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Kuan, Cheng-Peng; Young, Kuu‐Young

doi:10.1023/a:1008083631190

Cited by 19 publications

(1 citation statement)

References 28 publications

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“…In an inverted pole-balancing issue, Schaal [151] employed RL for robot learning. For compliance tasks, Kuan and Young [152] developed an RL-based mechanism in conjunction with a robust sliding mode impedance controller, which they evaluated in simulation.…”

Section: Reinforcement Learning For Robotic Controlmentioning

confidence: 99%

Evolution of adaptive learning for nonlinear dynamic systems: a systematic survey

Harib¹,

Chaoui²,

Miah³

2022

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The extreme nonlinearity of robotic systems renders the control design step harder. The consideration of adaptive control in robotic manipulation started in the 1970s. However, in the presence of bounded disturbances, the limitations of adaptive control rise considerably, which led researchers to exploit some “algorithm modifications”. Unfortunately, these modifications often require a priori knowledge of bounds on the parameters and the perturbations and noise. In the 1990s, the field of Artificial Neural Networks was hugely investigated in general, and for control of dynamical systems in particular. Several types of Neural Networks (NNs) appear to be promising candidates for control system applications. In robotics, it all boils down to making the actuator perform the desired action. While purely control-based robots use the system model to define their input-output relations, Artificial Intelligence (AI)-based robots may or may not use the system model and rather manipulate the robot based on the experience they have with the system while training or possibly enhance it in real-time as well. In this paper, after discussing the drawbacks of adaptive control with bounded disturbances and the proposed modifications to overcome these limitations, we focus on presenting the work that implemented AI in nonlinear dynamical systems and particularly in robotics. We cite some work that targeted the inverted pendulum control problem using NNs. Finally, we emphasize the previous research concerning RL and Deep RL-based control problems and their implementation in robotics manipulation, while highlighting some of their major drawbacks in the field.

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Section: Reinforcement Learning For Robotic Controlmentioning

confidence: 99%