scholarly journals A New Path Tracking Method Based on Multilayer Model Predictive Control

2019 ◽  
Vol 9 (13) ◽  
pp. 2649 ◽  
Author(s):  
Guoxing Bai ◽  
Yu Meng ◽  
Li Liu ◽  
Weidong Luo ◽  
Qing Gu ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Real Time ◽  
Predictive Control ◽  
Control Method ◽  
Control Period ◽  
Path Tracking ◽  
Positioning Systems ◽  
The Real ◽  
Time Performance ◽  
Tracking Controllers

At present, many path tracking controllers are unable to actively adjust the longitudinal velocity according to path information, such as the radius of the curve, to further improve tracking accuracy. For this problem, we propose a new path tracking framework based on model predictive control (MPC). This is a multilayer control system that includes three path tracking controllers with fixed velocities and a velocity decision controller. This new control method is named multilayer MPC. This new control method is compared to other control methods through simulation. In this paper, the maximum values of the displacement error and the heading error of multilayer MPC are 92.92% and 77.02%, respectively, smaller than those of nonlinear MPC. The real-time performance of multilayer MPC is very good, and parallel computation can further improve the real-time performance of this control method. In simulation results, the calculation time of multilayer MPC in each control period does not exceed 0.0130 s, which is much smaller than the control period. In addition, when the error of positioning systems is at the centimeter level, the performance of multilayer MPC is still good.

scholarly journals Review and Comparison of Path Tracking Based on Model Predictive Control

Electronics ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 1077 ◽  
Author(s):  
Guoxing Bai ◽  
Yu Meng ◽  
Li Liu ◽  
Weidong Luo ◽  
Qing Gu ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Real Time ◽  
Predictive Control ◽  
Path Tracking ◽  
Error Model ◽  
The Real ◽  
Time Performance ◽  
Reference Path ◽  
Reference Velocity ◽  
Positioning Errors

Recently, model predictive control (MPC) is increasingly applied to path tracking of mobile devices, such as mobile robots. The characteristics of these MPC-based controllers are not identical due to the different approaches taken during design. According to the differences in the prediction models, we believe that the existing MPC-based path tracking controllers can be divided into four categories. We named them linear model predictive control (LMPC), linear error model predictive control (LEMPC), nonlinear model predictive control (NMPC), and nonlinear error model predictive control (NEMPC). Subsequently, we built these four controllers for the same mobile robot and compared them. By comparison, we got some conclusions. The real-time performance of LMPC and LEMPC is good, but they are less robust to reference paths and positioning errors. NMPC performs well when the reference velocity is high and the radius of the reference path is small. It is also robust to positioning errors. However, the real-time performance of NMPC is slightly worse. NEMPC has many disadvantages. Like LMPC and LEMPC, it performs poorly when the reference velocity is high and the radius of the reference path is small. Its real-time performance is also not good enough.

Fast Model Predictive Control for Aircraft Engine Based on Automatic Differentiation

2020 ◽  
Author(s):  
Yunlai Wang ◽  
Xi Wang
Keyword(s):  
Model Predictive Control ◽  
Predictive Model ◽  
Real Time ◽  
Predictive Control ◽  
Automatic Differentiation ◽  
Hessian Matrix ◽  
Aircraft Engine ◽  
Component Level ◽  
The Real ◽  
Time Performance

