The thesis is organized into 7 chapters. A brief outline of contents for each chapter is detailed as follows:
Chapter 1 gives an overview of the background study as well as the problem statement of the research. The research objectives, the scopes of the study and its contribution are also presented. Finally, the research methodology and a flow chart representing the research strategies are outlined in this chapter.
In Chapter 2, a review of the existing design of AAC experimental rigs and different approaches of dynamic modelling proposed in previous works are presented. A brief overview of different control strategies for air conditioning and refrigeration system and their respective performance are highlighted. Finally the research gaps on AAC thermal control schemes with VSC operation are identified.
Chapter 3 presents the development of an AAC experimental rig equipped with a VSC. The design of the ducting system, refrigeration circuit, measurement instruments and interfaces between the data signals and computer are further elaborated. Step response tests are performed to have a basic study of the AAC dynamics. Subsequently, the effects of different operating conditions on the AAC rig performance are then evaluated based on experimental results. Finally, two conventional control systems, namely an On/Off controller and a PID control system tuned using ZN tuning rules are implemented on the experimental rig. These tests serve to analyse the performance delivered by both conventional control methods in term of reference tracking performance and power efficiency.
Chapter 4 presents the dynamic modelling of the AAC system using a NARX neural networks. The two network architectures investigated in this research are the MLP and RBN networks. A comparative study is conducted to evaluate the performance of these two classes of NARX models in capturing the dynamics of the AAC system. Following this, the application of PI and PID controllers tuned using
DE algorithm is introduced for the cabin temperature control of an AAC system. The optimisation of the PID based controllers is performed based on the aforementioned NARX model in simultion. The advantages of the proposed controller over the conventional ZN method are highlighted based on the experimental results.
Chapter 5 presents the working principle of A-NNMPC. Experimental tests are carried out to verify the control performance of the proposed control scheme. A parametric study is performed to investigate the effect of each control parameter on the performance of the proposed controller. To show the necessity of adopting an online trained ANN model in the control application, a comparative assessment is performed between the proposed adaptive controller and the predictive controller with an offline trained ANN model.
In Chapter 6, a comparative study is performed between the performance of the two proposed control schemes and the On/Off operation. The comparative results provide several findings which highlight the strengths and weaknesses of the proposed control methods.
The final chapter of this thesis summarises the work presented and draws relevant conclusions. Recommendations of future works for further improvement are discussed.
REFERENCES
Afram, A. and Janabi-Sharifi, F. (2014). Theory and Application of HVAC Control Systems-A Review of Model Predictive Control. Building and Environment. 72(1), 343-355.
Aggelogiannaki, E., Sarimveis, H. and Koubogiannis, D. (2007). Model Predictive Temperature Control in Long Ducts by Means of a Neural Network Approximation Tool. Applied Thermal Engineering. 27(14-15), 2363-2369. Akin, O., Turkaslan-Bulbul, T., Lee, S. H., Garrett, J. and Akinci, B. (2011).
Embedded Commissioning of Building Systems. Norwood, USA: Artech House. Akpan, V. A. and Hassapis, G. D. (2010). Nonlinear Model Identification and Adaptive Model Predictive Control Using Neural Networks. ISA Transactions. 50(2), 177-194.
Albertos, P. and Sala, A. (1998). Fuzzy Logic Controllers: Advantages and Drawbacks. IEEE Transactions on Control System Technology.
Aliev, R. A. and Aliev, R. R. (2001). Soft Computing and Its Applications. London, UK: World Scientific.
Alkan, A. and Hosoz, M. (2010). Comparative Performance of an Automotive Air Conditioning System Using Fixed and Variable Capacity Compressor. International Journal of Refrigeration. 33(3), 487-495.
Allgoewer, F., Findeisen, R. and Nagy, Z. K. (2004). Nonlinear Model Predictive Control: From Theory to Application. Journal of the Chinese Institute of Chemical Engineers. 35(3), 299-315.
Amjad, A. M., Salam, Z. and Saif, A. M. A. (2015). Application of Differential Evolution for Cascaded Multilevel VSI with Harmonics Elimination PWM Switching. Electrical Power and Energy Systems. 64(1), 447-456.
Ananthanarayana, P. N. (2005). Basic Refrigeration and Air Conditioning. 3rd ed. New Delhi, India: Tata McGraw-Hill Education.
