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

Abstract

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.

Stability analysis and efficiency of EMPC for Type-1 systems

“…The energy dissipated also matches closely with that of LQG. [16] show that system constant of proportionality K sa varies with demand and control action parameter T 0 . The nonconstant value of K sa can be attributed to the nonlinearities present in practical systems like dry friction and stiction.…”

“…In Fig.27, the rise time of EMPC is more than that of PD. In [16], EMPC proposes another control action termed bipolar action. Here, during the learning phase, EMPC applies a pulse width of fixed duration followed by another pulse width in the negative direction until the motor stops moving.…”

“…After learning is completed, during the application of the control action when a demand is given, EMPC will refer to the EMK and interpolate the required value of T 0 for the given demand. This method of using an EMK for a practical system has been shown to be robust for different demands and also shown to adapt to changes in system parameter [15] [20] [16].…”

“…Recently, a new control algorithm called the Experience Mapping based Predictive Controller (EMPC) was developed for position control [14] [15] [16] [17] and speed control [18] [19] of a DC motor and shown to outperform other robust controllers. The concepts were further improved to extend the control for underdamped Type-1 systems in [20] [21].…”