Abstract Nonlinear model predictive control (NMPC) is a strategy suitable for dealing with highly complex, nonlinear, uncertain, and constrained dynamics involved in aircraft engine control problems. Because of the complexity of the algorithm and the real-time performance of the predictive model, it has thus far been infeasible to implement model predictive control in the realtime control system of aircraft engine. In most nonlinear model predictive control, nonlinear interior point methods (IPM) are used to calculate the optimal solution, which iterate to the optimal solution based on the Jacobian and Hessian matrix. Most nonlinear IPM solver, such as MATLAB fmincon and IPOPT, cannot calculate the Jacobian and Hessian matrix precisely and quickly, instead of using numerical differentiation to calculate the Jacobian matrix and BFGS method to approach to the Hessian matrix. From what has been discussed above, we will 1) improve the real-time performance of predictive model by replacing the time-consuming component level model (CLM) with a neural network model, which is trained based on the data of component level model, 2) precisely calculate the Jacobian and Hessian matrix using automatic differentiation, and propose a group of algorithms to make NMPC strategy quicker, which include making use of the structure of predictive model, and the integrity of weighted sums of Hessian matrix in IPM. Finally, considering input and output constraints, the fast NMPC strategy is compared with normal NMPC. Simulation results with mean time of 19.3% – 27.9% of normal NMPC on different platforms, verify that the fast NMPC proposed can improve the real-time performance during the process of acceleration, deceleration for aircraft engine.

scholarly journals A Cooperative Intermodal Transportation Network Flow Control Method Based on Model Predictive Control

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Yuankai Huang ◽  
Qicai Zhou ◽  
Xiaolei Xiong ◽  
Jiong Zhao
Keyword(s):  
Model Predictive Control ◽  
Flow Control ◽  
Real Time ◽  
Predictive Control ◽  
Dynamic Optimization ◽  
Network Flow ◽  
Control Method ◽  
Discrete Event ◽  
Transport Systems ◽  
Intermodal Transport

With the development of information technology, intermodal transport research pays more attention to dynamic optimization and multi-role cooperation. The core issue of this paper was to realize container routing with dynamic adjustment, real-time optimization, and multi-role cooperation characteristics in the intermodal transport network. This paper first introduces the Intermodal Transport Cooperation Protocol (ITCP) that describes the operation and analysis of intermodal transport systems with the concept of encapsulation and layering. Then, a new network flow control method was built based on Model Predictive Control (MPC) in the ITCP framework. The method takes real-time information from all ITCP layers as input and generates flow control decisions for containers. To evaluate the method’s effectiveness, a discrete event simulation experiment is applied. The results show that the proposed method outperforms the all-or-nothing method in scenarios with high freight volume, which means the method proposed in this paper can effectively balance the network transport load and reduce network operating costs. The research of this paper may throw some new light on intermodal transport research from the perspectives of digitization, multi-role cooperation, dynamic optimization, and system standardization.

scholarly journals Integrated Optimal Dispatch of a Rural Micro-Energy-Grid with Multi-Energy Stream Based on Model Predictive Control

Energies ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 3439 ◽  
Author(s):  
Xin Zhang ◽  
Jianhua Yang ◽  
Weizhou Wang ◽  
Man Zhang ◽  
Tianjun Jing
Keyword(s):  
Model Predictive Control ◽  
Real Time ◽  
Predictive Control ◽  
Simulation Analysis ◽  
Operating Cost ◽  
Energy Output ◽  
Output Curve ◽  
The Real ◽  
Optimal Dispatch ◽  
Energy Grid

Due to the randomness of the intermittent distributed energy output and load demand of a micro-energy-grid, micro-sources cannot fully follow the day-ahead micro-energy-grid optimal dispatching plan. Therefore, a micro-energy-grid is difficult to operate steadily and is challenging to include in the response dispatch of a distribution network. In view of the above problems, this paper proposes an integrated optimal dispatch method for a micro-energy-grid based on model predictive control. In the day-ahead optimal dispatch, an optimal dispatch model of a micro-energy-grid is built taking the daily minimum operating cost as the objective function, and the optimal output curve of each micro-source of the next day per hour is obtained. In the real-time dispatch, rolling optimization of the day-ahead optimal dispatching plan is implemented based on model predictive control theory. The real-time state of the system is sampled, and feedback correction of the system is implemented. The influence of uncertain factors in the system is eliminated to ensure steady operation of the system. Finally, the validity and feasibility of the integrated optimal dispatching method are verified by a case simulation analysis.

scholarly journals Model Predictive Control Method with Constant Switching Frequency to Reduce Common-Mode Voltage for PMSM Drives