Antsaklis, P. J. (1997). Intelligent Control. In: Webster J. G. (Ed.) Encyclopedia of Electrical and Electronics Engineering (pp. 493-503). New York, USA: John Wiley & Son.
Aprea, C., Mastrullo, R. and Renno, C. (2004). Fuzzy Control of the Compressor Speed in a Refrigeration Plant. International Journal of Refrigeration. 27(6), 639-648.
ASHRAE (2004). ASHRAE Standard 55-2004. Atlanta, USA: American Society of Heating, Refrigerating and Air Conditioning Engineers.
ASHRAE. (2009). 2009 ASHRAE Handbook: Fundamentals. SI ed. Atlanta, Georgia: American Society of Heating, Refrigeration and Air-Conditioning Engineers, Inc.
Astrom, K. J. and Hagglund, T. (1988). Automatic Tuning of PID Controllers. Research Triangle Park, N.C.: Instrumentation, Systems, and Automation Society.
Astrom, K. J. and Hagglund, T. (1995). PID Controllers: Theory, Design, and Tuning. 2nd ed. Reasearch Triangle Park, N.C.: Instrument Society of America. Astrom, K. J. and Hagglund, T. (2006). Advanced PID Control. Research Triangle
Park, N.C.: Instrumentation, System, and Automation Society.
Astrom, K. J. and Murray, R. M. (2008). Feedback Systems: An Introduction for Scientists and Engineers. Princeton, N.J.: Princeton University Press.
Atabani, A. E., Badruddin, I. A., Mekhlilef, S. and Silitonga, A. S. (2011). A Review on Global Fuel Economy Standards, Labels and Technologies in the Transportation Sector. Renewable and Sustainable Energy Reviews. 15(9), 4586-4610.
Atik, K., Aktas, A. and Deniz, E. (2010). Performance Parameters Estimation of MAC by Using Artificial Neural Networks. Expert Systems with Applications. 37(7), 5436-5442.
Atuonwu, J. C., Cao, Y. and Tade, M. O. (2010). Identification and Predictive Control of a Multistage Evaporator. Control Engineering Practice. 18(12), 1418-1428.
Baharum, M. A., Surat, M., Tawil, N. M. and Che-Ani, A. I. (2014). Modern Housing Tranquillity in Malaysia from the Aspect of Thermal Comfort for Humid Hot Climate Zone. Emerging Technology for Sustainable Development Congress. 5th August. Bangi, Malaysia, 1-6.
Bartolini, C. M. and Vincenzi, G. (1986). New Internal Capacity Control for Reciprocating Compressor. Purdue Compressor Technology Conference. 16-19 July. Purdue University, USA, 521-536.
Bauer, L. R. and Hamby, D. M. (1991). Relative Sensitivities of Existing and Novel Model Parameters in Atmospheric Tritium Dose Estimates. Radiation Protection Dosimetry. 37(4), 253-260.
Bechtler, H., Browne, M. W., Bansal, P. K. and Kecman, V. (2001). New Approach to Dynamic Modelling of Vapour-Compression Liquid Chillers: Artificial Neural Networks. Applied Thermal Engineering. 21(9), 941-953.
Bequetter, B. W. (2003). Process Control: Modeling, Design, and Simulation. Upper Saddle Rive, N.J.: Prentice Hall.
Billings, S. A. (2013). Nonlinear System Identification: NARMAX Methods in the Time, Frequency, and Spatio-Temporal Domains. West Sussex, U.K.: John Wiley & Sons.
Bingul, Z. (2004). A New PID Tuning Technique Using Differential Evolution for Unstable and Integrating Processes with Time Delay. Neural Information Processing. 22-25 November. Calcutta, India, 254-260.
Bishop, C. M. (1994). Neural Networks and Their Applications. Review of Scientific Instruments. 65(6), 1803-1832.
Boekhoff, H. (2013, January 21). SK Continental E-motion Lauches Global Battery Business. Continental Press Portal, Retrieved December 3, 2014, from http://www.continental-corporation.com/.
British (1999). BS 5141-1:1975. Chiswick High Road, London: British Standards Institution.
Brosilow, C. and Joseph, B. (2002). Techniques of Model-Based Control. Upper Saddle River, N.J.: Prentice Hall.
Buzelin, L. O. S., Amico, S. C., Vargas, J. V. C. and Parise, J. A. R. (2005). Experimental Development of an Intelligent Refrigeration System. International Journal of Refrigeration. 28(2), 165-175.
