708 research outputs found
Model-based fault diagnosis for aerospace systems: a survey
http://pig.sagepub.com/content/early/2012/01/06/0954410011421717International audienceThis survey of model-based fault diagnosis focuses on those methods that are applicable to aerospace systems. To highlight the characteristics of aerospace models, generic nonlinear dynamical modeling from flight mechanics is recalled and a unifying representation of sensor and actuator faults is presented. An extensive bibliographical review supports a description of the key points of fault detection methods that rely on analytical redundancy. The approaches that best suit the constraints of the field are emphasized and recommendations for future developments in in-flight fault diagnosis are provided
Distributed Set-Based Observers Using Diffusion Strategy
Distributed estimation is more robust against single points of failure and
requires less communication overhead compared to the centralized version. Among
distributed estimation techniques, set-based estimation has gained much
attention as it provides estimation guarantees for safety-critical applications
and copes with unknown but bounded uncertainties. We propose two distributed
set-based observers using interval-based and set-membership approaches for a
linear discrete-time dynamical system with bounded modeling and measurement
uncertainties. Both algorithms utilize a new over-approximating zonotopes
intersection step named the set-based diffusion step. We use the term diffusion
since our intersection of zonotopes formula resembles the traditional diffusion
step in the stochastic Kalman filter. Our new zonotopes intersection takes
linear time. Our set-based diffusion step decreases the estimation errors and
the size of estimated sets and can be seen as a lightweight approach to achieve
partial consensus between the distributed estimated sets. Every node shares its
measurement with its neighbor in the measurement update step. The neighbors
intersect their estimated sets constituting our proposed set-based diffusion
step. We represent sets as zonotopes since they compactly represent
high-dimensional sets, and they are closed under linear mapping and Minkowski
addition. The applicability of our algorithms is demonstrated by a localization
example. All used data and code to recreate our findings are publicly availabl
A study on fault diagnosis in nonlinear dynamic systems with uncertainties
In this draft, fault diagnosis in nonlinear dynamic systems is addressed. The
objective of this work is to establish a framework, in which not only
model-based but also data-driven and machine learning based fault diagnosis
strategies can be uniformly handled. Instead of the well-established
input-output and the associated state space models, stable image and kernel
representations are adopted in our work as the basic process model forms. Based
on it, the nominal system dynamics can then be modelled as a lower-dimensional
manifold embedded in the process data space. To achieve a reliable fault
detection as a classification problem, projection technique is a capable tool.
For nonlinear dynamic systems, we propose to construct projection systems in
the well-established framework of Hamiltonian systems and by means of the
normalised image and kernel representations. For nonlinear dynamic systems,
process data form a non-Euclidean space. Consequently, the norm-based distance
defined in Hilbert space is not suitable to measure the distance from a data
vector to the manifold of the nominal dynamics. To deal with this issue, we
propose to use a Bregman divergence, a measure of difference between two points
in a space, as a solution. Moreover, for our purpose of achieving a
performance-oriented fault detection, the Bregman divergences adopted in our
work are defined by Hamiltonian functions. This scheme not only enables to
realise the performance-oriented fault detection, but also uncovers the
information geometric aspect of our work. The last part of our work is devoted
to the kernel representation based fault detection and uncertainty estimation
that can be equivalently used for fault estimation. It is demonstrated that the
projection onto the manifold of uncertainty data, together with the
correspondingly defined Bregman divergence, is also capable for fault
detection
Throughput-Distortion Computation Of Generic Matrix Multiplication: Toward A Computation Channel For Digital Signal Processing Systems
The generic matrix multiply (GEMM) function is the core element of
high-performance linear algebra libraries used in many
computationally-demanding digital signal processing (DSP) systems. We propose
an acceleration technique for GEMM based on dynamically adjusting the
imprecision (distortion) of computation. Our technique employs adaptive scalar
companding and rounding to input matrix blocks followed by two forms of packing
in floating-point that allow for concurrent calculation of multiple results.
Since the adaptive companding process controls the increase of concurrency (via
packing), the increase in processing throughput (and the corresponding increase
in distortion) depends on the input data statistics. To demonstrate this, we
derive the optimal throughput-distortion control framework for GEMM for the
broad class of zero-mean, independent identically distributed, input sources.
