Optimal proportional-integral-derivative control of shape memory alloy actuators using genetic algorithm and the Preisach model

Shape memory alloy (SMA) actuators, which have the ability to return to a predetermined shape when heated, have many potential applications in aeronautics, surgical tools, robotics, and so on. Although the number of applications is increasing, there has been limited success in precise motion control owing to the hysteresis effect of these smart actuators. The present paper proposes an optimization of the proportional-integral-derivative (PID) control method for SMA actuators by using genetic algorithm and the Preisach hysteresis model.

Second-Order Training of Adaptive Critics for Online Process Control

A new algorithm for training of nonlinear optimal neuro-controllers (in the form of the model-free, action-dependent, adaptive critic paradigm). Overcomes problems with existing stochastic backpropagation training: need for data storage, parameter shadowing and poor convergence, offering significant benefits for online applications.

A new Jacobian matrix for optimal learning of single-layer neural networks

This paper investigates the learning of a wide class of single-hidden-layer feedforward neural networks (SLFNs) with two sets of adjustable parameters, i.e., the nonlinear parameters in the hidden nodes and the linear output weights. The main objective is to both speed up the convergence of second-order learning algorithms such as Levenberg-Marquardt (LM), as well as to improve the network performance. This is achieved here by reducing the dimension of the solution space and by introducing a new Jacobian matrix. Unlike conventional supervised learning methods which optimize these two sets of parameters simultaneously, the linear output weights are first converted into dependent parameters, thereby removing the need for their explicit computation. Consequently, the neural network (NN) learning is performed over a solution space of reduced dimension. A new Jacobian matrix is then proposed for use with the popular second-order learning methods in order to achieve a more accurate approximation of the cost function. The efficacy of the proposed method is shown through an analysis of the computational complexity and by presenting simulation results from four different examples.

A hierarchical physical model-based approach to predictive control of a thermal power plant for efficient plant-wide disturbance rejection

Extending the work presented in Prasad et al. (IEEE Proceedings on Control Theory and Applications, 147, 523-37, 2000), this paper reports a hierarchical nonlinear physical model-based control strategy to account for the problems arising due to complex dynamics of drum level and governor valve, and demonstrates its effectiveness in plant-wide disturbance handling. The strategy incorporates a two-level control structure consisting of lower-level conventional PI regulators and a higher-level nonlinear physical model predictive controller (NPMPC) for mainly set-point manoeuvring. The lower-level PI loops help stabilise the unstable drum-boiler dynamics and allow faster governor valve action for power and grid-frequency regulation. The higher-level NPMPC provides an optimal load demand (or set-point) transition by effective handling of plant-wide interactions and system disturbances. The strategy has been tested in a simulation of a 200-MW oil-fired power plant at Ballylumford in Northern Ireland. A novel approach is devized to test the disturbance rejection capability in severe operating conditions. Low frequency disturbances were created by making random changes in radiation heat flow on the boiler-side, while condenser vacuum was fluctuating in a random fashion on the turbine side. In order to simulate high-frequency disturbances, pulse-type load disturbances were made to strike at instants which are not an integral multiple of the NPMPC sampling period. Impressive results have been obtained during both types of system disturbances and extremely high rates of load changes, right across the operating range, These results compared favourably with those from a conventional state-space generalized predictive control (GPC) method designed under similar conditions.

Reducing quantum control for spin-spin entanglement distribution

We present a protocol that sets maximum stationary entanglement between remote spins through scattering of mobile mediators without initialization, post-selection or feedback of the mediators' state. No time-resolved tuning is needed and, counterintuitively, the protocol generates two-qubit singlet states even when classical mediators are used. The mechanism responsible for this effect is resilient against non-optimal coupling strengths and dephasing affecting the spins. The scheme uses itinerant particles and scattering centres and can be implemented in various settings. When quantum dots and photons are used a striking result is found: injection of classical mediators, rather than quantum ones, improves the scheme efficiency.

