In this paper, a sensorless hybrid control scheme for brushless DC (BLDC) motors for use in multirotor aerial vehicles is introduced. In such applications the control scheme must satisfy high performance demands for a wide range of rotor speeds and must be robust to motor parameter uncertainties and measurement noise. The proposed controller combines Field-Oriented Control (FOC) and Direct Torque Control (DTC) techniques to take benefit of the advantages offered by each of these techniques individually. Simulation results demonstrate the effectiveness of the proposed control scheme over a wide range of rotor speeds as well as good robustness against parameter uncertainties within −5.. + 10% for inductance and −5.. + 5% for resistance parameters. The proposed hybrid controller is robust also against noise in voltage and current measurements. In order to verify the results from simulation, the proposed hybrid controller is implemented in hardware using the TI C2000 Piccolo Launchpad and TI BOOSTXL-DRV8305EVM BoosterPack. Testing is done with a Bull Running motor typically used in aerial drones. Testing experiments demonstrate that the hybrid controller reduces the rotor speed ripple when compared to DTC while operating in steady-state mode and decreases the response time to desired speed changes when compared to FOC.
The main contribution of this paper is to introduce a computationally efficient iterative closest line (ICL) algorithm for determining indoor position drift of a quadcopter using minimal lidar data. In addition, we present the system-level design and implementation of a new quadcopter both as hardware and flight control algorithms. Such a platform allows us to develop and experiment new control and system optimization techniques. As an example, we discuss how the proposed ICL algorithm is used for position hold and control purposes by plugging it into the low level implementation of the flight control algorithm of the quadcopter. For testing and validation we use simulations with real world data. As part of the system-level design aspects, we present an investigation of the quadcopter power consumption. We are interested in power consumption because it is the major factor that determines the flight time of a typical quadcopter. We believe that this work is a contribution toward achieving better quadcopter design and control for indoor autonomous navigation.
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