ℓasso MPC: Smart regulation of over-actuated systems

In this paper, a novel MPC strategy is proposed, and referred to as asso MPC. The new paradigm features an 1-regularised least squares loss function, in which the control error variance competes with the sum of input channels magnitude (or slew rate) over the whole horizon length. This cost choice is motivated by the successful development of LASSO theory in signal processing and machine learning. In the latter fields, sum-of-norms regularisation have shown a strong capability to provide robust and sparse solutions for system identification and feature selection. In this paper, a discrete-time dual-mode asso MPC is formulated, and its stability is proven by application of standard MPC arguments. The controller is then tested for the problem of ship course keeping and roll reduction with rudder and fins, in a directional stochastic sea. Simulations show the asso MPC to inherit positive features from its corresponding regressor: extreme reduction of decision variables' magnitude, namely, actuators' magnitude (or variations), with a finite energy error, being particularly promising for over-actuated systems. © 2012 AACC American Automatic Control Council).

An Error Free Passive UHF RFID System using a New Form of Wireless Signal Distribution

A wide area and error free ultra high frequency (UHF) radio frequency identification (RFID) interrogation system based on the use of multiple antennas used in cooperation to provide high quality ubiquitous coverage, is presented. The system uses an intelligent distributed antenna system (DAS) whereby two or more spatially separated transmit and receive antenna pairs are used to allow greatly improved multiple tag identification performance over wide areas. The system is shown to increase the read accuracy of 115 passive UHF RFID tags to 100% from <60% over a 10m x 8m open plan office area. The returned signal strength of the tag backscatter signals is also increased by an average of 10dB and 17dB over an area of 10m x 8m and 10m x 4m respectively. Furthermore, it is shown that the DAS RFID system has improved immunity to tag orientation. Finally, the new system is also shown to increase the tag read speed/rate of a population of tags compared with a conventional RFID system.

Generalized vector control for brushless doubly fed machines with nested-loop rotor

This paper presents a generalized vector control system for a generic brushless doubly fed (induction) machine (BDFM) with nested-loop type rotor. The generic BDFM consists of p1/p2 pole-pair stator windings and a nested-loop rotor with N number of loops per nest. The vector control system is derived based on the basic BDFM equation in the synchronous mode accompanied with an appropriate synchronization approach to the grid. An analysis is performed for the vector control system using the generic BDFM vector model. The analysis proves the efficacy of the proposed approach in BDFM electromagnetic torque and rotor flux control. In fact, in the proposed vector control system, the BDFM torque can be controlled very effectively promising a high-performance BDFM shaft speed control system. A closed-loop shaft speed control system is composed based on the presented vector control system whose performance is examined both in simulations and experiments. The results confirm the high performance of the proposed approach in BDFM shaft speed control as well as a very close agreement between the simulations and experiments. Tests are performed on a 180-frame prototype BDFM. © 2012 IEEE.

N-best error simulation for training spoken dialogue systems

A recent trend in spoken dialogue research is the use of reinforcement learning to train dialogue systems in a simulated environment. Past researchers have shown that the types of errors that are simulated can have a significant effect on simulated dialogue performance. Since modern systems typically receive an N-best list of possible user utterances, it is important to be able to simulate a full N-best list of hypotheses. This paper presents a new method for simulating such errors based on logistic regression, as well as a new method for simulating the structure of N-best lists of semantics and their probabilities, based on the Dirichlet distribution. Off-line evaluations show that the new Dirichlet model results in a much closer match to the receiver operating characteristics (ROC) of the live data. Experiments also show that the logistic model gives confusions that are closer to the type of confusions observed in live situations. The hope is that these new error models will be able to improve the resulting performance of trained dialogue systems. © 2012 IEEE.

Dopamine-dependent prediction errors underpin reward-seeking behaviour in humans.

Theories of instrumental learning are centred on understanding how success and failure are used to improve future decisions. These theories highlight a central role for reward prediction errors in updating the values associated with available actions. In animals, substantial evidence indicates that the neurotransmitter dopamine might have a key function in this type of learning, through its ability to modulate cortico-striatal synaptic efficacy. However, no direct evidence links dopamine, striatal activity and behavioural choice in humans. Here we show that, during instrumental learning, the magnitude of reward prediction error expressed in the striatum is modulated by the administration of drugs enhancing (3,4-dihydroxy-L-phenylalanine; L-DOPA) or reducing (haloperidol) dopaminergic function. Accordingly, subjects treated with L-DOPA have a greater propensity to choose the most rewarding action relative to subjects treated with haloperidol. Furthermore, incorporating the magnitude of the prediction errors into a standard action-value learning algorithm accurately reproduced subjects' behavioural choices under the different drug conditions. We conclude that dopamine-dependent modulation of striatal activity can account for how the human brain uses reward prediction errors to improve future decisions.

