A study of Experience Mapping based Prediction Controller for position control of DC motors with inertial and friction load changes

Saikumar, Niranjan; Dinesh, N. S.

doi:10.1109/iciinfs.2012.6304819

Cited by 9 publications

(24 citation statements)

References 5 publications

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“…To overcome the problems due to non-idealities in a practical system, EMK is developed using initial learning as described in [35,36]. Here the duration of the rectangular pulse that should be applied in terms of T on values for different values of P total of the position control system is obtained instead of a single input-output recording as proposed before to counter the non-linearity of the relation.…”

Section: Initial Learningmentioning

confidence: 99%

“…The principle unipolar action and the corresponding system response is shown in Fig. 1 [35,36]. EMPC utilizes rectangular control inputs with constant amplitude A m and time duration T on for the development of EMK.…”

Section: Experience Mappingmentioning

confidence: 99%

“…The simulation and the experimental studies show that the EMPC achieves accurate position control for step and dynamic demands. A comparative study of performance of the position system based on the EMPC, PD and model reference adaptive controller (MRAC) in [35,36] shows that the EMPC performs better. EMPC presented in [35,36] uses a method of action where the voltage applied to the motor always generates torque to assist the rotation of the motor and not oppose it.…”

Section: Introductionmentioning

confidence: 98%

“…OJR is also accomplished only through new experiential learning and not with the help of a mathematical model of the system. EMPC was developed for a DC motor based precision positioning system [35,36]. The simulation and the experimental studies show that the EMPC achieves accurate position control for step and dynamic demands.…”

Section: Introductionmentioning

confidence: 98%

“…Inspired by the concepts of HMC, a new type of simple, effective and robust controller named experience mapping based prediction controller (EMPC) has been designed [35,36]. Similar to HMC, EMPC directly interacts with the system to be controlled, and develops experiences.…”

Section: Introductionmentioning

confidence: 99%

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A study of bipolar control action with EMPC for the position control of DC motors

Saikumar

Dinesh

2014

Int. J. Dynam. Control

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The paper studies the use of bipolar control action with experience mapping based prediction controller (EMPC) for the position control of DC motors. EMPC is based on the human learning mechanism without any need for a detailed mathematical plant model. Experiential learning is used in EMPC to control and adapt to environmental changes introducing robustness. An improved method for the development of experiences to achieve faster settling times is presented. A new concept of control action in EMPC to achieve faster response named 'bipolar action' is presented. The advantages of bipolar action are studied in terms of the transient response characteristics and adaptation to changes in system parameters. The performance of EMPC using bipolar action is compared against the use of unipolar action and also against the popular MRAC for position control for changes in active load torque, motor terminal voltage and armature resistance. A new method of correcting the control action by sampling the system response during the initial phase to reduce the overshoots in systems where overshoots are not tolerated is presented. EMPC with bipolar action is practically implemented and the results are provided and compared with the simulation results.

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Section: Initial Learningmentioning

confidence: 99%

Section: Experience Mappingmentioning

confidence: 99%