Estimation of oxygen consumption for moderate exercises by using a hammerstein model

This paper aims to establish block-structured nonlinear model (Hammerstein model) to predict oxygen uptake during moderate treadmill exercises. In order to model the steady state relationship between oxygen uptake (oxygen consumption) and walking speed, six healthy male subjects walked on a motor driven treadmill at six different speed (2,3,4,5,6, and 7 km/h). The averaged oxygen uptake of exercisers at steady state was measured by a mixing chamber based gas analyzer(AEI Moxus Metabolic Cart). Based on these reliable experiment data, a nonlinear static function was obtained by using Support Vector Regression. In order to capture the dynamics of oxygen uptake, a suitable Pseudo Random Binary Signal (PRBS) input was designed and implemented on a computer controlled treadmill. Breath by breath analysis of all exercisers' dynamic responses (PRBS responses) to treadmill walking was performed. A useful ARX model is identified to justify the measured oxygen uptake dynamics within the aerobic range. Finally, a Hammerstein is achieved, which is useful for the control system design of oxygen uptake regulation during treadmill exercises. © 2006 IEEE

Oxygen uptake estimation in humans during exercise using a Hammerstein model

This paper aims to establish a block-structured model to predict oxygen uptake in humans during moderate treadmill exercises. To model the steady state relationship between oxygen uptake (oxygen consumption) and walking speed, six healthy male subjects walked on a motor driven treadmill with constant speed from 2 to 7 km/h. The averaged oxygen uptake at steady state (VO 2) was measured by a mixing chamber based gas analysis and ventilation measurement system (AEI Moxus Metabolic Cart). Based on these reliable date, a nonlinear steady state relationship was successfully established using Support Vector Regression methods. In order to capture the dynamics of oxygen uptake, the treadmill velocity was modulated using a Pseudo Random Binary Signal (PRBS) input. Breath by breath analysis of all subjects was performed. An ARX model was developed to accurately reproduce the measured oxygen uptake dynamics within the aerobic range. Finally, a Hammerstein model was developed, which may be useful for implementing a control system for the regulation of oxygen uptake during treadmill exercises. © 2007 Biomedical Engineering Society

Predictor development for controlling real-time applications over the Internet

Over the past decade there has been a growing demand for interactive multimedia applications deployed over public IP networks. To achieve acceptable Quality of Ser- vice (QoS) without significantly modifying the existing infrastructure, the end-to-end applications need to optimize their behavior and adapt according to network char- acteristics. Most existing application optimization techniques are based on reactive strategies, i.e. reacting to occurrences of congestion. We propose the use of predic- tive control to address the problem in an anticipatory manner. This research deals with developing models to predict end-to-end single flow characteristics of Wide Area Networks (WANs). A novel signal, in the form of single flow packet accumulation, is proposed for feedback purposes. This thesis presents a variety of effective predictors for the above signal using Auto-Regressive (AR) models, Radial Basis Functions (RBF) and Sparse Basis Functions (SBF). The study consists of three sections. We first develop time- series models to predict the accumulation signal. Since encoder bit-rate is the most logical and generic control input, a statistical analysis is conducted to analyze the effect of input bit-rate on end-to-end delay and the accumulation signal. Finally, models are developed using this bit-rate as an input to predict the resulting accu- mulation signal. The predictors are evaluated based on Noise-to-Signal Ratio (NSR) along with their accuracy with increasing accumulation levels. In time-series models, RBF gave the best NSR closely followed by AR models. Analysis based on accu- racy with increasing accumulation levels showed AR to be better in some cases. The study on effect of bit-rate revealed that bit-rate may not be a good control input on all paths. Models such as Auto-Regressive with Exogenous input (ARX) and RBF were used to develop models to predict the accumulation signal using bit-rate as a modeling input. ARX and RBF models were found to give comparable accuracy, with RBF being slightly better

Identification and control for heart rate regulation during treadmill exercise

This paper proposes a novel integrated approach for the identification and control of Hammerstein systems to achieve desired heart rate profile tracking performance for an automated treadmill system. For the identification of Hammerstein systems, the pseudorandom binary sequence input is employed to decouple the identification of dynamic linear part from input nonlinearity. The powerful ε-insensitivity support vector regression method is adopted to obtain sparse representations of the inverse of static nonlinearity in order to obtain an approximate linear model of the Hammerstein system. An H ∞ controller is designed for the approximated linear model to achieve robust tracking performance. This new approach is successfully applied to the design of a computer-controlled treadmill system for the regulation of heart rate during treadmill exercise. Minimizing deviations of heart rate from a preset profile is achieved by controlling the speed of the treadmill. Both conventional proportional-integral-derivative (PID) control and the proposed approaches have been employed for the controller design. The proposed algorithm achieves much better heart rate tracking performance. © 2007 IEEE

