Mathematical Models of Physiological Responses to Exercise

This paper develops empirical mathematical models for physiological responses to exercise. We first find single-input single-output models describing heart rate variability, ventilation, oxygen consumption and carbon dioxide production in response to workload changes and then identify a single-input multi-output model from workload to these physiological variabilities. We also investigate the possibility of the existence of a universal model for physiological variability in different individuals during treadmill running. Simulations based on real data substantiate that the obtained models accurately capture the physiological responses to workload variations. In particular, it is observed that (i) different physiological responses to exercise can be captured by low-order linear or mildly nonlinear models; and (ii) there may exist a universal model for oxygen consumption that works for different individuals

Development of 2x2 Model Predictive Control Model For Crude Distillation Unit

This report is documented mainly discuss about the final year project entitled "Development of 2x2 Model Predictive Model for Crude Distillation Unit". Advancements in the oil and gas industries requires parallel progress both in maximizing production rate and profit. One sector in which those objectives are accessible is in the refinery business. Core business ofthe refinery sector is swarmed around the crude distillation unit (CDU) which separates raw crude into few marketable products. Due to its high nonlinearity profile and sensitivity of profit margin, any advancement in CDU is considered to be essential. Many researches and engineers use CDU as their case study for projects and paper works to contribute on the optimization, control and production problems. This piece of literature narrows it's scope to control issue of the CDU in which system identification and simulation of CDU system will be developed. Main purpose of this study is to investigate whether development of 2 by 2 MIMO model using Model Predictive Controller (MPC) can increase the performance and reproduce actual data ofCDU to the respect to the variables chosen. Contribution of this research channels to error minimization produced by MPC in which evaluated by minimal controller moves and fluctuations of chosen control variables in comparative to its set points. Testing data from virtual plant will be used as base case to develop relevant robust mathematical model to be eligible for representing CDU system and performance analysis on the chosen model were conducted to derive relevant conclusions. Both research work is possible using MATLAB and HYSYS in which needed materials and toolboxes are available

Constrained generalized predictive control of battery charging process based on a coupled thermoelectric model

Battery temperature is a primary factor affecting the battery performance, and suitable battery temperature control in particular internal temperature control can not only guarantee battery safety but also improve its efficiency. This is however challenging as current controller designs for battery charging have no mechanisms to incorporate such information. This paper proposes a novel battery charging control strategy which applies the constrained generalized predictive control (GPC) to charge a LiFePO₄ battery based on a newly developed coupled thermoelectric model. The control target primarily aims to maintain the battery cell internal temperature within a desirable range while delivering fast charging. To achieve this, the coupled thermoelectric model is firstly introduced to capture the battery behaviours in particular SOC and internal temperature which are not directly measurable in practice. Then a controlled auto-regressive integrated moving average (CARIMA) model whose parameters are identified by the recursive least squares (RLS) algorithm is developed as an online self-tuning predictive model for a GPC controller. Then the constrained generalized predictive controller is developed to control the charging current. Experiment results confirm the effectiveness of the proposed control strategy. Further, the best region of heat dissipation rate and proper internal temperature set-points are also investigated and analysed

State-of-Charge Estimation of Li-ion Battery Packs Based on Optic Fibre Sensor Measurements

This paper presents a battery pack State-of-Charge (SOC) estimation approach by integrating both the cell-based strategy and the pack-based strategy. The approach first utilizes an optic fibre sensor network to monitor variations in strain across the battery cells, based on which a strain model is developed to estimate the SOC of single cells. Then, the cell-based strategy is adopted, for which the SOC of a pack is determined by the highest SOC of single cells observed during charging and the lowest SOC of single cells during discharging. To improve the SOC estimation accuracy of the battery pack strategy, the Thevenin model is employed in conjunction with the Extended Kalman Filter (EKF). The final SOC estimation of the battery pack is then obtained by averaging the results obtained from both the cell-based strategy and the pack-based strategy. Experimental results confirm that this modelling strategy can significantly improve the estimation accuracy and reliability

Efficient least angle regression for identification of linear-in-the-parameters models

Least angle regression, as a promising model selection method, differentiates itself from conventional stepwise and stagewise methods, in that it is neither too greedy nor too slow. It is closely related to L1 norm optimization, which has the advantage of low prediction variance through sacrificing part of model bias property in order to enhance model generalization capability. In this paper, we propose an efficient least angle regression algorithm for model selection for a large class of linear-in-the-parameters models with the purpose of accelerating the model selection process. The entire algorithm works completely in a recursive manner, where the correlations between model terms and residuals, the evolving directions and other pertinent variables are derived explicitly and updated successively at every subset selection step. The model coefficients are only computed when the algorithm finishes. The direct involvement of matrix inversions is thereby relieved. A detailed computational complexity analysis indicates that the proposed algorithm possesses significant computational efficiency, compared with the original approach where the well-known efficient Cholesky decomposition is involved in solving least angle regression. Three artificial and real-world examples are employed to demonstrate the effectiveness, efficiency and numerical stability of the proposed algorithm

Mathematical Models of Physiological Responses to Exercise

This paper develops empirical mathematical models for physiological responses to exercise. We first find single-input single-output models describing heart rate variability, ventilation, oxygen consumption and carbon dioxide production in response to workload changes and then identify a single-input multi-output model from workload to these physiological variabilities. We also investigate the possibility of the existence of a universal model for physiological variability in different individuals during treadmill running. Simulations based on real data substantiate that the obtained models accurately capture the physiological responses to workload variations. In particular, it is observed that (i) different physiological responses to exercise can be captured by low-order linear or mildly nonlinear models; and (ii) there may exist a universal model for oxygen consumption that works for different individuals