Catano, J., Lizarralde, F., Zhang, T., Wen, J. T., Jensen, M. K. and Peles, Y. (2013). Vapor Compression Refrigeration Cycle for Electronics Cooling-Part II: Gain- Scheduling Control for Critical Heat Flux Avoidance. International Journal of Heat and Mass Transfer. 66(1), 922-929.
Cheah, L. and Heywood, J. (2011). Meeting U.S. Passenger Vehicle Fuel Economy Standards in 2016 and beyong. Energy Policy. 39(1), 454-466.
Chen, J. and Huang, T. (2004). Applying Neural Networks to On-line Updated PID Controllers for Nonlinear Process Control. Journal of Process Control. 14(2), 211-230.
Chen, S., Billings, S. A. and Grant, P. M. (1990). Non-linear System Identification using Neural Networks. International Journal of Control. 51(6), 1191-1214. Chen, S., Cowan, C. F. N. and Grant, P. M. (1991). Orthogonal Least Squares
Learning Algorithm for Radial Function Networks. IEEE Transactions on Neural Networks. 2(2), 302-309.
Chiong, R., Weise, T. and Michalewicz, Z. (2011). Variants of Evolutionary Algorithms for Real World Applications. Berlin Heidelberg, Germany: Springer.
Choudhury, D. R. (2005). Modern Control Engineering. 2nd ed. New Delhi, India: Prentice Hall.
Coelho, L. d. S. and Pessoa, M. W. (2011). A Tuning Strategy for Multivariable PI and PID Controllers Using Differential Evolutoin Combined with Chaotic Zaslavskii Map. Expert Systems with Applications. 38(11), 13694-13701. Cominos, P. and Munro, N. (2002). PID Controllers: Recent Tuning Methods and
Design to Specification. Control Theory and Application s. 149(1), 46-53. Cuevas, C., Fonseca, N. and Lemort, V. (2012). Automotive Electric Scroll
Compressor: Testing and Modeling. International Journal of Refrigeration. 35(4), 841-849.
Daly, S. (2006). Automotive Air Conditioning and Climate Control Systems. Burlington, MA: Butterworth-Heinemann.
Darus, I. Z. M. and Al-Khafaji, A. A. M. (2012). Non-parametric Modelling of a Rectangular Flexible Plate Structure. Engineering Application of Artificial Intelligence. 25(1), 94-106.
Das, S. and Suganthan, P. N. (2011). Differential Evolution: A Survey of the State of the Art. Evolutionary Computation. 15(11), 4-31.
Datta, A., Ho, M. and Bhattacharyya, S. P. (2000). Structure and Synthesis of PID Controllers. London, UK: Springer.
Davis, L. I., Sieja, T. F., Matteson, R. W., Dage, G. A. and Ames, R. (1994). Fuzzy Logic for Vehicle Climate Control. International Conference Fuzzy System. 26- 29 June. Orlando, Florida, USA, 530-534.
Doherty, S. K., Gomm, J. B. and Williams, D. (1997). Experiment Design Consideration for Non-Linear System Identification Using Neural Networks. Computers and Chemical Engineering. 21(3), 327-346.
Dong, R. (2009). Differential Evolution versus Particle Swarm Optimization for PID Controller Design. Fifth International Conference on Natural Computation. 14-16 August. Tianjin, China, 236-240.
Dote, Y. and Ovaska, S. J. (2001). Industrial Applications of Soft Computing: A Review. Proceedings of the IEEE. 89(9), 1243-1265.
Ekren, O., Celik, S., Noble, B. and Kraus, R. (2013). Performance Evaluation of A Variable Speed DC Compressor. International Journal of Refrigeration. 36(3), 745-757.
Ekren, O., Sahin, S. and Isler, Y. (2010). Comparison of Different Controllers for Variable Speed Compressor and Electronic Expansion Valve. International Journal of Refrigeration. 33(6), 1161-1168.
Elliott, M. S. and Rasmussen , B. P. (2013). Decentralized Model Predictive Control of a Multi-Evaporator Air Conditioning System. Control Engineering Practice. 21(12), 1665-1677.
Fasso, A. and Perri, P. F. (2002). Sensitivity Analysis. In: El-Shaarawi A. H. and Piegorsch W. W. (Eds) Encyclopedia of Environmetrics (1968-1982). Chichester, England: John Wiley & Sons.
Feoktistiv, V. (2006). Differential Evolution: In Search of Solutions. New York, USA: Springer.
