A study of bipolar control action with EMPC for the position control of DC motors: Categories 1,5

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.