Our approach converts matrix multiplication in programmable processors into a
computation channel: when increasing the processing throughput, the output
noise (error) increases due to (i) coarser quantization and (ii) computational
errors caused by exceeding the machine-precision limitations. We show that,
under certain distortion in the GEMM computation, the proposed framework can
significantly surpass 100% of the peak performance of a given processor. The
practical benefits of our proposal are shown in a face recognition system and a
multi-layer perceptron system trained for metadata learning from a large music
feature database.Comment: IEEE Transactions on Signal Processing (vol. 60, 2012
Optimal tracking control for uncertain nonlinear systems with prescribed performance via critic-only ADP
This paper addresses the tracking control problem for a class of nonlinear systems described by Euler-Lagrange equations with uncertain system parameters. The proposed control scheme is capable of guaranteeing prescribed performance from two aspects: 1) A special parameter estimator with prescribed performance properties is embedded in the control scheme. The estimator not only ensures the exponential convergence of the estimation errors under relaxed excitation conditions but also can restrict all estimates to pre-determined bounds during the whole estimation process; 2) The proposed controller can strictly guarantee the user-defined performance specifications on tracking errors, including convergence rate, maximum overshoot, and residual set. More importantly, it has the optimizing ability for the trade-off between performance and control cost. A state transformation method is employed to transform the constrained optimal tracking control problem to an unconstrained stationary optimal problem. Then a critic-only adaptive dynamic programming algorithm is designed to approximate the solution of the Hamilton-Jacobi-Bellman equation and the corresponding optimal control policy. Uniformly ultimately bounded stability is guaranteed via Lyapunov-based stability analysis. Finally, numerical simulation results demonstrate the effectiveness of the proposed control scheme
System identification and adaptive current balancing ON/OFF control of DC-DC switch mode power converter
PhD ThesisReliability becomes more and more important in industrial application of Switch Mode Power
Converters (SMPCs). A poorly performing power supply in a power system can influence its operation
and potentially compromise the entire system performance in terms of efficiency. To maintain a high
reliability, high performance SMPC effective control is necessary for regulating the output of the SMPC
system. However, an uncertainty is a key factor in SMPC operation. For example, parameter variations
can be caused by environmental effects such as temperature, pressure and humidity. Usually, fixed
controllers cannot respond optimally and generate an effective signal to compensate the output error
caused by time varying parameter changes. Therefore, the stability is potentially compromised in this
case. To resolve this problem, increasing interest has been shown in employing online system
identification techniques to estimate the parameter values in real time. Moreover, the control scheme
applied after system identification is often called “adaptive control” due to the control signal selfadapting to the parameter variation by receiving the information from the system identification process.
In system identification, the Recursive Least Square (RLS) algorithm has been widely used because it
is well understood and easy to implement. However, despite the popularity of RLS, the high
computational cost and slow convergence speed are the main restrictions for use in SMPC applications.
For this reason, this research presents an alternative algorithm to RLS; Fast Affline Projection (FAP).
Detailed mathematical analysis proves the superior computational efficiency of this algorithm.
Moreover, simulation and experiment result verify this unique adaptive algorithm has improved
performance in terms of computational cost and convergence speed compared with the conventional
RLS methods. Finally, a novel adaptive control scheme is designed for optimal control of a DC-DC
buck converter during transient periods. By applying the proposed adaptive algorithm, the control signal
can be successfully employed to change the ON/OFF state of the power transistor in the DC-DC buck
converter to improve the dynamic behaviour. Simulation and experiment result show the proposed
adaptive control scheme significantly improves the transient response of the buck converter, particularly
during an abrupt load change conditio
Fault tolerant control for nonlinear aircraft based on feedback linearization
The thesis concerns the fault tolerant flight control (FTFC) problem for nonlinear aircraft by making use of analytical redundancy. Considering initially fault-free flight, the feedback linearization theory plays an important role to provide a baseline control approach for de-coupling and stabilizing a non-linear statically unstable aircraft system. Then several reconfigurable control strategies are studied to provide further robust control performance:- A neural network (NN)-based adaption mechanism is used to develop reconfigurable FTFC performance through the combination of a concurrent updated learninglaw. - The combined feedback linearization and NN adaptor FTFC system is further improved through the use of a sliding mode control (SMC) strategy to enhance the convergence of the NN learning adaptor. - An approach to simultaneous estimation of both state and fault signals is incorporated within an active FTFC system.The faults acting independently on the three primary actuators of the nonlinear aircraft are compensated in the control system.The theoretical ideas developed in the thesis have been applied to the nonlinear Machan Unmanned Aerial Vehicle (UAV) system. The simulation results obtained from a tracking control system demonstrate the improved fault tolerant performance for all the presented control schemes, validated under various faults and disturbance scenarios.A Boeing 747 nonlinear benchmark model, developed within the framework of the GARTEUR FM-AG 16 project “fault tolerant flight control systems”,is used for the purpose of further simulation study and testing of the FTFC scheme developed by making the combined use of concurrent learning NN and SMC theory. The simulation results under the given fault scenario show a promising reconfiguration performance
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