Model-based thermoacoustic oscillation control via ℋ ∞ loop-shaping and integral quadratic constraints

Lean premixed prevaporized (LPP) technology has been widely used in the new generation of gas turbines in which reduced emissions are a priority. However, such combustion systems are susceptible to the damage of self-excited oscillations. Feedback control provide a way of preventing such dynamic stabilities. A flame dynamics assumption is proposed for a recently developed unsteady heat release model, the robust design technique, ℋ ∞ loop-shaping, is applied for the controller design and the performance of the controller is confirmed by simulations of the closed-loop system. The Integral Quadratic Constraints(IQC) method is employed to prove the stability of the closed-loop system. ©2010 IEEE.

Sensor data scheduling for linear quadratic Gaussian control with full state feedback

Networked control systems (NCSs) have attracted much attention in the past decade due to their many advantages and growing number of applications. Different than classic control systems, resources in NCSs, such as network bandwidth and communication energy, are often limited, which degrade the closed-loop system performance and may even cause the system to become unstable. Seeking a desired trade-off between the closed-loop system performance and the limited resources is thus one heated area of research. In this paper, we analyze the trade-off between the sensor-to-controller communication rate and the closed-loop system performance indexed by the conventional LQG control cost. We present and compare several sensor data schedules, and demonstrate that two event-based sensor data schedules provide better trade-off than an optimal offline schedule. Simulation examples are provided to illustrate the theories developed in the paper. © 2012 AACC American Automatic Control Council).

Linear quadratic game and non-cooperative predictive methods for potential application to modelling driver-AFS interactive steering control

This paper is concerned with the modelling of strategic interactions between the human driver and the vehicle active front steering (AFS) controller in a path-following task where the two controllers hold different target paths. The work is aimed at extending the use of mathematical models in representing driver steering behaviour in complicated driving situations. Two game theoretic approaches, namely linear quadratic game and non-cooperative model predictive control (non-cooperative MPC), are used for developing the driver-AFS interactive steering control model. For each approach, the open-loop Nash steering control solution is derived; the influences of the path-following weights, preview and control horizons, driver time delay and arm neuromuscular system (NMS) dynamics are investigated, and the CPU time consumed is recorded. It is found that the two approaches give identical time histories as well as control gains, while the non-cooperative MPC method uses much less CPU time. Specifically, it is observed that the introduction of weight on the integral of vehicle lateral displacement error helps to eliminate the steady-state path-following error; the increase in preview horizon and NMS natural frequency and the decline in time delay and NMS damping ratio improve the path-following accuracy. © 2013 Copyright Taylor and Francis Group, LLC.

Temporally adaptive estimation of logistic classifiers on data streams

Modern technology has allowed real-time data collection in a variety of domains, ranging from environmental monitoring to healthcare. Consequently, there is a growing need for algorithms capable of performing inferential tasks in an online manner, continuously revising their estimates to reflect the current status of the underlying process. In particular, we are interested in constructing online and temporally adaptive classifiers capable of handling the possibly drifting decision boundaries arising in streaming environments. We first make a quadratic approximation to the log-likelihood that yields a recursive algorithm for fitting logistic regression online. We then suggest a novel way of equipping this framework with self-tuning forgetting factors. The resulting scheme is capable of tracking changes in the underlying probability distribution, adapting the decision boundary appropriately and hence maintaining high classification accuracy in dynamic or unstable environments. We demonstrate the scheme's effectiveness in both real and simulated streaming environments. © Springer-Verlag 2009.

Mode I crack growth resistance of metallic foams

A Dugdale-type cohesive zone model is used to predict the mode I crack growth resistance (R-curve) of metallic foams, with the fracture process characterized by an idealized traction-separation law that relates the crack surface traction to crack opening displacement. A quadratic yield function, involving the von Mises effective stress and mean stress, is used to account for the plastic compressibility of metallic foams. Finite element calculations are performed for the crack growth resistance under small scale yielding and small scale bridging in plane strain, with K-field boundary conditions. The following effects upon the fracture process are quantified: material hardening, bridging strength, T-stress (the non-singular stress acting parallel to the crack plane), and the shape of yield surface. To study the failure behaviour and notch sensitivity of metallic foams in the presence of large scale yielding, a study is made for panels embedded with either a centre-crack or an open hole and subjected to tensile stressing. For the centre-cracked panel, a transition crack size is predicted for which the fracture response switches from net section yielding to elastic-brittle fracture. Likewise, for a panel containing a centre-hole, a transition hole diameter exists for which the fracture response switches from net section yielding to a local maximum stress criterion at the edge of the hole.

A constitutive model for transversely isotropic foams, and its application to the indentation of balsa wood

An elastic-plastic constitutive model for transversely isotropic compressible solids (foams) has been developed. A quadratic yield surface with four parameters and one hardening function is proposed. Associated plastic flow is assumed and the yield surface evolves in a self-similar manner calibrated by the uniaxial compressive (or tensile) response of the cellular solid in the axial direction. All material constants in the model (elastic and plastic) can be determined from a combination of a total of four uniaxial and shear tests. The model is used to predict the indentation response of balsa wood to a conical indenter. For the three cone angles considered in this study, very good agreement is found between the experimental measurements and the finite element (FE) predictions of the transversely isotropic cellular solid model. On the other hand, an isotropic foam model is shown to be inadequate to capture the indentation response. © 2005 Elsevier Ltd. All rights reserved.

Monte Carlo approaches to nonlinear optimal control

This paper explores the use of Monte Carlo techniques in deterministic nonlinear optimal control. Inter-dimensional population Markov Chain Monte Carlo (MCMC) techniques are proposed to solve the nonlinear optimal control problem. The linear quadratic and Acrobot problems are studied to demonstrate the successful application of the relevant techniques.

