Non-parametric system identification with Gaussian processes for underwater vehicles is explored in this research with the purpose of modelling autonomous underwater vehicle(AUV) dynamics with a low amount of data. Mu...Non-parametric system identification with Gaussian processes for underwater vehicles is explored in this research with the purpose of modelling autonomous underwater vehicle(AUV) dynamics with a low amount of data. Multi-output Gaussian processes and their aptitude for modelling the dynamic system of an underactuated AUV without losing the relationships between tied outputs are used. The simulation of a first-principle model of a Remus 100 AUV is employed to capture data for the training and validation of the multi-output Gaussian processes. The metric and required procedure to carry out multi-output Gaussian processes for AUV with 6 degrees of freedom(DoF) is also shown in this paper. Multi-output Gaussian processes compared with the popular technique of recurrent neural network show that multi-output Gaussian processes manage to surpass RNN for non-parametric dynamic system identification in underwater vehicles with highly coupled DoF with the added benefit of providing the measurement of confidence.展开更多
System Identification becomes very crucial in the field of nonlinear and dynamic systems or practical systems.As most practical systems don’t have prior information about the system behaviour thus,mathematical modell...System Identification becomes very crucial in the field of nonlinear and dynamic systems or practical systems.As most practical systems don’t have prior information about the system behaviour thus,mathematical modelling is required.The authors have proposed a stacked Bidirectional Long-Short Term Memory(Bi-LSTM)model to handle the problem of nonlinear dynamic system identification in this paper.The proposed model has the ability of faster learning and accurate modelling as it can be trained in both forward and backward directions.The main advantage of Bi-LSTM over other algorithms is that it processes inputs in two ways:one from the past to the future,and the other from the future to the past.In this proposed model a backward-running Long-Short Term Memory(LSTM)can store information from the future along with application of two hidden states together allows for storing information from the past and future at any moment in time.The proposed model is tested with a recorded speech signal to prove its superiority with the performance being evaluated through Mean Square Error(MSE)and Root Means Square Error(RMSE).The RMSE and MSE performances obtained by the proposed model are found to be 0.0218 and 0.0162 respectively for 500 Epochs.The comparison of results and further analysis illustrates that the proposed model achieves better performance over other models and can obtain higher prediction accuracy along with faster convergence speed.展开更多
This study explores the potential of the advanced selective state spaces model(SSSM)in modeling complicated process industries system and proposes the process industry state identification model(PISIM)for controlled p...This study explores the potential of the advanced selective state spaces model(SSSM)in modeling complicated process industries system and proposes the process industry state identification model(PISIM)for controlled prediction of flotation cell pulp level.As a neural system identification model,the PISIM inherits two advantages of the SSSM to address the challenges in identifying flotation systems,including modeling the impact of frequent upstream fluctuations on system states,complex nonlinear physicochemical processes,and long-term dependencies.The first advantage is the ability to capture long-range dependencies,thereby boosting its long-term predictive accuracy.The second lies in the model structure adhering to scaling laws,enabling ongoing enhancements in performance as datasets expand.PISIM is evaluated using a real industrial dataset from a flotation plant at a copper mine in Zambia,with the results demonstrating its theoretical advantages.In a 4.5-hour pulp level prediction task,PISIM outperforms the baseline model by more than 31.34%.Furthermore,a flotation process control simulation experimental system based on PISIM is developed and deployed in a flotation plant in Zambia,assisting engineers in evaluating and optimizing setpoint strategies,ensuring stable production and improving production efficiency.展开更多
文摘Non-parametric system identification with Gaussian processes for underwater vehicles is explored in this research with the purpose of modelling autonomous underwater vehicle(AUV) dynamics with a low amount of data. Multi-output Gaussian processes and their aptitude for modelling the dynamic system of an underactuated AUV without losing the relationships between tied outputs are used. The simulation of a first-principle model of a Remus 100 AUV is employed to capture data for the training and validation of the multi-output Gaussian processes. The metric and required procedure to carry out multi-output Gaussian processes for AUV with 6 degrees of freedom(DoF) is also shown in this paper. Multi-output Gaussian processes compared with the popular technique of recurrent neural network show that multi-output Gaussian processes manage to surpass RNN for non-parametric dynamic system identification in underwater vehicles with highly coupled DoF with the added benefit of providing the measurement of confidence.
文摘System Identification becomes very crucial in the field of nonlinear and dynamic systems or practical systems.As most practical systems don’t have prior information about the system behaviour thus,mathematical modelling is required.The authors have proposed a stacked Bidirectional Long-Short Term Memory(Bi-LSTM)model to handle the problem of nonlinear dynamic system identification in this paper.The proposed model has the ability of faster learning and accurate modelling as it can be trained in both forward and backward directions.The main advantage of Bi-LSTM over other algorithms is that it processes inputs in two ways:one from the past to the future,and the other from the future to the past.In this proposed model a backward-running Long-Short Term Memory(LSTM)can store information from the future along with application of two hidden states together allows for storing information from the past and future at any moment in time.The proposed model is tested with a recorded speech signal to prove its superiority with the performance being evaluated through Mean Square Error(MSE)and Root Means Square Error(RMSE).The RMSE and MSE performances obtained by the proposed model are found to be 0.0218 and 0.0162 respectively for 500 Epochs.The comparison of results and further analysis illustrates that the proposed model achieves better performance over other models and can obtain higher prediction accuracy along with faster convergence speed.
基金supported by the National Natural Science Foundation of China(62506031,62332017,U22A2022)National Science and Technology Major Project of the Ministry of Science and Technology of China(2024ZD0608100).
文摘This study explores the potential of the advanced selective state spaces model(SSSM)in modeling complicated process industries system and proposes the process industry state identification model(PISIM)for controlled prediction of flotation cell pulp level.As a neural system identification model,the PISIM inherits two advantages of the SSSM to address the challenges in identifying flotation systems,including modeling the impact of frequent upstream fluctuations on system states,complex nonlinear physicochemical processes,and long-term dependencies.The first advantage is the ability to capture long-range dependencies,thereby boosting its long-term predictive accuracy.The second lies in the model structure adhering to scaling laws,enabling ongoing enhancements in performance as datasets expand.PISIM is evaluated using a real industrial dataset from a flotation plant at a copper mine in Zambia,with the results demonstrating its theoretical advantages.In a 4.5-hour pulp level prediction task,PISIM outperforms the baseline model by more than 31.34%.Furthermore,a flotation process control simulation experimental system based on PISIM is developed and deployed in a flotation plant in Zambia,assisting engineers in evaluating and optimizing setpoint strategies,ensuring stable production and improving production efficiency.