Adaptive regulation of vector-controlled induction motors

This paper addresses the speed and flux regulation of induction motors under the assumption that the motor parameters are poorly known. An adaptive passivity-based control is proposed that guarantees robust regulation as well as accurate estimation of the electrical parameters that govern the motor performance. This paper provides a local stability analysis of the adaptive scheme, which is illustrated by simulations and supported by a successful experimental validation on an industrial product. © 2009 IEEE.

Complementary Phone Error training

This paper introduces a novel method for the training of a complementary acoustic model with respect to set of given acoustic models. The method is based upon an extension of the Minimum Phone Error (MPE) criterion and aims at producing a model that makes complementary phone errors to those already trained. The technique is therefore called Complementary Phone Error (CPE) training. The method is evaluated using an Arabic large vocabulary continuous speech recognition task. Reductions in word error rate (WER) after combination with a CPE-trained system were obtained with up to 0.7% absolute for a system trained on 172 hours of acoustic data and up to 0.2% absolute for the final system trained on nearly 2000 hours of Arabic data.

Micro-electro-mechanical resonant tilt sensor with 250 nano-radian resolution

This paper reports a high-resolution frequency-output MEMS tilt sensor based on resonant sensing principles. The tilt sensor measures orientation by sensing the component of gravitational acceleration along a specified input axis. A combination of design enhancements enables significantly higher sensitivity for this device as compared to previously reported prototype sensors. The MEMS tilt sensor is calibrated on a manual tilt table over tilt angles ranging over 0-90 degrees with a relatively linear response measured in the range of ±20°(linearity error <2.3%) with a scale factor of approximately 50.06 Hz/degree. The noise-limited resolution of the sensor is found to be approximately 250 nano-radians for an integration time of 0.8 s, which is over an order of magnitude better than previously reported results [1]. © 2013 IEEE.

Decoupling method for vector control of the brushless doubly-fed machine

The brushless doubly-fed machine exhibits rotor-speed-dependent, cross-coupling effects between inputs and outputs when vector control is implemented. Manipulation of the model equations shows that these effects are represented by rotation angles. A parameter-independent decoupling method is presented which reduces these cross-coupling disturbances by estimating the rotation angle and applying it back to the controller. © 2013 IEEE.

Reference frame re-alignment for vector control of the brushless doubly-fed machine

Vector control provides stability and performance when applied to the brushless doubly-fed machine, however cross-coupling effects can arise between inputs and outputs. To address these effects, a procedure is proposed to both visualize and minimize the cross-coupling by means of steady-state mapping and a re-alignment of the dq reference frame. With this method implemented, gain-response tests show improved decoupling across the operating region. © 2013 EUCA.

A derivation of the source-channel error exponent using nonidentical product distributions

This paper studies the random-coding exponent of joint source-channel coding for a scheme where source messages are assigned to disjoint subsets (referred to as classes), and codewords are independently generated according to a distribution that depends on the class index of the source message. For discrete memoryless systems, two optimally chosen classes and product distributions are found to be sufficient to attain the sphere-packing exponent in those cases where it is tight. © 2014 IEEE.

Correcting the systematic error of the density functional theory calculation: the alternate combination approach of genetic algorithm and neural network

The alternate combinational approach of genetic algorithm and neural network (AGANN) has been presented to correct the systematic error of the density functional theory (DFT) calculation. It treats the DFT as a black box and models the error through external statistical information. As a demonstration, the AGANN method has been applied in the correction of the lattice energies from the DFT calculation for 72 metal halides and hydrides. Through the AGANN correction, the mean absolute value of the relative errors of the calculated lattice energies to the experimental values decreases from 4.93% to 1.20% in the testing set. For comparison, the neural network approach reduces the mean value to 2.56%. And for the common combinational approach of genetic algorithm and neural network, the value drops to 2.15%. The multiple linear regression method almost has no correction effect here.