4,360 research outputs found
Evolutionary algorithms for practical sensor fault tolerant control
The Shaky Hand is a multi-input, multi-output laboratory demonstrator which is modelled
on a village fete game. In the original, the aim is to guide, by hand, a wire loop along a wire
which has been bent to form a meandering track, 'without touching the loop to the wire. In
the original game, touching the hand-held loop against the wire track sets off a loud warning
bell and the player loses.
The thesis presents the research work associated with the quest for practical solutions to a
generic problem: the correct operation of a fallible system. The work covers three distinct
areas: modelling of the demonstrator, design and construction of a physical system, and
evoiution of algorithms for control of the demonstrator in practice in the presence of sensor
faults, using Cartesian Genetic Programming (CGP). The third area forms the core of the
thesis. The key challenges in creating the virtual environment to train for generic sensor fault
tolerant algorithms are considered and addressed. The evolved algorithms are analysed and
then verified using the demonstrator in practice. The practical results showed that sensor
fault tolerant control was successfully achieved
Optimised configuration of sensors for fault tolerant control of an electro-magnetic suspension system
For any given system the number and location of sensors can affect the closed-loop performance as well as the reliability of the system. Hence, one problem in control system design is the selection of the sensors in some optimum sense that considers both the system performance and reliability. Although some methods have been proposed that deal with some of the aforementioned aspects, in this work, a design framework dealing with both control and reliability aspects is presented. The proposed framework is able to identify the best sensor set for which optimum performance is achieved even under single or multiple sensor failures with minimum sensor redundancy. The proposed systematic framework combines linear quadratic Gaussian control, fault tolerant control and multiobjective optimisation. The efficacy of the proposed framework is shown via appropriate simulations on an electro-magnetic suspension system
Mathematical control of complex systems 2013
Mathematical control of complex systems have already become an ideal research area for control engineers, mathematicians, computer scientists, and biologists to understand, manage, analyze, and interpret functional information/dynamical behaviours from real-world complex dynamical systems, such as communication systems, process control, environmental systems, intelligent manufacturing systems, transportation systems, and structural systems. This special issue aims to bring together the latest/innovative knowledge and advances in mathematics for handling complex systems. Topics include, but are not limited to the following: control systems theory (behavioural systems, networked control systems, delay systems, distributed systems, infinite-dimensional systems, and positive systems); networked control (channel capacity constraints, control over communication networks, distributed filtering and control, information theory and control, and sensor networks); and stochastic systems (nonlinear filtering, nonparametric methods, particle filtering, partial identification, stochastic control, stochastic realization, system identification)
Optimised configuration of sensing elements for control and fault tolerance applied to an electro-magnetic suspension system
New technological advances and the requirements to increasingly abide
by new safety laws in engineering design projects highly affects industrial
products in areas such as automotive, aerospace and railway industries.
The necessity arises to design reduced-cost hi-tech products with minimal
complexity, optimal performance, effective parameter robustness properties,
and high reliability with fault tolerance. In this context the control system
design plays an important role and the impact is crucial relative to the level
of cost efficiency of a product.
Measurement of required information for the operation of the design
control system in any product is a vital issue, and in such cases a number of
sensors can be available to select from in order to achieve the desired system
properties. However, for a complex engineering system a manual procedure
to select the best sensor set subject to the desired system properties can
be very complicated, time consuming or even impossible to achieve. This is
more evident in the case of large number of sensors and the requirement to
comply with optimum performance.
The thesis describes a comprehensive study of sensor selection for control
and fault tolerance with the particular application of an ElectroMagnetic
Levitation system (being an unstable, nonlinear, safety-critical system with
non-trivial control performance requirements). The particular aim of the
presented work is to identify effective sensor selection frameworks subject to
given system properties for controlling (with a level of fault tolerance) the
MagLev suspension system. A particular objective of the work is to identify
the minimum possible sensors that can be used to cover multiple sensor faults,
while maintaining optimum performance with the remaining sensors.
