Model-free Subspace Based Dynamic Control of Mechanical Manipulators

Simple Model-Free Controller for the Stabilization of Planetary Inverted Pendulum

Journal of Control Science and Engineering ◽

10.1155/2012/634985 ◽

2012 ◽

Vol 2012 ◽

pp. 1-8 ◽

Cited By ~ 1

Author(s):

Huanhuan Mai ◽

Ying-Jeh Huang ◽

Xiaofeng Liao ◽

Ping-Chou Wu

Keyword(s):

Simple Model ◽

Inverted Pendulum ◽

Fuzzy Controller ◽

Dynamic Control ◽

Planetary Gear ◽

Real Time Control ◽

Model Free ◽

Time Control ◽

Scaling Parameters ◽

Model Free Controller

A simple model-free controller is presented for solving the nonlinear dynamic control problems. As an example of the problem, a planetary gear-type inverted pendulum (PIP) is discussed. To control the inherently unstable system which requires real-time control responses, the design of a smart and simple controller is made necessary. The model-free controller proposed includes a swing-up controller part and a stabilization controller part; neither controller has any information about the PIP. Since the input/output scaling parameters of the fuzzy controller are highly sensitive, we use genetic algorithm (GA) to obtain the optimal control parameters. The experimental results show the effectiveness and robustness of the present controller.

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Open-loop Model-free Dynamic Control of a Soft Manipulator for Tracking Tasks

10.1109/icar53236.2021.9659370 ◽

2021 ◽

Author(s):

Andrea Centurelli ◽

Alessandro Rizzo ◽

Silvia Tolu ◽

Egidio Falotico

Keyword(s):

Dynamic Control ◽

Open Loop ◽

Loop Model ◽

Model Free ◽

Tracking Tasks ◽

Soft Manipulator ◽

Free Dynamic

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Physics-informed Dyna-style model-based deep reinforcement learning for dynamic control

Proceedings of The Royal Society A Mathematical Physical and Engineering Sciences ◽

10.1098/rspa.2021.0618 ◽

2021 ◽

Vol 477 (2255) ◽

Author(s):

Xin-Yang Liu ◽

Jian-Xun Wang

Keyword(s):

Reinforcement Learning ◽

Predictive Accuracy ◽

Dynamic Control ◽

Learning Performance ◽

Governing Equations ◽

Model Learning ◽

Physical Constraints ◽

Model Based ◽

Model Free

Model-based reinforcement learning (MBRL) is believed to have much higher sample efficiency compared with model-free algorithms by learning a predictive model of the environment. However, the performance of MBRL highly relies on the quality of the learned model, which is usually built in a black-box manner and may have poor predictive accuracy outside of the data distribution. The deficiencies of the learned model may prevent the policy from being fully optimized. Although some uncertainty analysis-based remedies have been proposed to alleviate this issue, model bias still poses a great challenge for MBRL. In this work, we propose to leverage the prior knowledge of underlying physics of the environment, where the governing laws are (partially) known. In particular, we developed a physics-informed MBRL framework, where governing equations and physical constraints are used to inform the model learning and policy search. By incorporating the prior information of the environment, the quality of the learned model can be notably improved, while the required interactions with the environment are significantly reduced, leading to better sample efficiency and learning performance. The effectiveness and merit have been demonstrated over a handful of classic control problems, where the environments are governed by canonical ordinary/partial differential equations.

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Representation, abstraction, and simple-minded sophisticates

Behavioral and Brain Sciences ◽

10.1017/s0140525x19002942 ◽

2020 ◽

Vol 43 ◽

Author(s):

Peter Dayan

Keyword(s):

Decision Theory ◽

Predictive Coding ◽

Bayesian Decision Theory ◽

Bayesian Decision ◽

Model Based ◽

Model Free

Abstract Bayesian decision theory provides a simple formal elucidation of some of the ways that representation and representational abstraction are involved with, and exploit, both prediction and its rather distant cousin, predictive coding. Both model-free and model-based methods are involved.

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