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Hao Li ◽  
Shuo Chen ◽  
Xiang Wu ◽  
Guojun Tan
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Control Method ◽  
Control Period ◽  
Duty Ratio ◽  
Switching Frequency ◽  
Common Mode ◽  
Common Mode Voltage ◽  
Control Frequency ◽  
Finite Control

A model predictive control method to reduce the common-mode voltage (MPC-RCMV) with constant switching frequency for PMSM drives is proposed in this paper. Four nonzero VVs are adopted in future control period and the switching sequence is designed to ensure the switching frequency is fixed and equal to the control frequency. By substituting the finite-control nonzero voltage vectors in the current predictive model, a current predictive error space vector diagram is obtained to determine the adopted four VVs. The duty ratio calculating method for the selected four VVs is studied. Compared with the conventional MPC-RCMV method, the current and torque ripples are greatly reduced and the switching frequency is fixed. The simulation and experiment results validate the effectiveness of the proposed method.

scholarly journals Model predictive control of steering torque in shared driving of autonomous vehicles

2020 ◽  
Vol 103 (3) ◽  
pp. 003685042095013
Author(s):  
Chunjiang Bao ◽  
Jiwei Feng ◽  
Jian Wu ◽  
Shifu Liu ◽  
Guangfei Xu ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Tracking Control ◽  
Control Method ◽  
Path Tracking ◽  
Hardware In The Loop ◽  
Control Loop ◽  
Vehicle Model ◽  
Automatic Driving ◽  
Steering Torque

The current path tracking control method is usually based on the steering wheel angle loop, which often makes the driver lose control of the automatic driving control loop. In order to involve the driver in the automatic driving control loop, and to solve the vehicle path tracking control problem with system robustness and model uncertainty, this paper puts forward a steering torque control method based on model predictive control algorithm. Based on the vehicle model, this method introduces the steering system model and the steering resistance torque model, and calculates the optimal control torque of the vehicle through the real-time vehicle status, so as to make up for the model mismatch, interference and other uncertainties, and ensure the real-time participation of the driver in the automatic driving control loop. To combine the nonlinear vehicle dynamics model with the steering column model, and to take the vehicle state parameters as the feedback variables of the model predictive controller model, then input the solution of the steering superposition control rate into the vehicle model, the design of the steering controller is realized. Finally, to carry out the simulation of lane keeping based on CarSim software and Simulink control model, and the hardware in-the-loop test on the hardware in-the-loop experimental platform of CarSim/LabVIEW-RT. The simulation and test results indicate that the designed torque loop path tracking control method based on model predictive control can help the driver track the target path better.

Path tracking based on model predictive control with variable predictive horizon

2021 ◽  
pp. 014233122110038
Author(s):  
Huiran Wang ◽  
Qidong Wang ◽  
Wuwei Chen ◽  
Linfeng Zhao ◽  
Dongkui Tan
Keyword(s):  
Model Predictive Control ◽  
Real Time ◽  
Predictive Control ◽  
Control Algorithm ◽  
Path Tracking ◽  
Response Analysis ◽  
Tracking Accuracy ◽  
Tracking Method ◽  
Comprehensive Performance ◽  
Path Tracking Method

Model predictive control is one of the main methods used in path tracking for autonomous vehicles. To improve the path tracking performance of the vehicle, a path tracking method based on model predictive control with variable predictive horizon is proposed in this paper. Based on the designed model predictive controller for path tracking, the response analysis of path tracking control system under the different predictive horizons is carried out to clarify the influence of predictive horizon on path tracking accuracy, driving comfort and real-time of the control algorithm. Then, taking the lateral offset, the steering frequency and the real-time of the control algorithm as comprehensive performance indexes, the particle swarm optimization algorithm is designed to realize the adaptive optimization for the predictive horizon. The effectiveness of the proposed method is evaluated via numerical simulation based on Simulink/CarSim and hardware-in-the-loop experiment on an autonomous driving simulator. The obtained results show that the optimized predictive horizon can adapt to the different driving environment, and the proposed path tracking method has good comprehensive performance in terms of path tracking accuracy of the vehicle, driving comfort and real-time.

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