An Electromechanical Pendulum Robot Arm in Action: Dynamics and Control

Kadjié, Arnaud Notue; Tuwa, P.R. Nwagoum; Woafo, P.

doi:10.1155/2017/3979384

Cited by 5 publications

(2 citation statements)

References 30 publications

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“…Robotic limb behavior is like a controlled inverted pendulum. 10 Deep learning has made a huge contribution to the scalability and performance of machines. 11 The Sequential decision-making setting of reinforcement learning and control is an interesting application.…”

Section: Introductionmentioning

confidence: 99%

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A real-time HIL control system on rotary inverted pendulum hardware platform based on double deep Q-network

Dai

Lee

2021

Measurement and Control

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For real applications, rotary inverted pendulum systems have been known as the basic model in nonlinear control systems. If researchers have no deep understanding of control, it is difficult to control a rotary inverted pendulum platform using classic control engineering models, as shown in section 2.1. Therefore, without classic control theory, this paper controls the platform by training and testing reinforcement learning algorithm. Many recent achievements in reinforcement learning (RL) have become possible, but there is a lack of research to quickly test high-frequency RL algorithms using real hardware environment. In this paper, we propose a real-time Hardware-in-the-loop (HIL) control system to train and test the deep reinforcement learning algorithm from simulation to real hardware implementation. The Double Deep Q-Network (DDQN) with prioritized experience replay reinforcement learning algorithm, without a deep understanding of classical control engineering, is used to implement the agent. For the real experiment, to swing up the rotary inverted pendulum and make the pendulum smoothly move, we define 21 actions to swing up and balance the pendulum. Comparing Deep Q-Network (DQN), the DDQN with prioritized experience replay algorithm removes the overestimate of Q value and decreases the training time. Finally, this paper shows the experiment results with comparisons of classic control theory and different reinforcement learning algorithms.

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Section: Introductionmentioning

confidence: 99%

“…In order to swing up the rotary inverted pendulum, the action should be continuous. In the experiment, the voltage to control the DC motor is constrained in the range of[210,10] Volt. In the experiment, the e-greedy is set as 0.9.…”

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confidence: 99%