Robust adaptive control of DC motor system fed by Buck converter

Abstract

For the Buck-

Sensorless Tracking Control for a “Full-Bridge Buck Inverter–DC Motor” System: Passivity and Flatness-Based Design

“…By exploiting the flatness of the system, for the angular velocity tracking task, Silva-Ortigoza et al explained two hierarchical robust controls; in 2013 a sensorless control approach based on integral reconstructors [10] and in 2014 the experimental verification of a hierarchical control based on flatness [11]. An adaptive robust control based on dynamic surface control and sliding mode control (SMC) was exhibited by Wei et al [12]. In 2015, Silva-Ortigoza et al exposed a hierarchical controller composed of two independent controls; one via differential flatness (for the DC motor) and the other one via a cascade scheme through SMC and PI control (for the Buck converter), which were interconnected in order to work as a whole [13].…”

New “Full-Bridge Buck Inverter–DC Motor” System: Steady-State and Dynamic Analysis and Experimental Validation

“…In accordance with the aforementioned, different approaches have been proposed for a DC motor fed by a DC/DC Buck converter when the unidirectional rotation of the motor shaft is considered [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]. This unidirectional rotation emerges because the Buck converter only delivers unipolar voltages.…”

A Robust Differential Flatness-Based Tracking Control for the “MIMO DC/DC Boost Converter–Inverter–DC Motor” System: Experimental Results

“…Other control techniques for the DC motor fed by a DC/DC Buck converter are proposed as well: zero average dynamics and fixed-point inducting control [10], [11], active disturbance rejection and flatness based control [12], adaptive control using sliding modes with dynamic surface [13], passivity and flatness based control with load torque estimation methods [14], sliding mode control and PI controls [15], fractional order PID control [16], [17], adaptive backstepping control [18], neuro-adaptive backstepping control [19], GPI based model predictive control [20], fuzzy logic based control [21], third order sliding mode control [22], output feedback disturbance rejection control [23], flatness based control in successive loops [24], predictive control via a discrete-time reduced-order GPI observer [25].…”