single-output {LPV} models relating the steering angle to the yaw rate and to the sideslip angle. A set of experimental data obtained by performing a large number of maneuvers is used to identify the vehicle lateral dynamics model. Prior information on the error bounds on the output and the time-varying parameter measurements are taken into account. Comparison with other vehicle lateral dynamics models is discussed
Fault tolerant model predictive control of open channels
Trabajo presentado a la USCID Conference on Planning, Operation and Automation of Irrigation Delivery Systems celebrada en Phoenix, Arizona (US) del 2 al 5 de diciembre de 2014.Automated control of water systems (irrigation canals, navigation canals, rivers etc.) relies on the measured data. The control action is calculated, in case of feedback controller, directly from the on-line measured data. If the measured data is corrupted, the calculated control action will have a different effect than it is desired. Therefore, it is crucial that the feedback controller receives good quality measurement data. On-line fault detection techniques can be applied in order to detect the faulty data and correct it. After the detection and correction of the sensor data, the controller should be able to still maintain the set point of the system. In this paper this principle using the sensor fault masking is applied to model predictive control of open channels. A case study of a reach of the northwest of the inland navigation network of France is presented. Model predictive control and water level sensor masking is applied.Peer Reviewe
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