Fie, L. and Chunxuan, Y. (2013). Study of the Fuzzy Neural Network Control Used in A New Type of Household Central Air Conditioning. 32nd Chinese Control Conference. 26-28 July. Xian, China, 3510-3514.
Fortuna, L., Rizzotto, G., Lavorgna, M., Nunnari, G., Xibilia, M. G. and Capanetton, R. (2001). Soft Computing: New Trends and Applications. London, U.K.: Springer.
Franco, I. C., Dall'Agnol, T. V., Costa, A. M. F. and Silva, F. V. (2011). A Neuro- Fuzzy Identification of Non-Linear Transient Systems: Application to a Pilot Refrigeration Plant. International Journal of Refrigeration. 34(8), 2063-2075. Ganesh, R. (2010). Control Engineering. New Delhi, India: Pearson Education. Giannavola, M. S. and Hrnjak, P. S. (2002). Experimental Study of System
Performance Improvements in Transcritical R744 Systems for Mobile Air- Conditioning and Heat Pumping. Technical Report, University of Illinois, Illinois.
Goswami, D. Y. and Kreith, F. (2007). Handbook of Energy Efficiency and Renewable Energy. New York, USA: CRC Press.
Graviss, K. (1998). A Neural Network Controller for Optimal Temperature Control of Household Refrigerators. Intelligent Automation & Soft Computing. 4(4), 357-372.
Grozde, H. and Taplamacioglu, M. C. (2011). Automatic Generation Control Application with Craziness based Particle Swarm Optimization in a Thermal Power System. International Journal of Electrical Power & Energy Systems. 33(1), 19-33.
Hagan, M. T., Demuth, H. B. and Jesus, O. D. (2002). An Introduction to the Use of Neural Networks in Control Systems. International Journal of Robust and Nonlinear Control. 12(11), 959-985.
Hagan, M. T. and Menhaj, M. B. (1994). Training Feedforward Networks with the Marquardt Algorithm. IEEE Transactions on Neural Networks. 5(6), 989-993. Hamby, D. M. (1995). A Comparison of Sensitivity Analysis Techniques. Health
Physics. 68(2), 195-204.
Hamid, N. H. A., Kamal, M. M. and Yahaya, F. H. (2009). Application of PID Controller in Controlling Refrigerator Temperature. 5th International Colloqium of Signal Processing & Its Application. 6-8 March. Kuala Lumpur, Malaysia, 378-384.
Hammerschmidt, C. (2011, June 16). German Carmakers Agree on 48V On-Board Supply, Charging Plug. EE Times Europe Automotive, Retrieved December 3, 2014, from http://automotive-eetimes.com/.
Hangos, K. M., Lakner, R. and Gerzson, M. (2002). Intelligent Control Systems: An Introduction with Examples. Dordrecht, The Netherlands: Kluwer Academic Plublishers.
He, X.-D., Liu, S. and Asada, H. (1997). Modeling of Vapor Compression Cycles for Multivariable Feedback Control of HVAC Systems. Journal of Dynamic Systems, Measurement and Control. 119(2), 183-191.
Hensen, J., Bartak, M. and Drkal, F. (2002). Modeling and Simulation of a Double- skin Facade System. ASHRAE Transaction. 108(2), 1251-1259.
Hoffman, F. O. and Gardner, R. H. (1983). Evaluation of Uncertainties in Environmental Radiological Assessment Models. In: Till J. E. and Meyer H. R. (Eds) Radiological Assessment: A Textbook on Environmental Dose Assessment (11.11-11.55). Washington, D.C.: US Nuclear Regulatory Commission.
Holloway, M. D., Iwaoha, C. and Onyewuenyi, O. A. (2012). Process Plant Equipment: Operation, Control, and Reliability. Chichester, U.K.: Wiley. Hornik, K. and Stinchcombe, H. W. (1989). Multilayer Feedforward Networks are
Universal Approximators. Neural Networks. 2(5), 359-366.
Hosen, M. A., Hussain, M. A., Mjalli, F. S., Khosravi, A., Creighton, D. and Nahavandi, S. (2014). Performance Analysis of Three Advanced Controllers for Polymerization Batch Reactor: An Experimental Investigation. Chemical Engineering Research and Design. 92(5), 903-916.
Hosoz, M. and Direk, M. (2004). Performance Evaluation of an Integrated Automotive Air Conditioning and Heat Pump System. Energy Conversion and Management. 47(5), 545-559.
