8,246 research outputs found
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
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
Magnetic Actuators and Suspension for Space Vibration Control
The research on microgravity vibration isolation performed at the University of Virginia is summarized. This research on microgravity vibration isolation was focused in three areas: (1) the development of new actuators for use in microgravity isolation; (2) the design of controllers for multiple-degree-of-freedom active isolation; and (3) the construction of a single-degree-of-freedom test rig with umbilicals. Described are the design and testing of a large stroke linear actuator; the conceptual design and analysis of a redundant coarse-fine six-degree-of-freedom actuator; an investigation of the control issues of active microgravity isolation; a methodology for the design of multiple-degree-of-freedom isolation control systems using modern control theory; and the design and testing of a single-degree-of-freedom test rig with umbilicals
Maximum Entropy/Optimal Projection (MEOP) control design synthesis: Optimal quantification of the major design tradeoffs
The underlying philosophy and motivation of the optimal projection/maximum entropy (OP/ME) stochastic modeling and reduced control design methodology for high order systems with parameter uncertainties are discussed. The OP/ME design equations for reduced-order dynamic compensation including the effect of parameter uncertainties are reviewed. The application of the methodology to several Large Space Structures (LSS) problems of representative complexity is illustrated
Robust Loopshaping for Process Control
Strong trends in chemical engineering and plant operation have made the control of processes increasingly difficult and have driven the process industry's demand for improved control techniques. Improved control leads to savings in resources, smaller downtimes, improved safety, and reduced pollution. Though the need for improved process control is clear, advanced control methodologies have had only limited acceptance and application in industrial practice. The reason for this gap between control theory and practice is that existing control methodologies do not adequately address all of the following control system requirements and problems associated with control design:
* The controller must be insensitive to plant/model mismatch, and perform well under unmeasured or poorly modeled disturbances.
* The controlled system must perform well under state or actuator constraints.
* The controlled system must be safe, reliable, and easy to maintain.
* Controllers are commonly required to be decentralized.
* Actuators and sensors must be selected before the controller can be designed.
* Inputs and outputs must be paired before the design of a decentralized controller.
A framework is presented to address these control requirements/problems in a general, unified manner. The approach will be demonstrated on adhesive coating processes and distillation columns
A two-stage probability based, conservatism reduction methodology for traditional Minimax robust control system design
A two-stage, probability-based controller design methodology is proposed to reduce the conservatism from traditional robust minimax controller design method, by relaxing the norm-bounded parameter uncertainty constraint and incorporating uncertain parameters' probabilistic information.Ph.D
Application of robust control in unmanned vehicle flight control system design
The robust loop-shaping control methodology is applied in the flight control system
design of the Cranfield A3 Observer unmanned, unstable, catapult launched air vehicle.
Detailed linear models for the full operational flight envelope of the air vehicle are
developed. The nominal and worst-case models are determined using the v-gap metric.
The effect of neglecting subsystems such as actuators and/or computation delays on
modelling uncertainty is determined using the v-gap metric and shown to be significant.
Detailed designs for the longitudinal, lateral, and the combined full dynamics TDF
controllers were carried out. The Hanus command signal conditioning technique is also
implemented to overcome actuator saturation and windup. The robust control system is
then successfully evaluated in the high fidelity 6DOF non-linear simulation to assess its
capability of launch stabilization in extreme cross-wind conditions, control
effectiveness in climb, and navigation precision through the prescribed 3D flight path in
level cruise. Robust performance and stability of the single-point non-scheduled control
law is also demonstrated throughout the full operational flight envelope the air vehicle
is capable of and for all flight phases and beyond, to severe launch conditions, such as
33knots crosswind and exaggerated CG shifts.
The robust TDF control law is finally compared with the classical PMC law where the
actual number of variables to be manipulated manually in the design process are shown
to be much less, due to the scheduling process elimination, although the size of the final
controller was much higher. The robust control law performance superiority is
demonstrated in the non-linear simulation for the full flight envelope and in extreme
flight conditions
Risk-Averse Model Predictive Operation Control of Islanded Microgrids
In this paper we present a risk-averse model predictive control (MPC) scheme
for the operation of islanded microgrids with very high share of renewable
energy sources. The proposed scheme mitigates the effect of errors in the
determination of the probability distribution of renewable infeed and load.
This allows to use less complex and less accurate forecasting methods and to
formulate low-dimensional scenario-based optimisation problems which are
suitable for control applications. Additionally, the designer may trade
performance for safety by interpolating between the conventional stochastic and
worst-case MPC formulations. The presented risk-averse MPC problem is
formulated as a mixed-integer quadratically-constrained quadratic problem and
its favourable characteristics are demonstrated in a case study. This includes
a sensitivity analysis that illustrates the robustness to load and renewable
power prediction errors
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