Model Predictive Controller Based on Online Obtaining of Softness Factor and Fusion Velocity for Automatic Train Operation

Wang, Longda; Wang, Xingcheng; Sheng, Zhao; Lu, Senkui

doi:10.3390/s20061719

Cited by 7 publications

(4 citation statements)

References 44 publications

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“…Wang et al present in [ 44 ] an improved model predictive controller based on the online obtaining of softness factor and fusion velocity for automatic train operation to enhance the tracking control performance. Specifically, the softness factor of the improved model predictive control algorithm is not a constant, conversely, an improved online adaptive adjusting method for softness factor based on fuzzy satisfaction of system output value and velocity distance trajectory characteristic is adopted, and an improved whale optimization algorithm has been proposed to solve the adjustable parameters (see Figure 26 ).…”

Section: Driver Assistance Systems and Automatic Vehicle Operationmentioning

confidence: 99%

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Sensors and Sensing for Intelligent Vehicles

Llorca

Daza

Hernández

et al. 2020

Sensors

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Over the past decades, both industry and academy have made enormous advancements in the field of intelligent vehicles, and a considerable number of prototypes are now driving our roads, railways, air and sea autonomously. However, there is still a long way to go before a widespread adoption. Among all the scientific and technical problems to be solved by intelligent vehicles, the ability to perceive, interpret, and fully understand the operational environment, as well as to infer future states and potential hazards, represent the most difficult and complex tasks, being probably the main bottlenecks that the scientific community and industry must solve in the coming years to ensure the safe and efficient operation of the vehicles (and, therefore, their future adoption). The great complexity and the almost infinite variety of possible scenarios in which an intelligent vehicle must operate, raise the problem of perception as an "endless" issue that will always be ongoing. As a humble contribution to the advancement of vehicles endowed with intelligence, we organized the Special Issue on Intelligent Vehicles. This work offers a complete analysis of all the mansucripts published, and presents the main conclusions drawn.

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Section: Driver Assistance Systems and Automatic Vehicle Operationmentioning

confidence: 99%

“… Schematic diagram of the Fuzzy Dynamic Matrix Control (DMC) Model Predictive Controller (MPC) proposed in [ 44 ] for automatic train operation. …”

Section: Figurementioning

confidence: 99%

Sensors and Sensing for Intelligent Vehicles

Llorca

Daza

Hernández

et al. 2020

Sensors

View full text Add to dashboard Cite

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“…A whale optimization algorithm to tackle the three-dimensional path planning of autonomous underwater vehicles was proposed in [ 21 ]. An improved whale optimization algorithm based on the Tchebycheff decomposition method, convergence factor nonlinear decline strategy, and genetic evolution measurement for model predictive controller was proposed in [ 22 ]. However, there are few related works published on the active disturbance rejection controller based on the effective memetic algorithm for PMSM.…”

Section: Introductionmentioning

confidence: 99%

Modified ADRC Design of Permanent Magnet Synchronous Motor Based on Improved Memetic Algorithm

Liu

Wang

2023

Sensors

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In this paper, a novel modified auto disturbance rejection control (ADRC) design of a permanent magnet synchronous motor based on the improved memetic algorithm (IMA) is proposed. Firstly, there is an obvious system ripple caused by the defect that the optimal control function used in traditional ADRC cannot be differentiable and smooth at the segment point; aiming at weakening the system ripple effectively, the proposed method constructs a novel differentiable and smooth optimal control function to modify the ADRC design. Furthermore, aiming at improving the integration parameters optimization effect effectively, a novel improved memetic algorithm is proposed for obtaining the optimal parameters of ADRC. Specifically, an IMA with high-quality balance based on an adaptive nonlinear decreasing strategy for the convergence factor, Gaussian mutation mechanism, improved learning mechanism with the high-quality balance between competitive and opposition-based learning (OBL) and an elite set maintenance mechanism based on fusion distance is proposed so that these strategies can improve the optimization precision by a large margin. Finally, the experiment results of the PMSM speed control practical cases show that the ADRC based on IMA has an apparent better optimization effect than that of fuzzy PI, traditional ADRC based on the genetic algorithm and an improved ADRC based on improved moth–flame optimization.

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“…For instance, [24]- [26] proposed a fuzzy predictive control algorithm, which can effectively improve the passengers' riding comfort. To reduce large tracking error shortcomings, Liu, Wang, and others used the method of online adjustment of the predictive control softness factor to effectively improve the accuracy of train speed-tracking [27], [28]. Chu, Yu et al used the gray prediction GM (1, 1) model to design a train speed-tracking controller, which also effectively improved the speed-tracking accuracy [29].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Traction Control Method Based on Model Predictive Fuzzy PID Control and Online Optimization for Permanent Magnetic Maglev Trains

2021

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Considering that the speed control system of the suspended permanent magnetic maglev train is more complicated and the parameters are more unstable than those of other trains, the traditional speedtracking algorithm has large tracking errors, frequent controller output changes, high energy consumption, and decreasing the passengers' riding comfort. To improve the shortcomings of the traditional automatic train operation (ATO) control algorithm, this paper proposes a predictive fuzzy proportional-integralderivative control algorithm with weights (WM-F-PID). The main contribution of this work is to propose a cascaded predictive fuzzy PID (F-PID) control algorithm architecture with weights and use an improved steepest descent method to calculate online the weight of the F-PID controller input occupied by the predictive controller output. Compared with the proportional-integral-derivative (PID), F-PID, model predictive control (MPC), and simple cascade predictive fuzzy PID (M-F-PID) control algorithms, this control algorithm effectively improves train tracking accuracy and comfort and reduces train energy consumption and stopping errors.INDEX TERMS Suspended permanent magnetic maglev train, WM-F-PID control algorithm, Online optimization algorithm, Speed-tracking.

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Model Predictive Controller Based on Online Obtaining of Softness Factor and Fusion Velocity for Automatic Train Operation

Cited by 7 publications

References 44 publications

Sensors and Sensing for Intelligent Vehicles

Sensors and Sensing for Intelligent Vehicles

Modified ADRC Design of Permanent Magnet Synchronous Motor Based on Improved Memetic Algorithm

Intelligent Traction Control Method Based on Model Predictive Fuzzy PID Control and Online Optimization for Permanent Magnetic Maglev Trains

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