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1 |
An improved integral light-of-sight guidance law for path following of unmanned surface vehicles
یک قانون هدایت کننده نور بینایی یکپارچه برای پیگیری مسیر وسایل نقلیه سطحی بدون سرنشین-2020 This paper proposes a path following control system for Unmanned Surface Vehicles (USVs) based on an
improved integral Line-Of-Sight (LOS) guidance law. Unlike the conventional LOS guidance law, the lookahead
distance is designed as a function of the USV’s cruising speed and the cross tracking error to adapt
to the different cruising speeds of USVs. Meanwhile, a reduced-order state observer is developed for online
estimation of the time-varying sideslip angle caused by external disturbances such as wind, wave and current.
Then, a heading controller is further designed using the dynamic surface control technique to track the desired
heading angle. The guidance system and the reduced-order state observer subsystem are proved to be uniformly
asymptotically stable and input-to-state stable respectively. The simulation results show that the path following
control system designed in this paper can track the desired curved and straight line paths quickly and smoothly
at different cruising speeds. Keywords: Line-of-sight | Unmanned surface vehicle | Path following | Look-ahead distance | Reduced-order state observer | Dynamic surface control |
مقاله انگلیسی |
2 |
Deep reinforcement learning-based controller for path following of an unmanned surface vehicle
کنترلر مبتنی بر یادگیری تقویتی عمیق برای پیگیری مسیر یک وسیله نقلیه سطحی بدون سرنشین-2019 In this paper, a deep reinforcement learning (DRL)-based controller for path following of an unmanned surface
vehicle (USV) is proposed. The proposed controller can self-develop a vehicle’s path following capability by
interacting with the nearby environment. A deep deterministic policy gradient (DDPG) algorithm, which is
an actor-critic-based reinforcement learning algorithm, was adapted to capture the USV’s experience during
the path-following trials. A Markov decision process model, which includes the state, action, and reward
formulation, specially designed for the USV path-following problem is suggested. The control policy was trained
with repeated trials of path-following simulation. The proposed method’s path-following and self-learning
capabilities were validated through USV simulation and a free-running test of the full-scale USV. Keywords: Deep reinforcement learning | Path following | Unmanned surface vehicle | Learning-based control | Artificial intelligence |
مقاله انگلیسی |