A recurrent neural network based MPC for a hybrid neuroprosthesis system

Bao, Xuefeng; Sun, Ziyue; Sharma, Nitin

doi:10.1109/cdc.2017.8264356

Cited by 17 publications

(11 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…LSTM network can capture the characteristics of time series in a longer time span and achieve better results than RNN in traffic prediction. In this section, LSTM is used to model the dynamics of the preceding train and predict the state of the preceding train for MPC due to its superiority compared to other conventional modeling methods [36].…”

Section: Lstm-based Model Predictive Controlmentioning

confidence: 99%

Long Short-Term Memory-Based Model Predictive Control for Virtual Coupling in Railways

Chai

Liu

2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

The increasing need for capacity has led the railway industry to explore new train control systems based on a concept called virtual coupling. Inspired by the platooning of autonomous vehicles, the safe operation of virtual coupling is guaranteed by a relative brake distance-based train separation method. This paper proposes a novel long short-term memory (LSTM)-based model predictive control (MPC) method for train operations. An MPC-based control design for virtual coupled train operations is presented. The LSTM is introduced to model the dynamics of the preceding train to approximate the actual train operations. With the train dynamics models, the operation trajectories of the preceding train are predicted based on planned control inputs. A study of a metro line in Chengdu was chosen to analyze the proposed control approach. The simulation results of different scenarios show that compared with the conventional MPC methods, the proposed LSTM-based MPC can reduce the speed differences and position differences of tracking trains by up to 35 % and 25 % , respectively.

show abstract

Section: Lstm-based Model Predictive Controlmentioning

confidence: 99%

Long Short-Term Memory-Based Model Predictive Control for Virtual Coupling in Railways

Chai

Liu

2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…Although the feedback mechanism of MPC tolerates some model mismatches for multiquadrotor system, MPC still demands that the prediction model be sufficiently accurate. In this section, RNN is considered as the prediction model for MPC, due to its superiority in comparison with other conventional modeling methods [40].…”

Section: Rnn-based Prediction Modelsmentioning

confidence: 99%

Recurrent Neural Network-Based Model Predictive Control for Multiple Unmanned Quadrotor Formation Flight

Zhang

Sun

Deng

2019

International Journal of Aerospace Engineering

View full text Add to dashboard Cite

This paper presents a dynamical recurrent neural network- (RNN-) based model predictive control (MPC) structure for the formation flight of multiple unmanned quadrotors. A distributed hierarchical control system with the translation subsystem and rotational subsystem is proposed to handle the formation-tracking problem for each quadrotor. The RNN-based MPC is proposed for each subsystem, where the RNN is introduced as the predictive model in MPC. And to improve the modeling accuracy, an adaptive updating law is developed to tune weights online for the RNN. Besides, the adaptive differential evolution (DE) algorithm is utilized to solve the optimization problem for MPC. Furthermore, the closed-loop stability is analyzed; meanwhile, the convergence of the DE algorithm is discussed as well. Finally, some simulation examples are provided to illustrate the validity of the proposed control structure.

show abstract

“…The proposed solution is shown in figure 8 wherê Γ corresponds to the estimated disturbance. The neural network can be tuned online as it has been done in (Bao et al, 2017). Note that this solution may cause instability due to the feedback created with the EKF.…”

Section: Robust Neural Network Based Predictormentioning

confidence: 99%

“…Because the system is unvarying against disturbances, we can conclude that the global new system can be modeled by a FNN trained online as it has been done in (Bao et al, 2017). The predictor that arises from this FNN will be insensitive to disturbances and thus be efficient.…”

Section: Implementation Of the Feedback Compensatormentioning

confidence: 99%

Enhancing Neural Network Prediction against Unknown Disturbances with Neural Network Disturbance Observer

Pouilly-Cathelain

Feyel

Duc

et al. 2019

Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics

View full text Add to dashboard Cite

Neural network prediction is a very challenging subject in the presence of disturbances. The difficulty comes from the lack of knowledge about perturbation. Most papers related to prediction often omit disturbances but, in a natural environment, a system is often subject to disturbances which could be external perturbations or also small internal parameters variations caused, for instance, by the ageing of the system. The aim of this paper is to realize a neural network predictor of a nonlinear system; for the predictor to be effective in the presence of varying perturbations, we provide a neural network observer in order to reconstruct the disturbance and compensate it, without any a priori knowledge. Once the disturbance is compensated, it is easier to realize such a global neural network predictor. To reach this goal we model the system with a State-Space Neural Network and use this model, completed with a disturbance model, in an Extended Kalman Filter.

show abstract

A recurrent neural network based MPC for a hybrid neuroprosthesis system

Cited by 17 publications

References 30 publications

Long Short-Term Memory-Based Model Predictive Control for Virtual Coupling in Railways

Long Short-Term Memory-Based Model Predictive Control for Virtual Coupling in Railways

Recurrent Neural Network-Based Model Predictive Control for Multiple Unmanned Quadrotor Formation Flight

Enhancing Neural Network Prediction against Unknown Disturbances with Neural Network Disturbance Observer

Contact Info

Product

Resources

About