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

机译:基于软计算的变速压缩机汽车空调系统控制器

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摘要

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.
机译:长期以来,压缩机运行中的开/关控制效率低下一直被认为是导致汽车空调(AAC)系统能量损失和车厢温度控制不佳的主要因素。在这项研究中,建议使用两种基于软计算的控制器,即使用微分进化(DE)算法调整的基于比例积分微分(PID)的控制器和基于自适应神经网络的模型预测控制器(A-NNMPC)通过适当调节压缩机速度来调节机舱温度。与常规的开/关操作相比,结合DE和神经网络的控制方案的实现旨在在参考跟踪和功率效率方面提高AAC性能。已开发了配备变速压缩机的AAC实验台,用于实施所提出的控制器。 AAC系统的动力学是使用带有外部输入的非线性自回归(NARX)神经网络建模的。基于工厂模型,使用DE算法离线优化PID增益。实验结果表明,基于DE调整PID的控制器比Ziegler-Nichols调整方法具有更好的跟踪性能。对于A-NNMPC,将识别出的NARX模型作为预测模型合并到控制系统中。它在整个控制过程中都经过实时培训,因此能够自适应地捕获AAC系统随时间变化的动态特性。因此,即使工作点偏离标称条件也可以实现最佳性能。最后,比较评估清楚地表明,A-NNMPC的性能优于同类产品,其次是基于DE调整的PID控制器和On / Off控制器。两种建议的控制方案在开/关操作上均节省多达47%的功率,这表明,建议的控制方案可以替代AAC系统中的开/关操作。

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    Ng Boon Chiang;

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  • 年度 2015
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