The control of wave energy converters using active bipolar damping

A novel method for controlling wave energy converters using active bipolar damping is described and compared with current control methods. The performance of active bipolar damping is modelled numerically for two distinct types of wave energy converter and it is found that in both cases the power capture can be significantly increased relative to optimal linear damping. It is shown that this is because active bipolar damping has the potential for providing a quasi-spring or quasi-inertia, which improves the wave energy converter's tuning and amplitude of motion, resulting in the increase in power capture observed. The practical implementation of active bipolar damping is also discussed. It is noted that active bipolar damping does not require a reactive energy store and thereby reduces the cost and eliminates losses due to the cycling of reactive energy. It is also noted that active bipolar damping could be implemented using a single constant pressure double-acting hydraulic cylinder and so potentially represents a simple, efficient, robust and economic solution to the control of wave energy converters.

Numerical Investigation of Optimal Pin Actuator Location on a Supersonic Projectile

The flowfield around a supersonic projectile using a pin actuator control method has been predicted using computational fluid dynamics. It has been predicted using both viscous and inviscid methods for a number of positions. Both methods showed that an optimal longitudinal position exists. However, the inviscid model over-predicted the lateral acceleration due to the difference in shock formation around the pin between the two approaches. The optimal location was predicted independent of solver, however the higher-fidelity solver predicted lower achievable lateral accelerations. This is due to the viscous interactions caused by the pin. The effect of projectile orientation has shown that shielding the pin leads to reduced effectiveness due to the wake of the fin enveloping the pin. When the pin is exposed to onset flow, the forces achieved are increased. There is also an increase in the achievable forces and moments with increasing Mach number.

Cloning transformations in spin networks without external control

In this paper we present an approach to quantum cloning with unmodulated spin networks. The cloner is realized by a proper design of the network and a choice of the coupling between the qubits. We show that in the case of phase covariant cloner the XY coupling gives the best results. In the 1 -> 2 cloning we find that the value for the fidelity of the optimal cloner is achieved, and values comparable to the optimal ones in the general N -> M case can be attained. If a suitable set of network symmetries are satisfied, the output fidelity of the clones does not depend on the specific choice of the graph. We show that spin network cloning is robust against the presence of static imperfections. Moreover, in the presence of noise, it outperforms the conventional approach. In this case the fidelity exceeds the corresponding value obtained by quantum gates even for a very small amount of noise. Furthermore, we show how to use this method to clone qutrits and qudits. By means of the Heisenberg coupling it is also possible to implement the universal cloner although in this case the fidelity is 10% off that of the optimal cloner.

Modelling and Control of Shape Memory Alloy Actuators by using Preisach Model, Genetic Algorithm and Fuzzy Logic

Shapememoryalloy (SMA) actuators, which have the ability to return to a predetermined shape when heated, have many potential applications in aeronautics, surgical tools, robotics and so on. Nonlinearity hysteresis effects existing in SMA actuators present a problem in the motion control of these smart actuators. This paper investigates the control problem of SMA actuators in both simulation and experiment. In the simulation, the numerical Preisachmodel with geometrical interpretation is used for hysteresis modeling of SMA actuators. This model is then incorporated in a closed loop PID control strategy. The optimal values of PID parameters are determined by using geneticalgorithm to minimize the mean squared error between desired output displacement and simulated output. However, the control performance is not good compared with the simulation results when these parameters are applied to the real SMA control since the system is disturbed by unknown factors and changes in the surrounding environment of the system. A further automated readjustment of the PID parameters using fuzzylogic is proposed for compensating the limitation. To demonstrate the effectiveness of the proposed controller, real time control experiment results are presented.

Help needed in medication self-management for people with visual impairment: case-control study

Background: Visual impairment (VI) is rising in prevalence and contributing to increasing morbidity, particularly among older people. Understanding patients' problems is fundamental to achieving optimal health outcomes but little is known about how VI impacts on self-management of medication.

Aim: To compare issues relating to medication self-management between older people with and without VI.

Design and setting: Case-control study with participants aged =65 years, prescribed at least two long-term oral medications daily, living within the community.

Method: The study recruited 156 patients with VI (best corrected visual acuity [BCVA] 6/18 to 3/60) at low-vision clinics; community optometrists identified 158 controls (BCVA 6/9 or better). Researchers visited participants in their homes, administered two validated questionnaires to assess medication adherence (Morisky; Medication Adherence Report Scale [MARS]), and asked questions about medication self-management, beliefs, and support.