Hosoz, M. and Ertunc, H. M. (2006). Artificial Neural Network Analysis of an Automobile Air Conditioning System. Energy Conversion and Management. 47(11-12), 1574-1587.
Hosoz, M., Ertunc, H. M. and Bulgurcu, H. (2011). An Adaptive Neuro-Fuzzy Inference System Model for Predicting the Performance of a Refrigeration System with a Cooling Tower. Expert Systems with Applications. 38(11), 14148-14155.
Huang, D., Wallis, M., Oker, E. and Lepper, S. (2007). Design of Vehicle Air Conditioning Systems Using Heat Load Analysis. SAE World Congress and Exhibition. Detroit, Michigan.
Huang, W. and Lam, H. N. (1997). Using Genetic Algorithm to Optimize Controller Parameters for HVAC Systems. Energy and Buildings. 26(3), 277-282.
Hundy, G. H., Trott, A. R. and Welch, T. C. (2008). Refrigeration and air- conditioning. 4th ed. Oxford, U.K.: Butterworth-Heinemann.
Hussain, M. A. (1999). Review of the Applications of Neural Networks in Chemical Process Control - Simulation and Online Implementation. Artificial Intelligence in Engineering. 13(1), 55-68.
Hussain, M. A. and Ho, P. Y. (2004). Adaptive Sliding Mode Control with Neural Network based Hybrid Models. Journal of Process Control. 14(2), 157-176. Hussain, M. A. and Kershenbaum, L. S. (2000). Implementation of an Inverse Model
Based Control Strategy Using Neural Networks on a Partially Simulated Exothermic Reactor. Chemical Engineering Research and Design. 78(2), 299- 311.
Irwin, G. W., Warwick, K. and Hunt, K. J. (1995). Neural Network Applications in Control. London, U.K.: The Institution of Engineering and Technology.
Jabardo, J. M. S., Mamani, W. G. and Ianella, M. R. (2003). Modeling and Experimental Evaluation of an Automotive Air Conditioning System with a Variable Capacity Compressor. International Journal of Refrigeration. 25(8), 1157-1173.
Jahedi, G. and Ardehali, M. M. (2011). Genetic Algorithm-Based Fuzzy-PID Control Methodologies for Enhancement of Energy Efficiency of a Dynamic Energy System. Energy Conversion and Management. 52(1), 725-732.
Jain, K. K. and Asthana, R. B. (2002). Automobile Engineering. New Delhi, India: Tata McGraw-Hill Education.
Janakiraman, V. M., Nguyen, X. and Assanis, D. (2013). Nonlinear Identification of a Gasoline HCCI Using Neural Networks Coupled with Principle Component Analysis. Applied Soft Computing. 13(5), 2375-2389.
Johnson, M. A. and Moradi, M. H. (2005). PID Control: New Identification and Design Methods. London, UK: Springer.
Joudi, A. K., Mohammed, A. S. K. and Aljanabi, M. K. (2003). Experimental and Computer Performance Study of an Automotive Air Conditioning System with Alternative Refrigerants. Energy Conversion and Management. 44(18), 2959- 2976.
Kamar, H. M. (2009). Computerised Simulation of Automotive Air Conditioning System. Doctor of Philosophy, Universiti Teknologi Malaysia.
Kamar, H. M., Ahmad, R., Kamsah, N. B. and Mustafa, A. F. (2013). Artificial Neural Networks for Automotive Air Conditioning Systems Performance Prediction. Applied Thermal Engineering. 50(1), 63-70.
Karaboga, D. and Okdem, S. (2004). A Simple and Global Optimization Algorithm for Engineering Problems: Differential Evolution Algorithm. Turkish Journal of Electrical Engineering. 12(1), 53-66.
Karer, G. and Skjanc, I. (2013). Predictive Approaches to Control of Complex Systems. Berlin Heidelberg, Germany: Springer.
Kaynakh, O. and Horuz, I. (2003). An Experimental Analysis of Automotive Air Conditioning System. International Communications in Heat and Mass Transfer. 30(2), 273-284.
Khayyam, H., Kouzani, A. Z., Hu, E. J. and Nahavandi, S. (2011). Coordinated Energy Management of Vehicle Air Conditioning System. Applied Thermal Engineering. 31(5), 750-764.
