Research on Motion Planning of Seven Degree of Freedom Manipulator Based on DDPG

Liu, Lilan; Chen, En-lai; Gao, Zenggui; Wang, Yi

doi:10.1007/978-981-13-2375-1_44

Cited by 6 publications

(5 citation statements)

References 3 publications

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“…where S is the set of states of the agent and the environment, A is the set of actions executed by the agent, P is the model of the system-in other words, it is the transition probability of a state-R is the reward function, and γ is a discount factor [10]. The DRL objective function has two forms: the first is a value function that defines the expectation of the accumulated reward.…”

Section: Deep Reinforcement Learningmentioning

confidence: 99%

A Deep Reinforcement Learning Algorithm for Robotic Manipulation Tasks in Simulated Environments

Calderon-Cordova,

Sarango

2023

XXXI Conference on Electrical and Electronic Engineering

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Section: Deep Reinforcement Learningmentioning

confidence: 99%

A Deep Reinforcement Learning Algorithm for Robotic Manipulation Tasks in Simulated Environments

Calderon-Cordova,

Sarango

2023

XXXI Conference on Electrical and Electronic Engineering

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“…In those models, machine learning can help forecast solar energy output [14]. Otherwise, the authors combined LSTM with CNN, wavelet packet decomposition (WPD), wavelet transform (WT), and other methods, and combined the particle swarm algorithm (PSO) with the adaptive neuro-fuzzy inference system (ANFIS) to improve the performance, stability, and reliability of model extraction data features [15][16][17][18]. e authors applied the optimal frequency domain decomposition method to deep learning and used correlation to obtain the optimal frequency cutoff points of the decomposition components [19].…”

Section: Related Workmentioning

confidence: 99%

A Power Forecasting Method for Ultra-Short-Term Photovoltaic Power Generation Using Transformer Model

Tian

Fan

Wang

et al. 2022

Mathematical Problems in Engineering

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The volatility of solar energy, geographic location, and weather factors continues to affect the stability of photovoltaic power generation, reliable and accurate photovoltaic power prediction methods not only effectively reduce the operating cost of the photovoltaic system but also provide reliable data support for the energy scheduling of the light storage microgrid, improve the stability of the photovoltaic system, and provide important help for the optimization operation of the photovoltaic system. Therefore, it is an important study to find reliable photovoltaic power prediction methods. In recent years, researchers have improved the accuracy of photovoltaic power generation forecasting by using deep learning models. Compared with the traditional neural network, the Transformer model can better learn the relationship between weather features and has good stability and applicability. Therefore, in this paper, the transformer model is used for predicting ultra-short-term photovoltaic power generation, and the photovoltaic power generation data and weather data in Hebei are selected. In the experiment, the prediction result of the transformer model was compared to the GRU and DNN models to show that the transformer model has better predictive ability and stability. Experimental results demonstrated that the proposed Transformer model outperforms the GRU model and DNN model by a difference of about 0.04 kW and 0.047 kW in the MSE value, and 22.0% and 29.1% of the MAPE error. In addition, the public DC competition dataset is selected for control experiments to demonstrate the general applicability of the transformer model for PV power prediction in different regions.

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“…After storing enough experience in the replay buffer, the optimal strategy is learned by random sampling in small batches. The update of the critic online network is updated by (21), and the action network is updated by (22). After each training step, the target critic network and the target action network are slowly updated by (23) and (24).…”

Section: Thickness and Tension Control Frameworkmentioning

confidence: 99%

“…In recent years, Deep Reinforcement Learning (DRL) has attracted wide attention in solving high-dimensional control with high complexity [19]. DDPG is one of the model-free DRL methods for continuous action spaces and has been widely applied in many fields, such as robotic control [20], manipulator control [21] and wireless sensors [22].…”

Section: Introductionmentioning

confidence: 99%

DDPG-based continuous thickness and tension coupling control for the unsteady cold rolling process

Zeng

Wang

Zhang

et al. 2022

Int J Adv Manuf Technol

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Cold rolling is an important part of the iron and steel industry, and the unsteady rolling process of cold rolling usually brings significant influences on the stability of product quality. In the unsteady rolling process, various disturbances and uncertainties such as variable lubrication state, variable equipment working conditions lead to difficulties in the establishment of state space model of thickness and tension, which has become a thorny problem in thickness and tension control. In this paper, we present a model-free controller based on Deep Deterministic Policy Gradient(DDPG), which can continuously control the thickness tension of the unsteady rolling process without the mathematical model. We first formulate the thickness and tension control problem to Markov Decision Process(MDP). We apply strategies such as dividing state space variables with mechanism model, defining reward function and state normalization, the random disturbance and complex uncertainties of unsteady cold rolling process are coped with by utilizing the DDPG controller. In addition, these strategies also ensure the learning performance and stability of the DDPG controller under random disturbance. Simulations and experiments show that the proposed the DDPG controller does not require any prior knowledge of uncertain parameters and can operate without knowing unsteady rolling mathematical models, which has better accuracy, stability and rapidity for thickness and tension in the unsteady rolling process than proportional integral(PI) controller. The artificial intelligence-based controller brings both product quality improvement and intelligence to cold rolling.

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Research on Motion Planning of Seven Degree of Freedom Manipulator Based on DDPG

Cited by 6 publications

References 3 publications

A Deep Reinforcement Learning Algorithm for Robotic Manipulation Tasks in Simulated Environments

A Deep Reinforcement Learning Algorithm for Robotic Manipulation Tasks in Simulated Environments

A Power Forecasting Method for Ultra-Short-Term Photovoltaic Power Generation Using Transformer Model

DDPG-based continuous thickness and tension coupling control for the unsteady cold rolling process

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