Abstract
Experience Mapping based Prediction Controller (EMPC) is a control mechanism recently developed by adopting the concepts of Human Motor Control into engineering world. This paper presents the principles used to design EMPC based controller for Type-0 systems. The theory of the controller is mathematically established and its stability criteria are developed. Algorithms to obtain the required steady state and transient responses are developed and are simulated on a DC motor based speed control system model. The performance of EMPC is compared with that of a Model Reference Adaptive Controller. The controller developed is also successfully tried on a practical speed control system and the results are presented.
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Ungerleider LG, Doyon J, Karni A (2002) Imaging brain plasticity during motor skill learning. Neurobiol Learn Mem 78:553–564
Schlaug G (2001) The brain of musicians. Ann N Y Acad Sci 930(1):281–299
Karni A (1996) The acquisition of perceptual and motor skills: a memory system in the adult human cortex. Cogn Brain Res 5(1):39–48
Van Mier H, Tempel L, Perlmutter J, Raichle M, Petersen S (1998) Changes in brain activity during motor learning measured with PET: effects of hand of performance and practice. J Neurophysiol 80(4):2177–2199
Wolpert DM, Ghahramani Z (2000) Computational principles of movement neuroscience. Nat Neurosci 3:1212–1217
Wolpert DM, Ghahramani Z, Jordan MI (1995) Forward dynamic models in human motor control: psychophysical evidence. In: Advances in neural information processing systems, pp 43–50
Miall RC, Wolpert DM (1996) Forward models for physiological motor control. Neural Netw 9(8):1265–1279
Saikumar N, Dinesh NS (2012) Position control of DC motors with experience mapping based prediction controller. In: IECON 2012—38th annual conference on IEEE Industrial Electronics Society, pp 2394–2399
Saikumar N, Dinesh NS (2012) A study of experience mapping based prediction controller for position control of DC motors with inertial and friction load changes. In: 2012 IEEE 7th international conference on industrial and information systems (ICIIS), pp 1–6
Saikumar N, Dinesh NS (2016) A study of bipolar control action with EMPC for the position control of DC motors. Int J Dyn Control 4(1):154–166
Saikumar N, Dinesh N, Kammardi P (2017) Experience mapping based prediction controller for the smooth trajectory tracking of DC motors. Int J Dyn Control 5(3):704–720
Saikumar N, Dinesh N (2014) Improved adaptation of EMPC with response sampling based prediction correction for the position control of DC motors. In: International conference on computer, control, informatics and its applications (IC3INA, 2014). IEEE, pp 109–114
Aravind MA, Rajanna K, Dinesh NS (2017) Application of EMPC for under-damped Type-1 systems. In: 3rd international conference on control automation and robotics (ICCAR), pp 471–476
Aravind MA, Dinesh NS, Rajanna K (2018) Adaptive experience mapping based predictive controller for under-damped type 1 systems. Int J Dyn Control. https://doi.org/10.1007/s40435-018-0396-0
Raghu CV, Dinesh NS (2017) DC motor speed control using experience mapping based prediction controller (EMPC). In: 2017 3rd international conference on control automation and robotics (ICCAR), pp 533–538
Condit R (2004) Brushed DC motor fundamentals, Microchip Technology Inc. http://ww1.microchip.com/downloads/en/AppNotes/00905a.pdf. Accessed 9 Dec 2017
Meshram PM, Kanojiya RG (2012) Tuning of PID controller using Ziegler–Nichols method for speed control of DC motor. In: IEEE-international conference on advances in engineering, science and management (ICAESM-2012), pp 117–122
Tipsuwan Y, Chow M -Y (1999) Fuzzy logic micro controller implementation for DC motor speed control. In: The 25th annual conference of the Industrial Electronics Society, 1999. IECON ’99 Proceedings, vol 3. IEEE, pp 1271–1276
Sheel S, Chandkishor R, Gupta O (2010) Speed control of DC drives using MRAC technique. In: 2010 international conference on mechanical and electrical technology, pp 135–139
Moussavi SZ, Alasvandi M, Javadi S, Morad E (2014) PMDC motor speed control optimization by implementing ANFIS and MRAC. Int J Control Sci Eng 4(1):1–8
Sahoo S, Subudhi B, Panda G (2015) Optimal speed control of DC motor using linear quadratic regulator and model predictive control. In: 2015 international conference on energy, power and environment: towards sustainable growth (ICEPE). IEEE, pp 1–5
Pisano A, Davila A, Fridman L, Usai E (2008) Cascade control of PM DC drives via second-order sliding-mode technique. IEEE Trans Ind Electron 55(11):3846–3854
Yildiz AB, Bilgin MZ (2006) Speed control of averaged DC motor drive system by using neuro-PID controller. In: 10th international conference, knowledge-based intelligent information and engineering systems, KES 2006, Bournemouth, UK, 9–11 Oct 2006. Proceedings, Part I. Springer, Berlin
Bhushan B, Singh M (2011) Adaptive control of DC motor using bacterial foraging algorithm. Appl Soft Comput 11(8):4913–4920
Kuo B, Tal J (1978) DC motors and control systems. SRL Publishing Co, no. v. 1. https://books.google.co.in/books?id=stNSAAAAMAAJ. Accessed 9 Dec 2017
B5Z-12 magnetic practical brake. Placid Industries. http://placidindustries.com/spec.b5z.pdf. Accessed 9 Dec 2017
Nijhawan R, Wu S (2009) Compensating time delays with neural predictions: are predictions sensory or motor? Philos Trans R Soc Lond A Math Phys Eng Sci 367(1891):1063–1078
Maus GW, Fischer J, Whitney D (2013) Motion-dependent representation of space in area MT+. Neuron 78(3):554–562
Sinha NK, Dicenzo C D, Szabados B (1974) Modeling of DC motors for control applications. IEEE Trans Ind Electron Control Instrum IECI–21(2):84–88
Hu H (2011) Analysis of the mechanical characteristics and determination of power relationship for DC motor. Procedia Eng 15(Supplement C):531–535
Naitoh H, Tadakuma S (1987) Microprocessor-based adjustable-speed DC motor drives using model reference adaptive control. IEEE Trans Ind Appl 2:313–318
Hang C-C, Parks P (1973) Comparative studies of model reference adaptive control systems. IEEE Trans Autom Control 18(5):419–428
Pankaj S, Kumar JS, Nema R (2011) Comparative analysis of MIT rule and Lyapunov rule in model reference adaptive control scheme. Innov Syst Des Eng 2(4):154–162
Stellet JE (2011) Influence of adaptation gain and reference model parameters on system performance for model reference adaptive control. World Acad Sci Eng Technol 60:1768–1773
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Raghu, C.V., Dinesh, N.S. Development of Experience Mapping based Prediction Controller for Type-0 systems. Int. J. Dynam. Control 7, 577–594 (2019). https://doi.org/10.1007/s40435-018-0479-y
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DOI: https://doi.org/10.1007/s40435-018-0479-y