Lyndon J. Brown

Periodic disturbance cancellation with uncertain frequency

This paper presents a new algorithm to cancel periodic disturbances with uncertain frequency. The disturbances are cancelled using an internal model structure with adaptive frequency, in parallel with a stabilizing controller. The time-varying internal model controllers states, in steady state, can be mapped to two time-invariant variables: the magnitude or energy of the internal model and frequency of the disturbance. An additional integral controller then can be used to reduce the difference between the internal model controller (IMC) and disturbance frequency to zero. The stability of the feedback control system with this algorithm and convergence of the algorithm to the correct frequency with exact disturbance cancellation are justified by singular perturbation and averaging theories. The algorithm is locally exponentially stable, rather than asymptotically stable. Simulations demonstrate the performance of the algorithm, the ability of this algorithm to identify the frequency of periodic disturbances and to reject periodic disturbances with uncertain frequency.

Identification of periodic signals with uncertain frequency

This paper presents a new algorithm to identify periodic signals with uncertain frequency. The approach is based on the behavior of an internal model in an error feedback system. As such, the signal is fed to a fictitious plant with a feedback controller. The feedback controller is based on a traditional controller in parallel with an internal model which identifies and cancels the periodic disturbances. Under ideal circumstances, the phase plot of the states of the internal model form an ellipse. The speed of rotation about this ellipse can be used to calculate the difference between the nominal frequency of the model and the true frequency of the periodic signal. An integral controller or a least-squares estimator can be used to drive this error to zero. Simulations demonstrate the validity of this approach with time-varying frequency, and the algorithm is then applied to some data collected from a spot welder that has been corrupted by a sinusoidal signal whose frequency is between 1 Hz and 5 KHz.

Relationship between size and latency of action potentials in human muscle sympathetic nerve activity

We employed a novel action potential detection and classification technique to study the relationship between the recruitment of sympathetic action potentials (i.e., neurons) and the size of integrated sympathetic bursts in human muscle sympathetic nerve activity (MSNA). Multifiber postganglionic sympathetic nerve activity from the common fibular nerve was collected using microneurography in 10 healthy subjects at rest and during activation of sympathetic outflow using lower body negative pressure (LBNP). Burst occurrence increased with LBNP. Integrated burst strength (size) varied from 0.22 ± 0.07 V at rest to 0.28 ± 0.09 V during LBNP. Sympathetic burst size (i.e., peak height) was directly related to the number of action potentials within a sympathetic burst both at baseline (r = 0.75 ± 0.13; P < 0.001) and LBNP (r = 0.75 ± 0.12; P < 0.001). Also, the amplitude of detected action potentials within sympathetic bursts was directly related to the increased burst size at both baseline (r = 0.59 ± 0.16; P < 0.001) and LBNP (r = 0.61 ± 0.12; P < 0.001). In addition, the number of detected action potentials and the number of distinct action potential clusters within a given sympathetic burst were correlated at baseline (r = 0.7 ± 0.1; P < 0.001) and during LBNP (r = 0.74 ± 0.03; P < 0.001). Furthermore, action potential latency (i.e., an inverse index of neural conduction velocity) was decreased as a function of action potential size at baseline and LBNP. LBNP did not change the number of action potentials and unique clusters per sympathetic burst. It was concluded that there exists a hierarchical pattern of recruitment of additional faster conducting neurons of larger amplitude as the sympathetic bursts become stronger (i.e., larger amplitude bursts). This fundamental pattern was evident at rest and was not altered by the level of baroreceptor unloading applied in this study.

