Abstract
Compared with current mode flywheel torque controller, speed mode torque controller has superior disturbance rejection capability. However, the speed loop delay reduces system dynamic response speed. To solve this problem, a two-degrees-of-freedom controller (2DOFC) which consists of a feedback controller (FBC) and a command feedforward controller (FFC) is proposed. The transfer function of FFC is found based on the inverse model of motor drive system, whose parameters are identified by recursive least squares (RLS) algorithm in real-time. Upon this, Kalman filter with softening factor is introduced for the improved parameters identification and torque control performances. Finally, the validity and the superiority of the proposed control scheme are verified through experiments with magnetically suspended flywheel (MSFW) motor.
Highlights
Flywheels (FWs) are the key actuators for the attitude control of spacecrafts [1]
It mainly consists of a parameter identifier, a Kalman filter, and a 2DOFC, in which 2DOFC is composed of a feedback controller (FBC) and a feedforward controller (FFC), among which recursive least squares (RLS) algorithm is used to estimate the unknown parameters, which matches the input-output behaviour of the real electromechanical system
The validity of the flywheel torque control scheme is verified by the magnetically suspended flywheel (MSFW) motor which is used for offering torque to regulating satellite attitude
Summary
Flywheels (FWs) are the key actuators for the attitude control of spacecrafts [1]. Through applying suitable output torque to the motor shaft, the flywheel reaction momentum can change the spacecraft axial position accurately. As for the speed mode control system, it has improved steady state performance when the accuracy of speed feedback information is sufficiently high. The MRASbased method estimates inertia using Landau’s discrete timerecursive parameter identification [12] It has the advantage of simple implementation. In [13], a sliding mode observer based on the LuGre friction model is proposed to estimate the friction It needs a low-pass filter to extract the estimated signal, which will induce the phase lag and affect the estimation accuracy. To obtain the fast and precise flywheel torque, this paper analyzes the factors that influence the speed mode control system performance. Considering that the disturbance and noise would influence the identification accuracy, Kalman filter with softening factor is employed for the improved performance
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