| Penulis/Author |
Dr. Atikah Surriani, S.T., M.Eng. (1) ; Prof. Ir. Oyas Wahyunggoro, MT., Ph.D. (2); Dr.Eng. Ir. Adha Imam Cahyadi, S.T., M.Eng., IPM. (3) |
| Abstrak/Abstract |
Research about air-craft autonomous vehicles
has become a trend issue among researchers. Vertical Take-Off
and Landing (VTOL) controlling for a hover-craft vehicle is one
of benchmarking control problems. VTOL ability of hover-craft
is significantly crucial for the vehicle to do surveillance and
rescue, military, farming, etc. There are many papers concerned
with this VTOL benchmarking control. The purpose of this
paper is applying Deep Deterministic Policy Gradient (DDPG)
as the brand new continuous deep reinforcement learning
approach to solve a complex non-linear system, such as air-craft
VTOL problem. Furthermore, this paper analyses the effect of
Ornstein-Uhlenbeck (OU) Noise injection to action as the
exploration key issue of DDPG algorithm, in addition of the
analysis, this paper also applies the gaussian noise to the plant
as the disturbance of system. The test was performed in the
VTOL simulation. The evaluation used the episode reward,
average reward, total reward from the training process. Further
analysis also uses IAE (Integral of Absolute Magnitude of
Error), ISE (Integral of the Square Error) and MSE (Mean of
the Square Error) as the parameter error evaluation from the
system. The comparison results of all conditions this paper show
that applying DDPG with the injection of OU noise to VTOL
system with the disturbance of Gaussian noise achieves the best
total reward of -14,355.37 with the shortest episode training of
243, thus it works best. The VTOL system without the
disturbance has higher IAE and VTOL with Gaussian noise as
the disturbance has higher ISE and MSE. |
