Continuous trajectory tracking control of quadrotors is complicated when considering noise from the environment. Due to the difficulty in modeling the environmental dynamics, tracking methodologies based on conventional control theory, such as model predictive control, have limitations on tracking accuracy and response time. We propose a Time-attenuating Twin Delayed DDPG, a model-free algorithm that is robust to noise, to better handle the trajectory tracking task. A deep reinforcement learning framework is constructed, where a time decay strategy is designed to avoid trapping into local optima. The experimental results show that the tracking error is significantly small, and the operation time is one-tenth of that of a traditional algorithm. The OpenAI Mujoco tool is used to verify the proposed algorithm, and the simulation results show that, the proposed method can significantly improve the training efficiency and effectively improve the accuracy and convergence stability.
翻译:在考虑来自环境的噪音时,对四重体的持续轨迹跟踪控制十分复杂。由于在环境动态模型上存在困难,基于常规控制理论(如模型预测控制)的跟踪方法对跟踪准确性和反应时间有限制。我们提议采用一个时间简化双延延迟 DDPG,这是一个对噪音具有强大作用的无模型算法,以更好地处理轨迹跟踪任务。建立了一个深度强化学习框架,设计了时间衰减战略,以避免被困在本地optima。实验结果表明,跟踪错误非常小,操作时间是传统算法的十分之一。OpenAI Mujoco工具用于核实拟议的算法,模拟结果表明,拟议的方法可以大大提高培训效率,有效地提高准确性和汇合稳定性。