Maximum-likelihood estimation of delta-domain model parameters from noisy output signals

Fast sampling is desirable to describe signal transmission through wide-bandwidth systems. The delta-operator provides an ideal discrete-time modeling description for such fast-sampled systems. However, the estimation of delta-domain model parameters is usually biased by directly applying the delta-transformations to a sampled signal corrupted by additive measurement noise. This problem is solved here by expectation-maximization, where the delta-transformations of the true signal are estimated and then used to obtain the model parameters. The method is demonstrated on a numerical example to improve on the accuracy of using a shift operator approach when the sample rate is fast

Variable neural networks for adaptive control of nonlinear systems

This paper is concerned with the adaptive control of continuous-time nonlinear dynamical systems using neural networks. A novel neural network architecture, referred to as a variable neural network, is proposed and shown to be useful in approximating the unknown nonlinearities of dynamical systems. In the variable neural networks, the number of basis functions can be either increased or decreased with time, according to specified design strategies, so that the network will not overfit or underfit the data set. Based on the Gaussian radial basis function (GRBF) variable neural network, an adaptive control scheme is presented. The location of the centers and the determination of the widths of the GRBFs in the variable neural network are analyzed to make a compromise between orthogonality and smoothness. The weight-adaptive laws developed using the Lyapunov synthesis approach guarantee the stability of the overall control scheme, even in the presence of modeling error(s). The tracking errors converge to the required accuracy through the adaptive control algorithm derived by combining the variable neural network and Lyapunov synthesis techniques. The operation of an adaptive control scheme using the variable neural network is demonstrated using two simulated example

Parameter estimation and inference for stochastic reaction-diffusion systems: application to morphogenesis in D. melanogaster

Background: Reaction-diffusion systems are frequently used in systems biology to model developmental and signalling processes. In many applications, count numbers of the diffusing molecular species are very low, leading to the need to explicitly model the inherent variability using stochastic methods. Despite their importance and frequent use, parameter estimation for both deterministic and stochastic reaction-diffusion systems is still a challenging problem. Results: We present a Bayesian inference approach to solve both the parameter and state estimation problem for stochastic reaction-diffusion systems. This allows a determination of the full posterior distribution of the parameters (expected values and uncertainty). We benchmark the method by illustrating it on a simple synthetic experiment. We then test the method on real data about the diffusion of the morphogen Bicoid in Drosophila melanogaster. The results show how the precision with which parameters can be inferred varies dramatically, indicating that the ability to infer full posterior distributions on the parameters can have important experimental design consequences. Conclusions: The results obtained demonstrate the feasibility and potential advantages of applying a Bayesian approach to parameter estimation in stochastic reaction-diffusion systems. In particular, the ability to estimate credibility intervals associated with parameter estimates can be precious for experimental design. Further work, however, will be needed to ensure the method can scale up to larger problems

New insights for applications of Kreisselmeier's structure in robust and fault tolerant control

This paper addresses a two degree of freedom structure discussed by Kreisselmeier for the SISO case in 1999. The discussion herein considers a MIMO setting, and aims at the use of this control topology for robust and fault tolerant control. It is also shown how design barriers can be obtained for robust I/O transfer behavior assignment and robustness evaluation schemes can be devised which allow for the quantitative valuation of I/O transfer behavior degradation in the presence of plant model uncertainty. The concepts and techniques are illustrated and assessed using an in-flight simulation problem

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

Power sharing of parallel operated DC-DC converters using current-limiting droop control

In this paper, a nonlinear current-limiting droop controller is proposed to achieve accurate power sharing among parallel operated DC-DC boost converters in a DC micro-grid application. In particular, the recently developed robust droop controller is adopted and implemented as a dynamic virtual resistance in series with the inductance of each DC-DC boost converter. Opposed to the traditional approaches that use small-signal modeling, the proposed control design takes into account the accurate nonlinear dynamic model of each converter and it is analytically proven that accurate power sharing can be accomplished with an inherent current limitation for each converter independently using input-to-state stability theory. When the load requests more power that exceeds the capacity of the converters, the current-limiting capability of the proposed control method protects the devices by limiting the inductor current of each converter below a given maximum value. Extensive simulation results of two paralleled DC-DC boost converters are presented to verify the power sharing and current-limiting properties of the proposed controller under several changes of the load

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