Person-specific gesture set selection for optimised movement classification from EMG signals

© 2016 IEEE.Movement classification from electromyography (EMG) signals is a promising vector for improvement of human computer interaction and prosthetic control. Conventional work in this area typically makes use of expert knowledge to select a set of movements a priori and then design classifiers based around these movements. The disadvantage of this approach is that different individuals might have different sets of movements that would lead to high classification accuracy. The novel approach we take here is to instead use a data-driven diagnostic test to select a set of person-specific movements. This new approach leads to an optimised set of movements for a specific person with regards to classification performance

Design of a UDE Frequency Selective Filter to Reject Periodical Disturbances

In this paper a new filter design for the Uncertainty and Disturbance Estimator (UDE) is proposed to reject periodical disturbances when a limited bandwidth is required for the control output. The motivation comes from several applications where the system actuator may introduce a bandwidth limitation, as a result of internal delays, or when the actuator itself is a limited bandwidth closed-loop system. When the traditional UDE approach is applied in these systems, the stability requirements impose a limitation over the effective bandwidth of the UDE filter and therefore disturbances cannot be fully rejected by the filter. In the case where the expected disturbance is periodical with a known fundamental frequency, the proposed UDE filter is designed as a chain of filters to match selected bands of the expected disturbance spectrum and fully reject them while maintaining the desired stability margins. A design example of a power inverter application is investigated and extensive simulation results are provided to verify the proposed UDE filter design

Dynamic Structure Neural Networks for Stable Adaptive Control of Nonlinear Systems

An adaptive control technique, using dynamic structure Gaussian radical basis function neural networks, that grow in time according to the location of the system's state in space is presented for the affine class of nonlinear systems having unknown or partially known dynamics. The method results in a network that is economic in terms of network size, for cases where the state spans only a small subset of state space, by utilising less basis functions than would have been the case if basis functions were centred on discrete locations covering the whole, relevant region of state space. Additionally, the system is augmented with sliding control so as to ensure global stability if and when the state moves outside the region of state-space spanned by the basis functions and to ensure robustness to disturbances that arise due to the network inherent approximation errors and to the fact that for limiting the network size, a minimal number of basis functions are actually being used. Adaptation laws and sliding control gains that ensure system ability in a Lyapunov sense are presented, together with techniques for determining which basis functions are to form part of the network structure. The effectiveness of the method is demonstrated by experiment simulations

Nonlinear Adaptive Control Using Gaussian Networks with Composite Adaptation for Improved Convergence

The use of composite adaptive laws for control of the affine class of nonlinear systems having unknown dynamics is proposed. These dynamics are approximated by Gaussian radial basis function neural network whose parameters are updated by a composite law that is driven by both tracking and estimation errors, combining techniques used in direct and indirect adaptive control. This is motivated by the need to improve the speed of convergence of the unknown parameters, hence resulting in a better system performance. The inherent approximation error of the neural networks might lead to instability because of parameter drift. This is compensated for by augmenting the control law with a low gain sliding mode component and using deadzone adaptation for the indirect part of the composite law, The stability of the system is analysed and the effectiveness of the method is demonstrated by simulation and comparison with a direct adaptive control scheme

Dual and Adaptive Control of Nonlinear Stochastic Systems Using Neural Networks

A suboptimal dual adaptive system is developed for control of stochastic, nonlinear, discrete-time plants that are affine in the control input. The nonlinear functions are assumed to be unknown and neural networks are used to approximate them. Both Gaussian radial basis function and sigmodial multilayer perceptron neural networks are considered and parameter adjustment is based on Kalman filtering techniques. The result is a control law that takes into consideration the uncertainty of the parameter estimates, thereby, eliminating the need of performing prior open-loop plant identification. The performance of the system is analysed by simulation and Monte Carlo analysis and the advantages of the scheme are clearly outlined

A material system with integrated fault diagnosis and feedback controlled self-healing

Two significant drawbacks of current self-healing materials are that they are: (1) Passive and as such do not guarantee a match between the healing and damage rate; (2) Not monitored during and after healing, so that the performance of the healed material is not known without retrospective offline testing. As a consequence their application is currently limited in some sectors, such as the aerospace sector where high performance needs to be guaranteed within strict guidelines. This article proposes the first active self-healing material that integrates with control and fault diagnosis to provide a system with a desired healing response. A fault diagnosis algorithm using supervised regression is used to estimate the measure of damage. Then based on this estimate, adaptive feedback control is used to ensure a match between the healing response and the damage rate, while taking into account the nonlinear system dynamics and uncertainty. The system is demonstrated in simulation using a self-healing material based on piezoelectricity and electrolysis. This shows the ability of the integrated subsystems to tackle these two significant drawbacks of most current self-healing systems and will benefit applications with strict performance requirements, or systems operating under harsh conditions or that are remotely accessed

