1、Multi-Agent Quadrotor Testbed Control Design: Integral Sliding Mode vs. Reinforcement Learning Steven L. Waslander, Gabriel M. Hoffmann Ph.D. Candidate Aeronautics and Astronautics Stanford University stevenw, gabehstanford.edu Jung Soon Jang Research Associate Aeronautics and Astronautics Stanford
2、University jsjangstanford.edu Claire J. Tomlin Associate Professor Aeronautics and Astronautics Stanford University tomlinstanford.edu AbstractThe Stanford Testbed of Autonomous Rotorcraft for Multi-Agent Control (STARMAC) is a multi-vehicle testbed currently comprised of two quadrotors, also called
3、 X4-yers, with capacity for eight. This paper presents a comparison of control design techniques, specically for outdoor altitude control, in and above ground effect, that accommodate the unique dynamics of the aircraft. Due to the complex airow in- duced by the four interacting rotors, classical li
4、near techniques failed to provide sufcient stability. Integral Sliding Mode and Reinforcement Learning control are presented as two design techniques for accommodating the nonlinear disturbances. The methods both result in greatly improved performance over classical control techniques. I. INTRODUCTI
5、ON As rst introduced by the authors in 1,the Stanford Testbed of Autonomous Rotorcraft for Multi-Agent Control(STARMAC) is an aerial platform intended to validate novel multi-vehicle control techniques and present real-world problems for further investigation.The base vehicle for STARMAC is a four r
6、otor aircraft with xed pitch blades, referred to as a quadrotor, or an X4-yer.They are capable of 15 minute outdoor ights in a 100m square area1. Fig. 1. One of the STARMAC quadrotors in action. There have been numerous projects involving quadrotors to date,with the rst known hover occurring in Octo
7、ber,19222. Recent interest in the quadrotor concept has been sparked by commercial remote control versions, such as the DraganFlyer IV3. Many groups 47have seen significant success in developing autonomous quadrotor vehicles. To date,however,STARMAC is the only operational multi-vehicle quadrotor pl
8、atform capable of autonomous outdoor ight, without tethers or motion guides. The rst major milestone for STARMAC was autonomous hover control,with closed loop control of attitude, altitude and position. Using inertial sensing, the attitude of the aircraft is simple to control, by applying small vari
9、ations in the relative speeds of the blades. In fact, standard integral LQR techniques were applied to provide reliable attitude stability and tracking for the vehicle.Position control was also achieved with an integral LQR, with careful design in order to ensure spectral separation of the successiv
10、e loops. Unfortunately, altitude control proves less straightforward. There are many factors that affect the altitude loop specifically that do not amend themselves to classical control techniques. Foremost is the highly nonlinear and destabilizing effect of four rotor downwashes interacting. In our
11、 experience, this effect becomes critical when motion is not damped by motion guides or tethers. Empirical observation during manual ight revealed a noticeable loss in thrust upon descent through the highly turbulent ow eld.Similar aerodynamic phenomenon for helicopters have been studied extensively
12、8, but not for the quadrotor, due to its relative obscurity and complexity. Other factors that introduce disturbances into the altitude control loop include blade ex, ground effect and battery discharge dynamics. Although these effects are also present in generating attitude controlling moments, the
13、 differential nature of the control input eliminates much of the absolute thrust disturbances that complicate altitude control. Additional complications arise from the limited choice in low cost, high resolution altitude sensors. An ultrasonic ranging device9 was used, which suffers from non-Gaussia
14、n noise-false echoes and dropouts. The resulting raw data stream includes spikes and echoes that are difcult to mitigate, and most successfully handled by rejection of infeasible measurements prior to Kalman ltering. In order to accommodate this combination of noise and disturbances, two distinct approaches are adopted. Integral Sliding Mode (ISM) control1012 takes the approach that the disturbances