Kiran, T. R. and Rajput, S. P. S. (2011). An Effectiveness Model for an Indirect Evaporative Cooling (IEC) System: Comparison of Artificial Neural Networks (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Fuzzy Inference (FIS) Approach. Applied Soft Computing. 11(4), 3525-3533.
Krishna, P. S. (2010). Process Control Engineering. New Delhi, India: I. K. International Publishing House.
Kusiak, A. and Xu, G. (2012). Modeling and Optimization of HVAC Systems Using a Dynamic Neural Network. Energy. 42(1), 241-250.
Leducq, D., Guilpart, J. and Trystram, G. (2006). Non-linear Predictive Control of a Vapour Compression Cycle. International Journal of Refrigeration. 29(5), 761- 772.
Lee, W., Chen, Y. and Kao, Y. (2011). Optimal Chiller Loading by Differential Evolution Algorithm for Reducing Energy Consumption. Energy and Buildings. 43(2-3), 599-604.
Leva, A., Piroddi, L., Felice, M., Boer, A. and Paganini, R. (2010). Adaptive Relay- Based Control of Household Freezers with On-Off Actuators. Control Engineering Practice. 18(1), 94-102.
Li, B. and Alleyne, A. G. (2010). Optimal On-Off Control of An Air Conditioning and Refrigeration System. American Control Conference. 30 June-2 July. Baltimore, MD.
Li, N., Xia, L., Shiming, D., Xu, X. and Chan, M.-Y. (2012). Dynamic Modeling and Control of a Direct Expansion Air Conditioning System Using Artificial Neural Network. Applied Energy. 91(1), 290-300.
Li, N., Xia, L., Shiming, D., Xu, X. and Chan, M.-Y. (2013). On-line Adaptive Control of a Direct Expansion Air Conditioning System Using Artificial Neural Network. Applied Thermal Engineering. 53(1), 96-107.
Liao, G. (2014). Hybrid Improved Differential Evolution and Wavelet Neural Network with Load Forecasting Problem of Air Conditioning. International Journal of Electrical Power & Energy Systems. 61(1), 673-682.
Luo, Y. and Che, X. (2010). Tuning PID Control Parameters on Hydraulic Servo Control System Based on Differential Evolution Algorithm. 2nd International
Conference on Advanced Computer Control. 27-29 March. Shenyang, China, 348-351.
Maciejowski, J. M. (2002). Predictive control: with constraints. Prentice Hall.
Malhotra, R., Singh, N. and Singh, Y. (2011). Soft Computing Technologies for Process Control Applications. International Journal on Soft Computing. 2(3), 32-44.
Marinaki, M., Marinakis, Y. and Stravroulakis, G. E. (2010). Fuzzy Control Optimized by PSO for Vibration Suppression of Beams. Control Engineering Practice. 18(6), 618-629.
Mcculloch, W. S. and Pitts, W. (1943). A Logical Calculus of the Ideas Immanent in Nervous Activity. Bulletin of Mathematical Biophysics. 5(4), 115-133.
McDowall, R. (2010). Fundamentals of HVAC Control Systems. SI ed. Atlanta, Georgia: American Socitey of Heating, Refrigerating and Air-Conditioning Engineers.
Miller, W. T., Sutton, R. S. and Werbos, P. J. (1995). Neural Networks for Control. Cambridge, MA: The MIT Press.
Moffat, R. J. (1988). Describing the Uncertainties in Experimental Results. Experimental Thermal and Fluid Science. 1(1), 3-17.
Montgomery, D. C. (2012). Design and Analysis of Experiments. 8th ed. Danvers, MA: John Wiley & Sons.
Montiel, O., Castillo, O., Melin, P. and Sepulveda, R. (2006). Evolutionary Modeling Using a Wiener Model. Advanced in Soft Computing. 34(1), 619-632.
Naidu, D. S. and Rieger, C. (2011a). Advanced Control Strategies for Heating, Ventilation, Air Conditioning, and Refrigeration Systems-An Overview: Part 1: Hard Control. Science and Technoglogy for the Built Environment. 17(1), 2-21. Naidu, D. S. and Rieger, C. G. (2011b). Advanced Control Strategies for HVAC&R Systems-An Overview: Part II: Soft and Fusion Control. Science and Technoglogy for the Built Environment. 17(2), 144-158.
Narendra, K. S. and Parthasarathy, K. (1990). Identification and Control of Dynamical System Using Neural Networks. Neural Networks. 1(1), 4-27. Nasution, H. (2005). Development of Fuzzy Logic Control for Vehicle Air
Conditioning System. TELKOMNIKA. 6(2), 73-82.