Rumination syndrome: Assessment of vagal tone during and after meals and during diaphragmatic breathing

Background: Pathophysiology of rumination syndrome (RS) is not well understood. Treatment with diaphragmatic breathing improves rumination syndrome. The aim of the study was to characterize vagal tone in patients with rumination syndrome during and after meals and during diaphragmatic breathing. Methods: We prospectively recruited 10 healthy volunteers (HV) and 10 patients with RS. Subjects underwent measurement of vagal tone using heart rate variability. Vagal tone was measured during baseline, test meal and intervention (diaphragmatic (DiaB), slow deep (SlowDB), and normal breathing). Vagal tone was assessed using mean values of root mean square of successive differences (RMSSD), and area under curves (AUC) were calculated for each period. We compared baseline RMSSD, the AUC and meal‐induced discomfort scores between HV and RS. Furthermore, we assessed the effect of respiratory exercises on symptom scores, and number of rumination episodes. Key Results: There was no significant difference in baseline vagal tone between HV and RS. During the postprandial period, there was a trend to higher vagal tone in RS, but not significantly (P > .2 for all). RS had the higher total symptom scores than HV (P < .011). In RS, only DiaB decreased the number of rumination episodes during the intervention period (P = .028), while both DiaB and SlowDB increased vagal tone (P < .05 for both). The symptom scores with the 3 breathing exercises showed very similar trends. Conclusions and inferences: Patients with RS do not have decreased vagal tone related to meals. DiaB reduced number of rumination events by a mechanism not related to changes in vagal tone

A data-driven approach for predicting printability in metal additive manufacturing processes

Metal powder-bed fusion additive manufacturing technologies offer numerous benefits to the manufacturing industry. However, the current approach to printability analysis, determining which components are likely to build unsuccessfully, prior to manufacture, is based on ad-hoc rules and engineering experience. Consequently, to allow full exploitation of the benefits of additive manufacturing, there is a demand for a fully systematic approach to the problem. In this paper we focus on the impact of geometry in printability analysis. For the first time, we detail a machine learning framework for determining the geometric limits of printability in additive manufacturing processes. This framework consists of three main components. First, we detail how to construct strenuous test artefacts capable of pushing an additive manufacturing process to its limits. Secondly, we explain how to measure the printability of an additively manufactured test artefact. Finally, we construct a predictive model capable of estimating the printability of a given artefact before it is additively manufactured. We test all steps of our framework, and show that our predictive model approaches an estimate of the maximum performance obtainable due to inherent stochasticity in the underlying additive manufacturing process. © 2020, The Author(s)

Computational system identification of continuous-time nonlinear systems using approximate Bayesian computation

In this paper, we derive a system identification framework for continuous-time nonlinear systems, for the first time using a simulation-focused computational Bayesian approach. Simulation approaches to nonlinear system identification have been shown to outperform regression methods under certain conditions, such as non-persistently exciting inputs and fast-sampling. We use the approximate Bayesian computation (ABC) algorithm to perform simulation-based inference of model parameters. The framework has the following main advantages: (1) parameter distributions are intrinsically generated, giving the user a clear description of uncertainty, (2) the simulation approach avoids the difficult problem of estimating signal derivatives as is common with other continuous-time methods, and (3) as noted above, the simulation approach improves identification under conditions of non-persistently exciting inputs and fast-sampling. Term selection is performed by judging parameter significance using parameter distributions that are intrinsically generated as part of the ABC procedure. The results from a numerical example demonstrate that the method performs well in noisy scenarios, especially in comparison to competing techniques that rely on signal derivative estimation

A century of variation in the dependence of Greenland iceberg calving on ice sheet surface mass balance and regional climate change

Iceberg calving is a major component of the total mass balance of the Greenland ice sheet (GrIS). A century-long record of Greenland icebergs comes from the International Ice Patrol's record of icebergs (I48N) passing latitude 48° N, off Newfoundland. I48N exhibits strong interannual variability, with a significant increase in amplitude over recent decades. In this study, we show, through a combination of nonlinear system identification and coupled ocean-iceberg modelling, that I48N's variability is predominantly caused by fluctuation in GrIS calving discharge rather than open ocean iceberg melting. We also demonstrate that the episodic variation in iceberg discharge is strongly linked to a nonlinear combination of recent changes in the surface mass balance (SMB) of the GrIS and regional atmospheric and oceanic climate variability, on the scale of the previous 1-3 years, with the dominant causal mechanism shifting between glaciological (SMB) and climatic (ocean temperature) over time. We suggest that this is a change in whether glacial run-off or under-ice melting is dominant, respectively. We also suggest that GrIS calving discharge is episodic on at least a regional scale and has recently been increasing significantly, largely as a result of west Greenland sources. © 2014 The Author(s) Published by the Royal Society. All rights reserved