Results: Approximately half of the participants in both groups reported perfect adherence on both questionnaires (52.5% Morisky; 43.3%, MARS). Despite using optical aids, few (3%) with VI could read medication information clearly; 24% had difficulty distinguishing different tablets. More people with VI (29%) than controls (13%) (odds ratio [OR] = 2.8; 95% confidence interval [CI] = 1.6 to 5.0) needed help managing their medication, from friends (19% versus 10%) or pharmacists (10% versus 2.5%; OR = 4.4, 95% CI = 1.4 to 13.5); more received social service support (OR = 7.1; 95% CI = 3.9 to 12.9).

Conclusion: Compared to their peers without VI, older people with VI are more than twice as likely to need help in managing medication. In clinical practice in primary care, patients' needs for practical support in taking prescribed treatment must be recognised. Strategies for effective medication self-management should be explored.

Entanglement control via reservoir engineering in ultracold atomic gases

We study the entanglement of two impurity qubits immersed in a Bose-Einstein condensate (BEC) reservoir. This open quantum system model allows for interpolation between a common dephasing scenario and an independent dephasing scenario by modifying the wavelength of the superlattice superposed to the BEC, and how this influences the dynamical properties of the impurities. We demonstrate the existence of rich dynamics corresponding to different values of reservoir parameters, including phenomena such as entanglement trapping, revivals of entanglement, and entanglement generation. In the spirit of reservoir engineering, we present the optimal BEC parameters for entanglement generation and trapping, showing the key role of the ultracold-gas interactions. Copyright (C) EPLA, 2013

Effect of serviceability limits on optimal design of steel portal frames

The design of hot-rolled steel portal frames can be sensitive to serviceability deflection limits. In such cases, in order to reduce frame deflections, practitioners increase the size of the eaves haunch and / or the sizes of the steel sections used for the column and rafter members of the frame. This paper investigates the effect of such deflection limits using a real-coded niching genetic algorithm (RC-NGA) that optimizes frame weight, taking into account both ultimate as well as serviceability limit states. The results show that the proposed GA is efficient and reliable. Two different sets of serviceability deflection limits are then considered: deflection limits recommended by the Steel Construction Institute (SCI), which is based on control of differential deflections, and other deflection limits based on suggestions by industry. Parametric studies are carried out on frames with spans ranging between 15 m to 50 m and column heights between 5 m to 10 m. It is demonstrated that for a 50 m span frame, use of the SCI recommended deflection limits can lead to frame weights that are around twice as heavy as compared to designs without these limits.

Establishing optimal quantitative-polymerase chain reaction assays for routine diagnosis and tracking of minimal residual disease in JAK2-V617F-associated myeloproliferative neoplasms:a joint European LeukemiaNet/MPN&MPNr-EuroNet (COST action BM0902) study

Reliable detection of JAK2-V617F is critical for accurate diagnosis of myeloproliferative neoplasms (MPNs); in addition, sensitive mutation-specific assays can be applied to monitor disease response. However, there has been no consistent approach to JAK2-V617F detection, with assays varying markedly in performance, affecting clinical utility. Therefore, we established a network of 12 laboratories from seven countries to systematically evaluate nine different DNA-based quantitative PCR (qPCR) assays, including those in widespread clinical use. Seven quality control rounds involving over 21,500 qPCR reactions were undertaken using centrally distributed cell line dilutions and plasmid controls. The two best-performing assays were tested on normal blood samples (n=100) to evaluate assay specificity, followed by analysis of serial samples from 28 patients transplanted for JAK2-V617F-positive disease. The most sensitive assay, which performed consistently across a range of qPCR platforms, predicted outcome following transplant, with the mutant allele detected a median of 22 weeks (range 6-85 weeks) before relapse. Four of seven patients achieved molecular remission following donor lymphocyte infusion, indicative of a graft vs MPN effect. This study has established a robust, reliable assay for sensitive JAK2-V617F detection, suitable for assessing response in clinical trials, predicting outcome and guiding management of patients undergoing allogeneic transplant.