Development and identification of hierarchical nonlinear mixed effects models for the analysis of dynamic systems: identification and application of hierarchical nonlinear mixed effects models for the determination of steady-state and dynamic torque responses of an SI engine

Multi-level or hierarchical models present various features for dealing with data grouped at several levels. The majority of applications of hierarchical models use clustered data that is static in nature and collected over a long period of time. The purpose of this study is investigating hierarchical models for application with highly dynamic systems. Steady-state data are conventionally employed for engine torque mapping purposes. The data takes much time to collect and the dynamics of the system are routinely ignored. This valuable information could be used for better control of the system.In this study, an innovative transient spark-sweep approach is developed for collecting dynamic torque data more efficiently. The means of data collection implies a structure for which a multi-level model is best suited. A multi-model augmented D-optimal design is created, and the experimental data collected. Spark excitation is applied at speed/load points using Amplitude Modulated Pseudo Random Signal (AMPRS), and the torque response over the operating space is thus obtained. Conditional first-order linearization is used within the identification process for determining the hierarchical model parameters. The level-1 Nonlinear Auto Regressive eXogenous (NARX) models are separately determined using an Iterative Generalized Least Square (IGLS) method and the results are employed for initialisation of the covariance matrix and the model level-2 parameters. A novel gradient optimiser was established to facilitate the dynamic hierarchical model identification. Additionally, the uncertainty associated with model selection was mitigated using a multi-model approach. The model identified is evaluated and compared with experimental dynamic and steady-state data. It shows behaviour, both dynamic and steady state, providing prediction over a wider extrapolated spark range than conventional approaches. The new approach is eight time faster than current state-of-the-art approaches.</div

Nonparametric Hammerstein model based model predictive control for heart rate regulation.

This paper proposed a novel nonparametric model based model predictive control approach for the regulation of heart rate during treadmill exercise. As the model structure of human cardiovascular system is often hard to determine, nonparametric modelling is a more realistic manner to describe complex behaviours of cardiovascular system. This paper presents a new nonparametric Hammerstein model identification approach for heart rate response modelling. Based on the pseudo-random binary sequence experiment data, we decouple the identification of linear dynamic part and input nonlinearity of the Hammerstein system. Correlation analysis is applied to acquire step response of linear dynamic component. Support Vector Regression is adopted to obtain a nonparametric description of the inverse of input static nonlinearity that is utilized to form an approximate linear model of the Hammerstein system. Based on the established model, a model predictive controller under predefined speed and acceleration constraints is designed to achieve safer treadmill exercise. Simulation results show that the proposed control algorithm can achieve optimal heart rate tracking performance under predefined constraints

Data-driven, mechanistic and hybrid modelling for statistical fault detection and diagnosis in chemical processes

Research and applications of multivariate statistical process monitoring and fault diagnostic techniques for performance monitoring of continuous and batch processes continue to be a very active area of research. Investigations into new statistical and mathematical methods and there applicability to chemical process modelling and performance monitoring is ongoing. Successive researchers have proposed new techniques and models to address the identified limitations and shortcomings of previously applied linear statistical methods such as principal component analysis and partial least squares. This thesis contributes to this volume of research and investigation into alternative approaches and their suitability for continuous and batch process applications. In particular, the thesis proposes a modified canonical variate analysis state space model based monitoring scheme and compares the proposed scheme with several existing statistical process monitoring approaches using a common benchmark simulator – Tennessee Eastman benchmark process. A hybrid data driven and mechanistic model based process monitoring approach is also investigated. The proposed hybrid scheme gives more specific considerations to the implementation and application of the technique for dynamic systems with existing control structures. A nonmechanistic hybrid approach involving the combination of nonlinear and linear data based statistical models to create a pseudo time-variant model for monitoring of large complex plants is also proposed. The hybrid schemes are shown to provide distinct advantages in terms of improved fault detection and reliability. The demonstration of the hybrid schemes were carried out on two separate simulated processes: a CSTR with recycle through a heat exchanger and a CHEMCAD simulated distillation column. Finally, a batch process monitoring schemed based on a proposed implementation of interval partial least squares (IPLS) technique is demonstrated using a benchmark simulated fed-batch penicillin production process. The IPLS strategy employs data unfolding methods and a proposed algorithm for segmentation of the batch duration into optimal intervals to give a unique implementation of a Multiway-IPLS model. Application results show that the proposed method gives better model prediction and monitoring performance than the conventional IPLS approach.EThOS - Electronic Theses Online ServiceGBUnited Kingdo