Soft computing based controllers for automotive air conditioning system with variable speed compressor

The inefficient On/Off control for the compressor operation has long been regarded as the major factor contributing to energy loss and poor cabin temperature control of an automotive air conditioning (AAC) system. In this study, two soft computing based controllers, namely the proportional-integral-derivative (PID) based controllers tuned using differential evolution (DE) algorithm and an adaptive neural network based model predictive controller (A-NNMPC), are proposed to be used in the regulation of cabin temperature through proper compressor speed modulation. The implementation of the control schemes in conjunction with DE and neural network aims to improve the AAC performance in terms of reference tracking and power efficiency in comparison to the conventional On/Off operation. An AAC experimental rig equipped with variable speed compressor has been developed for the implementation of the proposed controllers. The dynamics of the AAC system is modelled using a nonlinear autoregressive with exogenous inputs (NARX) neural network. Based on the plant model, the PID gains are offline optimized using the DE algorithm. Experimental results show that the DE tuned PID based controller gives better tracking performance than the Ziegler-Nichols tuning method. For A-NNMPC, the identified NARX model is incorporated as a predictive model in the control system. It is trained in real time throughout the control process and therefore able to adaptively capture the time varying dynamics of the AAC system. Consequently, optimal performance can be achieved even when the operating point is drifted away from the nominal condition. Finally, the comparative assessment indicates clearly that A-NNMPC outperforms its counterparts, followed by DE tuned PID based controller and the On/Off controller. Both proposed control schemes achieve up to 47% power saving over the On/Off operation, indicating that the proposed control schemes can be potential alternatives to replace the On/Off operation in an AAC system

Visual perception and verbal response in medical communication

The 'learning theory' has been widely known to be effective and its general psycho concept has been extrapolated theoretically in many varied situations. However, no known studies have actually been made to conclude the effectiveness of its principles between a physician-patient interaction within a clinical environment. This dissertation aims to investigate that effectiveness.Master of Science (Human Factors Engineering

Biventricular assist devices

While left ventricular assist devices (LVADs) are common treatment for patients with heart failure, a sizeable portion of LVAD recipients demonstrate clinically significant postoperative right ventricular failure and potentially require a biventricular assist device (BiVAD). This chapter presents a summary of BiVAD requirements and then reviews several devices that have been used clinically. The majority of these devices are first-generation, pulsatile, paracorporeal systems that are large and unsuitable for long-term support with complications such as device failure, thrombus formation, infection, and severely reduced patient mobility. With the development of rotary blood pumps, more reliable BiVAD options are entering the clinical arena. However, most of these devices have been developed for LVAD support and require modifications for BiVAD support. Although these systems may offer a longer term, completely implantable option for patients with biventricular failure, their control strategies and implantation techniques must be refined. Several BiVAD-specific devices are reported to be under development, yet the advantages of such systems will not be realized until they are implanted in patients and clinicians have gained significant experience using them

Application of multiplayer perceptron and radial basis funtion neural network IN steady state modeling of automotive air conditioning system

In this paper, steady-state models of an automotive air conditioning (ACC) are identified based on two different artificial neural networks (ANN) architectures: Multilayer Perceptron Neural Networks (MLPNN) and Radial Basis Function Neural Networks (RBFNN). The ANN models are developed with a four-in three-out configuration to simulate the outlet evaporating air temperature, cooling capacity, and compressor power under different combination of input compressor speeds, evaporating air speeds, air temperature upstream of the condenser and evaporator. The required data for the system identification are collected from an experimental bench made up of the original components of an AAC system. Investigations signify the advantage of a RBFNN model over MLPNN in modeling the AAC system

Application of adaptive neural predictive control for an automotive air conditioning system

In this paper, a Model Predictive Controller (MPC) using an online trained artificial neural network (ANN) as the nonlinear plant model is implemented for an automotive air conditioning (AAC) system equipped with a variable speed compressor (VSC). The training scheme using Levenberg - Marquardt algorithm and sliding stack window technique is incorporated to train the ANN model in real time so that the time varying dynamics of the AAC system can be captured throughout the control process. The ANN model is initially identified offline using the training and testing data obtained from the experimental AAC system. Validation of the neural network is performed using one-step-ahead and 10-steps-ahead prediction tests. Subsequently, several experimental tests are carried out on the AAC test bench to verify the capability of the proposed controller in tracking set point changes and rejecting disturbances. In order to show the advantages of incorporating an online trained ANN in the proposed controller, comparative assessment is performed between the proposed adaptive controller and two other control schemes, namely a MPC using an of fline trained ANN model and a conventional PID controller. The experimental results signify the superiority of the proposed control scheme in terms of reference tracking as well as disturbance rejection due to its adaptation capability in capturing the real time AAC system behaviour over the wide range of operation condition

