Sensorless Speed Control of Brushed DC Motor Based at New Current Ripple Component Signal Processing

Abstract

Signal processing of the brushed DC motor current was developed in this paper to obtain information about a rotor speed from a measured motor current. The brushed DC motor current contains a signal with a frequency proportional to the rotor speed. This signal is the outcome of a commutation process occurring in the brushed DC motor, and it is called a ripple component. Since the number of ripples in the measured motor current per one rotation is constant, the rotor speed can be estimated. A discrete bandpass filter with a floating bandwidth was developed as the main part of signal processing. This new interpretation of the bandpass filter was used to extract a frequency of the ripple component from the measured motor current. This frequency was used to acquire information about the estimated rotor speed. The estimated speed was set as a feedback value to a cascade control structure to provide sensorless speed control. The advantages and limitations of this approach are presented in this paper. Based on simulations and experimental results, it was confirmed that the proposed sensorless speed control is robust, accurate, and works precisely in a wide range of speeds.

Impacts of Discretization and Numerical Propagation on the Ability to Follow Challenging Square Wave Commands

“…Reference [7] illustrates the efficacy of such approaches to guide autonomous underwater vehicles through simulated minefields illustrated in Figure 3a,b. The feedforward elements were used to develop deterministic artificial intelligence through maturation as applied in so-called physics-based methods championed by Lorenz [16] and his students [11,[17][18][19][20][21][22][23][24] for many years, which also extended the method from vehicles to actuator control circuits where The development of adaptive and learning systems has a long, distinguished lineage in the literature with many optional techniques available to choose from. The trendsetting work of Isidori and Byrnes [36] on the control of exogenous signals revealed the close tie between the nonlinear regulator equations and the output regulation of a nonlinear system.…”

“…The momentum continued, and the nonlinear output regulation has been further explored by numerous authors including Cheng, Tarn, and Spurgeon [37], Khalil [38], and Wang and Huang [39] across autonomous and nonautonomous systems. The lineage emphasized in this manuscript stems from a heritage in vehicle guidance and control techniques [8][9][10][11][12][13][14][15] extended to apply to motor controllers [17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35] that generate vehicle motion. Vehicle maneuvering is controlled by the actuator fins displayed in Figure 3b generating navigation as displayed in Figure 3a.…”

“…Reference [7] illustrates the efficacy of such approaches to guide autonomous underwater vehicles through simulated minefields illustrated in Figure 3a,b. The feedforward elements were used to develop deterministic artificial intelligence through maturation as applied in so-called physics-based methods championed by Lorenz [16] and his students [11,[17][18][19][20][21][22][23][24] for many years, which also extended the method from vehicles to actuator control circuits where representative results following challenging discontinuous commands are depicted in Figure 4. Zhang et al [17] illustrated fault-tolerance, while Apoorva et al [18] revealed loss reduction and Flieh et al demonstrated loss minimization [19] and dead-beat control [20] in addition to self-sensing [21], the precursor to using the physics-based dynamics for virtual sensing [22] following the illustration of optimality in [23] and self-sensing [24] specifically applied to DC motors.…”

Evaluation of Adaptive and Learning in Unmanned Systems

“…al, [19] revealed loss reduction and Flieh et. al, demonstrated loss minimization [20] and dead-beat control [21] in addition to self-sensing [22,23], the precursor to using the physics-based dynamics for virtual sensoring [24] following the illustration of optimality in [25] and selfsensoring [26] specifically applied to DC motors.…”

“…The momentum continued, and the nonlinear output regulation has been further explored by numerous authors including Cheng, Tarn, and Spurgeon [39], Khalil [40], and Wang and Huang [41] across autonomous and nonautonomous systems. The lineage emphasized in this manuscript stems from a heritage in vehicle guidance and control techniques [8][9][10][11][12][13][14][15][16] extended to apply to motor controllers [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37] This manuscript proposes a preferred instantiation of adaptive and learning systems [28,29] by evaluating the efficacy of motor control techniques based on iterated computational rates and system discretization. The materials and methods in section 2 first describe model discretization and then introduces the two compared: one adaptive and one learning each with interconnected lineage of research in the literature.…”