The tools employed combine modern control strategies and multiobjective
constraint optimisation (for tuning purposes) methods. An important part
of the work is the design and construction of a 25kg MagLev suspension
to be used for experimental verification of the proposed sensor selection
frameworks
Fast Damage Recovery in Robotics with the T-Resilience Algorithm
Damage recovery is critical for autonomous robots that need to operate for a
long time without assistance. Most current methods are complex and costly
because they require anticipating each potential damage in order to have a
contingency plan ready. As an alternative, we introduce the T-resilience
algorithm, a new algorithm that allows robots to quickly and autonomously
discover compensatory behaviors in unanticipated situations. This algorithm
equips the robot with a self-model and discovers new behaviors by learning to
avoid those that perform differently in the self-model and in reality. Our
algorithm thus does not identify the damaged parts but it implicitly searches
for efficient behaviors that do not use them. We evaluate the T-Resilience
algorithm on a hexapod robot that needs to adapt to leg removal, broken legs
and motor failures; we compare it to stochastic local search, policy gradient
and the self-modeling algorithm proposed by Bongard et al. The behavior of the
robot is assessed on-board thanks to a RGB-D sensor and a SLAM algorithm. Using
only 25 tests on the robot and an overall running time of 20 minutes,
T-Resilience consistently leads to substantially better results than the other
approaches
Fault Tolerant Control for EMS systems with sensor failure
The paper presents a method to recover the performance of an EMS (Electromagnetic suspension) under faulty air gap measurement. The controller is a combination of classical control loops, a Kalman estimator and analytical redundancy (for the air gap signal). In case of a faulty air gap sensor the air gap signal is recovered using the Kalman filter and analytical redundancy. Simulations verify the proposed sensor Fault Tolerant Control (FTC) method for the EMS system
Optimised configuration of sensors for fault tolerant control of an electro-magnetic suspension system
For any given system the number and location of sensors can affect the closed-loop performance as well as the reliability of the system. Hence, one problem in control system design is the selection of the sensors in some optimum sense that considers both the system performance and reliability. Although some methods have been proposed that deal with some of the aforementioned aspects, in this work, a design framework dealing with both control and reliability aspects is presented. The proposed framework is able to identify the best sensor set for which optimum performance is achieved even under single or multiple sensor failures with minimum sensor redundancy. The proposed systematic framework combines linear quadratic Gaussian control, fault tolerant control and multiobjective optimisation. The efficacy of the proposed framework is shown via appropriate simulations on an electro-magnetic suspension system
Robust fault estimation for wind turbine energy via hybrid systems.
The rapid development of modern wind turbine technology has led to increasing demand for improving system reliability and practical concern for robust fault monitoring scheme. This paper presents the investigation of a 5 MW Dynamic Wind Turbine Energy System that was designed to sustain condition monitoring and fault diagnosis with the goal of improving the reliability operations of universal practical control systems. A hybrid stochastic technique is proposed based on an augmented observer combined with eigenstructure assignment for the parameterisation and the genetic algorithm (GA) optimisation to address the attenuation of uncertainty mostly generated by disturbances. Scenarios-based are employed to explore sensor and actuator faults that have direct and indirect impacts on modern wind turbine system, based on monitoring components that are prone to malfunction. The analysis is aimed to determine the effect of concerned simulated faults from uncertainty in respect to environmental disturbances mostly challenged in real-world operations. The efficiency of the proposed approach will improve the reliability performance of wind turbine system states and diagnose uncertain faults simultaneously. The simulation outcomes illustrate the robustness of the dynamic turbine systems with a diagnostic performance to advance the practical solutions for improving reliable systems.N/
Soft Computing Techniques and Their Applications in Intel-ligent Industrial Control Systems: A Survey
Soft computing involves a series of methods that are compatible with imprecise information and complex human cognition. In the face of industrial control problems, soft computing techniques show strong intelligence, robustness and cost-effectiveness. This study dedicates to providing a survey on soft computing techniques and their applications in industrial control systems. The methodologies of soft computing are mainly classified in terms of fuzzy logic, neural computing, and genetic algorithms. The challenges surrounding modern industrial control systems are summarized based on the difficulties in information acquisition, the difficulties in modeling control rules, the difficulties in control system optimization, and the requirements for robustness. Then, this study reviews soft-computing-related achievements that have been developed to tackle these challenges. Afterwards, we present a retrospect of practical industrial control applications in the fields including transportation, intelligent machines, process industry as well as energy engineering. Finally, future research directions are discussed from different perspectives. This study demonstrates that soft computing methods can endow industry control processes with many merits, thus having great application potential. It is hoped that this survey can serve as a reference and provide convenience for scholars and practitioners in the fields of industrial control and computer science
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