Multi objective optimization of an evolutionary feedforward neural network for the automotive air conditioning system performance prediction

In this paper, a novel multi-objective evolutionary artificial neural network approach is proposed to predict the performance of an automotive air conditioning (AAC) system. A Feedforward Neural Network (FNN) was used to simulate the cooling capacity and compressor power under different combination of input compressor speeds, evaporator inlet air speeds, air temperature upstream of the condenser and evaporator. Differential Evolution (DE) algorithm was employed to automatically optimize the FNN's parameters, involving the number of hidden layers and the number of neurons in each hidden layer. The training of connection weights and biases is carried out using the basic backpropagation algorithm with Levenberg Marquardt nonlinear optimization method. For the purpose of multi-objective optimization, the DE algorithm is incorporated with two key elements of the NSGA-II (Non-dominated Sorting Genetic Algorithm II), namely the non-dominated sorting method and the crowding distance metric. A parametric study was performed on the proposed algorithm and the best DE base variant was determined. The experimental results show that the proposed algorithm with DE based variant 'DE/Best/1' exhibited its superiority in term of prediction performance. The best neural network obtained is FNN with 4×18×2 network configuration and its network complexity is equivalent to 108 connection weights. It yields an average relative error of 0.60% for the prediction of cooling power and one of 3.0% for the prediction of compressor power

Pulsatile operation of a continuous-flow right ventricular assist device (RVAD) to improve vascular pulsatility

Despite the widespread acceptance of rotary blood pump (RBP) in clinical use over the past decades, the diminished flow pulsatility generated by a fixed speed RBP has been regarded as a potential factor that may lead to adverse events such as vasculature stiffening and hemorrhagic strokes. In this study, we investigate the feasibility of generating physiological pulse pressure in the pulmonary circulation by modulating the speed of a right ventricular assist device (RVAD) in a mock circulation loop. A rectangular pulse profile with predetermined pulse width has been implemented as the pump speed pattern with two different phase shifts (0% and 50%) with respect to the ventricular contraction. In addition, the performance of the speed modulation strategy has been assessed under different cardiovascular states, including variation in ventricular contractility and pulmonary arterial compliance. Our results indicated that the proposed pulse profile with optimised parameters (Apulse = 10000 rpm and ωmin = 3000 rpm) was able to generate pulmonary arterial pulse pressure within the physiological range (9–15 mmHg) while avoiding undesirable pump backflow under both co- and counter-pulsation modes. As compared to co-pulsation, stroke work was reduced by over 44% under counter-pulsation, suggesting that mechanical workload of the right ventricle can be efficiently mitigated through counter-pulsing the pump speed. Furthermore, our results showed that improved ventricular contractility could potentially lead to higher risk of ventricular suction and pump backflow, while stiffening of the pulmonary artery resulted in increased pulse pressure. In conclusion, the proposed speed modulation strategy produces pulsatile hemodynamics, which is more physiologic than continuous blood flow. The findings also provide valuable insight into the interaction between RVAD speed modulation and the pulmonary circulation under various cardiovascular states

Application of multiobjective neural predictive control to biventricular assistance using dual rotary blood pumps

Rotary blood pumps are used to provide mechanical circulatory support to the failing heart in patients who are ineligible or waiting for a transplant. One of the major challenges when implementing two rotary blood pumps for biventricular support is the difficulty in maintaining pulmonary and systemic circulatory volume balance. In this study, a novel multiobjective neural predictive controller (MONPC) hybridized with a preload-based Frank-Starling-like controller (PFS) has been proposed for a dual rotary blood pump biventricular assist device in two different configurations: PFS-MONPC and MONPC-PFS. The flow rate of one pump is regulated by PFS as a function of preload, while the other pump is controlled by MONPC, which is intended to meet cardiac demand, avoid pulmonary congestion and ventricular suction. A comparative assessment was performed between the proposed controllers and a Dual Independent Frank-Starling-like control system (DI-FS) as well as a constant speed controller. The numerical simulation results showed that MONPC-PFS helped unload the congested left ventricle while maintaining high cardiac output during exercise. In contrast, improper flow regulation by DI-FS has resulted in pulmonary congestion. During blood loss, PFS-MONPC delivered the lowest suction risk, as compared to the constant speed mode, which produced negative right ventricular preload. When sensor noise and time delays were introduced in the flow and end-diastolic pressure signals, the proposed controllers were able to respond with adequate robustness during the transition from rest to exercise. This study demonstrated that the proposed controllers are superior in matching the pump flow with the cardiac demand without causing hemodynamic instabilities