Brushless DC Motor Dual Closed-Loop (Speed and Current) Control System

The dual closed-loop control system for brushless DC motors (BLDC) represents an efficient and precise driving solution. Through the coordinated operation of both speed and current feedback loops, it significantly enhances the motor's dynamic response and steady-state performance.

The outer speed loop handles macroscopic regulation by calculating the difference between target speed and actual speed. Using control algorithms like PID (Proportional-Integral-Derivative), it generates reference current values. This loop ensures rapid tracking of setpoint speeds and maintains stable operation during load variations. In code implementation, the PID controller typically uses functions like pid() or pidtune() in MATLAB/Simulink, with parameters adjusted through tuning algorithms such as Ziegler-Nichols or auto-tuning methods.

The inner current loop employs hysteresis control strategy to directly regulate phase currents. Hysteresis control operates by setting upper and lower threshold limits - when current exceeds these boundaries, it immediately switches power device states (such as MOSFET turn-on/off) to force current back within the set range. This approach offers fast response and eliminates need for complex modulation algorithms, though variable switching frequency may cause noise issues. Implementation typically involves comparator functions and digital I/O controls in embedded systems, with threshold values programmed based on motor specifications.

During simulation, special attention must be paid to the coupling effects between loops: the outer loop's output serves as the input target for the inner loop, while the inner loop's dynamic performance affects the outer loop's adjustment effectiveness. Proper design of speed loop bandwidth and hysteresis tolerance can prevent system oscillation while balancing response speed and current ripple. Typical applications include drone electronic speed controllers (ESC) and precision servo systems, where simulation models often use transfer function blocks and state-space representations to analyze system stability.