Spike detection in human muscle sympathetic nerve activity using a matched wavelet approach

Sympathetic nerve recordings associated with blood pressure regulation can be recorded directly using microneurography. A general characteristic of this signal is spontaneous burst activity of spikes (action potentials) separated by silent periods against a background of considerable Gaussian noise. During measurement with electrodes, the raw muscle sympathetic nerve activity (MSNA) signal is amplified, band-pass filtered, rectified and integrated. This integration process removes information regarding action potential content and their discharge properties. This paper proposes a new method for detecting action potentials from the raw MSNA signal to enable investigation of post-ganglionic neural discharge properties. The new method is based on the design of a mother wavelet that is matched to an actual mean action potential template extracted from a real raw MSNA signal. To detect action potentials, the new matched wavelet is applied to the MSNA signal using a continuous wavelet transform following a thresholding procedure and finding of a local maxima that indicates the location of action potentials. The performance of the proposed method versus two previous wavelet-based approaches was evaluated using (1) real MSNA recorded from seven healthy participants and, (2) simulated MSNA. The results show that the new matched wavelet performs better than the previous wavelet-based methods that use a non-matched wavelet in detecting action potentials in the MSNA signal.

Adaptive dead-time compensation with application to a robotic welding system

The paper proposes a paradigm for control design suitable for poorly modeled plants with significant dead-time. An adaptive dead-time compensator is developed which is substantially different to the standard Smith predictor, and more generally applicable in practice. A framework is developed for selecting the controller parameters using now standard design techniques such as /spl mu/-synthesis. It is argued that adaptation allows for exact set point matching without the need for integration in the control law. Stability and convergence results are established for the resulting closed-loop system equations. The proposed adaptive dead-time compensator is compared with both a standard robust controller and an adaptive pole placement controller through experiments on a gas metal arc welding testbed.

A multiple Lyapunov functions approach for stability of switched systems

In this paper, a multiple Lyapunov functions based approach is presented for the stability analysis for switched systems. Compared with existing Multiple Lyapunov functions approaches in the literature, this approach requires less conservative conditions on the constituent subsystems. The stability theorem proposed here gives not only Lyapunov stability, but asymptotic stability as well. In particular, the result presented here allows the switched system to have different states, and for some of the subsystems to be unstable or to lack a common equilibrium point.

Performance analysis of stationary and discrete wavelet transform for action potential detection from sympathetic nerve recordings in humans

Accurate investigation of the sympathetic nervous system is important in the diagnosis and study of various autonomic and cardiovascular control and disorders. Sympathetic function associated with blood pressure regulation in humans can be evaluated by recording muscle sympathetic nerve activity (MSNA), which is characterised by synchronous neuronal discharges separated by periods of neural silence dominated by colored gaussian noise. In this paper two common methods for detecting filtered action potential in MSNA recordings is compared. These methods are based on stationary wavelet transform (SWT) and discrete wavelet transform (DWT). The performance analysis are evaluated using simulated MSNA using templates extracted from real MSNA recorded from three healthy subjects.

Bayesian adaptive control of time varying systems

Linear stochastic systems corrupted by Gaussian white noise disturbances are considered. An elementary proof of boundedness of the mean square response under a certainty equivalence minimum variance control law is described. The assumptions used in this ideal case indicate that the usual smallness in the mean conditions used in adaptive control could be overly restrictive. The methodology leads naturally to new performance bounds for such time-varying systems. Some simulations are presented to verify the results and to test the tightness of the assumptions used.<<ETX>>

Identification of Exponentially Damped Sinusoidal Signals

Abstract A discrete-time internal model principle based adaptive algorithm for identifying signals composed of a sum of exponentially damped sinusoids is presented. The time varying state variables of an internal model principle controller in a feedback loop can provide estimates of the exponentially damped sinusoidal signal parameters, the damping factor and the frequency. By using additional integral controllers, the estimation errors can be eliminated. The convergence of the proposed algorithm is justified using discrete-time averaging theory. Simulation results demonstrate the performance of this algorithm for signal identification.

Fast estimation of power system frequency using adaptive internal-model control technique

This paper presents a new approach to the fast estimation of power system frequency. The approach is adopted from the application of internal model based periodic disturbance cancellation technique in the control field. Frequency can be estimated by feeding the power system signals into a control system with an internal model incorporated in the feedback loop. After the first about 20 ms convergence, the approach is able to provide accurate noise-free estimates in less than 10 ms despite the presence of harmonics and DC decay. The estimation performance has been improved by 50 percent by introducing a notch filter to the adaptation loop. Computation requirements for the proposed method are very low compared to existing methods. The design of the control system is also described in the paper. Simulations are conducted using computer synthesized signals.