倒立摆毕业设计外文翻译MultiAgent旋翼试验台控制系统设计.doc

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, gabeh}@stanford.edu Jung Soon Jang Research Associate Aeronautics and Astronautics Stanford University jsjang@stanford.edu Claire J. Tomlin Associate Professor Aeronautics and Astronautics Stanford University tomlin@stanford.edu Abstract—The Stanford Testbed of Autonomous Rotorcraft for Multi-Agent Control (STARMAC) is a multi-vehicle testbed currently comprised of two quadrotors, also called X4-?yers, with capacity for eight. This paper presents a comparison of control design techniques, speci?cally for outdoor altitude control, in and above ground effect, that accommodate the unique dynamics of the aircraft. Due to the complex air?ow in- duced by the four interacting rotors, classical linear techniques failed to provide suf?cient 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. INTRODUCTION 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 rotor 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 area[1]. 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 October,1922[2]. Recent interest in the quadrotor concept has been sparked by commercial remote control versions, such as the DraganFlyer IV[3]. Many groups [4]–[7]have seen sign