Nasution, H. and Hassan, M. N. W. (2006). Potential Electriciy Savings by Variable Speed Control of Compressor for Air Conditioning Systems. Clean Technology Environment Policy. 8(2), 105-111.
Nelles, O. (2001). Nonlinear System Identification: From Classical Approaches to Neural Networks and Fuzzy Models. Berlin Heidelberg, Germany: Springer. Norgaard, M., Ravn, O., Poulsen, N. K. and Hansen, L. K. (2001). Neural Networks
Oosthuizen, D. J., Craig, I. K. and Pistorius, P. C. (1999). Model Predictive Control of an Electric Arc Furnace Off-Gas Procedure Combined with Temperature Control. IEEE Africon. 28 September-1 October. Cape Town, South Africa, 415-420.
Ovaska, S. J., Kamiya, A. and Chen, Y. (2006). Fusion of Soft Computing and Hard Computing: Computational Structures and Characteristic Features. Systems, Man, and Cybernetics. 36(3), 439-448.
Parlos, A. G., Rais, O. T. and Atiya, A. F. (2000). Multi-Step-Ahead Prediction Using Dymamic Recurrent Neural Networks. Neural Networks. 13(1), 765-786. Parreira, E. P. and Parise, J. A. R. (1993). Performance Analysis of Capacity Control
Devices for Heat-Pump Reciprocating Compressors. Heat Recovery Systems and CHP. 13(5), 451-461.
Parvaresh, A., Hasanzade, A., Mohammadi, S. M. A. and Gharaveisi, A. (2012). Fault Detection and Diagnosis in HVAC System Based on Soft Computing Approach. International Journal of Soft Computing and Engineering. 2(3), 2231-2307.
Perez-Segarra, C. D., Rigola, J., Soria, M. and Oliva, A. (2005). Detailed Thermodynamic Characterization of Hermetic Reciprocating Compressors. International Journal of Refrigeration. 28(4), 579-593.
Piedrahita-Velasquez, C. A., Ciro-Velasquez, H. J. and Gomez-Botero, M. A. (2014). Identification and Digital Control of a Household Refrigeration System with a Variable Speed Compressor. International Journal of Refrigeration. 48(1), 178-187.
Qin, S. J. and Badgwell, T. A. (2003). A Survey of Industrial Model Predictive Control Technology. Control Engineering Practice. 11(7), 733-764.
Qureshi, T. Q. and Tassou, S. A. (1995). Variable Speed Capacity Control in Refrigeration Systems. Applied Thermal Engineering. 16(2), 103-113.
Rasmussen, B. (2002). Control-Oriented Modeling of Transcritical Vapor Compression Systems. Doctor of Philosophy, University of Illinois.
Rasmussen , B., Alleyne, A., Bullard, C., Hmjak, P. and Miller, N. (2002). Control- Oriented Modeling and Analysis of Automotive Transcritical AC System Dynamics. American Control Conference. 8-10 May. Anchorage, AK, 3111- 3116.
Rasmussen, B. P. and Alleyne, A. G. (2010). Gain Scheduled Control of an Air Conditioning System Using the Youla Parametrization. Control Systems Technology. 18(5), 1216-1225.
Ratts, E. B. and Brown, J. S. (1999). An experimental analysis cycling in an automotive conditioning system. Applied Thermal Engineering. 20(11), 1039- 1058.
Ray, K. S. (2014). Soft Computing and its Applications: A Unified Engineering Concept. Oakville, Canada: Apple Academic Press.
Razi, M., Farrokhi, M., Sacide, M. H. and Khorasani, A. R. F. (2006). Neuro- Predictive Control for Automotive Air Conditioning System. IEEE International Conference on Engineering of Intelligent Systems. 22-23 April. Islamabad, Pakistan, 1-6.
Riva, E. D. and Col, D. D. (2011). Performance of a Semi-Hermetic Reciprocating Compressor with Propane and Mineral Oil. International Journal of Refrigeration. 34(3), 752-763.
Rubas, P. J. and Bullard, C. W. (1994). Factors Contributing to Refrigerator Cycling Losses. International Journal of Refrigeration. 18(3), 168-176.
Rugh, J. P., Chaney, L. and Lustbader, J. (2007). Reduction in Vehicle Temperatures and Fuel Use from Cabin Ventilation, Solar-Reflective Paint and a New Solar-