A Survey on Learning-Based Model Predictive Control: Toward Path Tracking Control of Mobile Platforms

Zhang, Kanghua; Wang, Jixin; Xin, Xueting; Li, Xiang; Sun, Cheng; Huang, Jianfei; Kong, Weikang

doi:10.3390/app12041995

Cited by 11 publications

(3 citation statements)

References 126 publications

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“…Additionally, neural network-based modeling outperforms other data driven modeling methods due to its ability to implicitly detect complex nonlinearities and the availability of multiple training algorithms. By taking advantage of multiple learning algorithms or various training datasets, ensemble learning provides better predictive performance than a single machine learning model [7].…”

Section: Overview Of Machine Learningmentioning

confidence: 99%

Kiln Predictive Modelisation for Performance Optimization

BENCHEKROUN¹,

ZAKI

ABOUSSALEH³

2023

Preprint

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Exploiting the power of AI in the heavy industry brought back satisfactory results at the kiln level, machine learning techniques allowed predictive modeling of the baking process using powerful Machine Learning models which had a great impact on the energy consumption and kiln production rate among all the models used. Large amounts of historical data were used after analysis and preparation, for which several methods were applied such as preprocessing and feature selection. All models were tested on 20% of the data, using Mean Absolute Error and Root mean squared error as metrics to evaluate our models in order to identify the influencing variables that contribute most to the increase in energy consumption and kiln production rate.

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Section: Overview Of Machine Learningmentioning

confidence: 99%

Kiln Predictive Modelisation for Performance Optimization

BENCHEKROUN¹,

ZAKI

ABOUSSALEH³

2023

Preprint

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“…The mobile medical robot path tracking learning program has also been solved 30 . For a detailed report on trajectory tracking, readers can refer to Zhang 138 …”

Section: Application Of Model Predictive Control Based On Learning In...mentioning

confidence: 99%

Emerging methodologies in stability and optimization problems of learning‐based nonlinear model predictive control: A survey

Meng

Shen

Karimi

2022

Circuit Theory & Apps

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Since last 40 years, the theory and technology of model predictive control (MPC) have been developed rapidly. However, nonlinear MPC still faces difficulties such as high online computational complexity and inability to accurately model the system. In order to improve or solve the problems mentioned above of MPC, recent researches have deepened the learning‐based control. The learned method can model unknown or highly uncertain nonlinearities. And the emergence of efficient algorithms has greatly improved the feasibility of computing. Stability is at the heart of control design. Learning‐based nonlinear model predictive control (LB‐NMPC) has achieved systematic research results in the past 10 years. But the stability of LB‐NMPC is still an open question that has not been fully addressed in the literature. This review mainly summarizes the latest research progress of LB‐NMPC. More specifically, the uncertainty and online optimization problems of the considered systems are investigated mainly focusing on the use of learning techniques. At the same time, the research hotspots such as the control stability and constraint satisfaction of LB‐NMPC are briefly discussed. Finally, the application of LB‐NMPC technology in integrated circuits, path tracking control, and other fields is reviewed, which provides a reference for the research and application of LB‐NMPC.

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“…Nowadays, wheeled mobile robot (WMR), the fruit of combination of the latest sensing technology with advanced control strategies, becomes an important player in modern society [1], [2]. Due to their abilities to increase productivity, and improve work environments safety, WMRs are increasingly entrusted to occupy significant roles in variant sectors such as agriculture, space, surveillance, and mining [3]- [6].…”

Section: Introductionmentioning

confidence: 99%

Enhanced MPC for Omnidirectional Robot Motion Tracking Using Laguerre Functions and Non-Iterative Linearization

El-Sayyah

Saad

2022

IEEE Access

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To cope with the computational complexity of the traditional model predictive control, and to reduce the error of the linearization and prediction processes, this paper presents an improved model predictive control algorithm, based on Laguerre functions, for the motion tracking of an omnidirectional mobile robot with non-iterative linearization. To design the controller, the kinematic modeling of the threewheeled omnidirectional robot was first performed. Next, the model predictive algorithm was developed using Laguerre functions to parametrize the control signals. At each sampling instant of the online optimization, a linearization along the predicted trajectory, based on the duality principle between optimal control and stochastic filtering, was carried out to deal with the nonlinearities of the system. This non-iterative linearization provides better approximation of the nonlinear behavior which improves the prediction process and the tracking performance, with lower computational burden due to the use of the Laguerre functions. The new controller is applied to solve the trajectory-tracking problem of an omnidirectional robot. A comparative study between the proposed controller, the conventional model predictive control, and the nonlinear model predictive approach is made. Simulation results confirm that the new controller outperform the latter ones regarding tracking accuracy with considerably low computational effort. The feasibility of the controller is demonstrated by real-time experiment on the Robotino-Festo omnidirectional mobile robot.

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A Survey on Learning-Based Model Predictive Control: Toward Path Tracking Control of Mobile Platforms

Cited by 11 publications

References 126 publications

Kiln Predictive Modelisation for Performance Optimization

Kiln Predictive Modelisation for Performance Optimization

Emerging methodologies in stability and optimization problems of learning‐based nonlinear model predictive control: A survey

Enhanced MPC for Omnidirectional Robot Motion Tracking Using Laguerre Functions and Non-Iterative Linearization

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