Isotropic constitutive models for metallic foams

The yield behaviour of two aluminum alloy foams (Alporas and Duocel) has been investigated for a range of axisymmetric compressive stress states. The initial yield surface has been measured, and the evolution of the yield surface has been explored for uniaxial and hydrostatic stress paths. It is found that the hydrostatic yield strength is of similar magnitude to the uniaxial yield strength. The yield surfaces are of quadratic shape in the stress space of mean stress versus effective stress, and evolve without corner formation. Two phenomenological isotropic constitutive models for the plastic behaviour are proposed. The first is based on a geometrically self-similar yield surface while the second is more complex and allows for a change in shape of the yield surface due to differential hardening along the hydrostatic and deviatoric axes. Good agreement is observed between the experimentally measured stress versus strain responses and the predictions of the models.

Variability of nanoindentation responses of bone and artificial bone-like composites

Nanoindentation is a popular technique for measuring the intrinsic mechanical response of bone and has been used to measure a single-valued elastic modulus. However, bone is a composite material with 20-80 nm hydroxyapatite plates embedded in a collagen matrix, and modern instrumentation allows for measurements at these small length scales. The present study examines the indentation response of bone and artificial gelatin-apatite nanocomposite materials across three orders of magnitude of lengthscale, from nanometers to micrometers, to isolate the composite phase contributions to the overall response. The load-displacement responses were variable and deviated from the quadratic response of homogeneous materials at small depths. The distribution of apparent elastic modulus values narrowed substantially with increasing indentation load. Indentation of particulate nanocomposites was simulated using finite element analysis. Modeling results replicated the convergence in effective modulus seen in the experiments. It appears that the apatite particles are acting as the continuous ("matrix") phase in bone and nanocomposites. Copyright © 2004 by ASME.

Load-displacement behavior during sharp indentation of viscous-elastic-plastic materials

A constitutive equation is developed for geometrically-similar sharp indentation of a material capable of elastic, viscous, and plastic deformation. The equation is based on a series of elements consisting of a quadratic (reversible) spring, a quadratic (time-dependent, reversible) dashpot, and a quadratic (time-independent, irreversible) slider-essentially modifying a model for an elastic-perfectly plastic material by incorporating a creeping component. Load-displacement solutions to the constitutive equation are obtained for load-controlled indentation during constant loading-rate testing. A characteristic of the responses is the appearance of a forward-displacing "nose" during unloading of load-controlled systems (e.g., magnetic-coil-driven "nanoindentation" systems). Even in the absence of this nose, and the associated initial negative unloading tangent, load-displacement traces (and hence inferred modulus and hardness values) are significantly perturbed on the addition of the viscous component. The viscous-elastic-plastic (VEP) model shows promise for obtaining material properties (elastic modulus, hardness, time-dependence) of time-dependent materials during indentation experiments.

Reinforcement learning with reference tracking control in continuous state spaces

The contribution described in this paper is an algorithm for learning nonlinear, reference tracking, control policies given no prior knowledge of the dynamical system and limited interaction with the system through the learning process. Concepts from the field of reinforcement learning, Bayesian statistics and classical control have been brought together in the formulation of this algorithm which can be viewed as a form of indirect self tuning regulator. On the task of reference tracking using a simulated inverted pendulum it was shown to yield generally improved performance on the best controller derived from the standard linear quadratic method using only 30 s of total interaction with the system. Finally, the algorithm was shown to work on the simulated double pendulum proving its ability to solve nontrivial control tasks. © 2011 IEEE.

Vehicle trajectory linearisation to enable efficient optimisation of the constant speed racing line

A driver model is presented capable of optimising the trajectory of a simple dynamic nonlinear vehicle, at constant forward speed, so that progression along a predefined track is maximised as a function of time. In doing so, the model is able to continually operate a vehicle at its lateral-handling limit, maximising vehicle performance. The technique used forms a part of the solution to the motor racing objective of minimising lap time. A new approach of formulating the minimum lap time problem is motivated by the need for a more computationally efficient and robust tool-set for understanding on-the-limit driving behaviour. This has been achieved through set point-dependent linearisation of the vehicle model and coupling the vehicle-track system using an intrinsic coordinate description. Through this, the geometric vehicle trajectory had been linearised relative to the track reference, leading to new path optimisation algorithm which can be formed as a computationally efficient convex quadratic programming problem. © 2012 Copyright Taylor and Francis Group, LLC.

Predictive control of a Boeing 747 aircraft using an FPGA

New embedded predictive control applications call for more eficient ways of solving quadratic programs (QPs) in order to meet demanding real-time, power and cost requirements. A single precision QP-on-a-chip controller is proposed, implemented in afield-programmable gate array (FPGA) with an iterative linear solver at its core. A novel offline scaling procedure is introduced to aid the convergence of the reduced precision solver. The feasibility of the proposed approach is demonstrated with a real-time hardware-in-the-loop (HIL) experimental setup where an ML605 FPGA board controls a nonlinear model of a Boeing 747 aircraft running on a desktop PC through an Ethernet link. Simulations show that the quality of the closed-loop control and accuracy of individual solutions is competitive with a conventional double precision controller solving linear systems using a Riccati recursion. © 2012 IFAC.

Kernelized sorting

Object matching is a fundamental operation in data analysis. It typically requires the definition of a similarity measure between the classes of objects to be matched. Instead, we develop an approach which is able to perform matching by requiring a similarity measure only within each of the classes. This is achieved by maximizing the dependency between matched pairs of observations by means of the Hilbert Schmidt Independence Criterion. This problem can be cast as one of maximizing a quadratic assignment problem with special structure and we present a simple algorithm for finding a locally optimal solution.