Research on Intelligent Platoon Formation Control Based on Kalman Filtering and Model Predictive Control
Abstract
:1. Introduction
- (1)
- Sliding mode control: The main principle is that the system continuously changes according to the current operating state, causing the system to move according to the predetermined trajectory and reach the sliding surface in a limited time, reducing the platoon driving difference and controlling it within the specified range [5]. Negash N. M. et al. [6] explored the problem of optimal velocity tracking of the vehicle platoon, modeled the automatic driving system by using the human-like prediction vehicle following model, and proposed a sliding mode control method based on adaptive radial basis function neural network. Through simulation analysis, the platoon stability of the vehicle platoon system was ensured. Gao Z. et al. [7] investigated the problem of vehicle platoon following control with actuator saturation, dynamic uncertainty and unknown disturbance. Based on the saturation function and fixed-time performance function of the Gaussian difference function, an adaptive fixed-time sliding mode control scheme was proposed to ensure that the vehicle platoon following difference converged to a steady state within a fixed time. Boo J. et al. [8] proposed a robust bidirectional longitudinal platoon control method based on integral sliding mode control for vehicles with unknown acceleration and unknown matching disturbance. The finite time robust estimator was used to estimate the acceleration and unknown matching disturbance of adjacent vehicles, and the disturbance compensation was carried out, effectively improving the performance of platoon following control. This method can overcome the uncertainty of the system and has strong robustness to external disturbances. However, when the trajectory of the system reaches the sliding mode surface, it is difficult to strictly follow the equilibrium point of the sliding mode surface, and the system is prone to jitter.
- (2)
- Model predictive control: This model establishes a prediction model based on the current state of the control object, predicts the future output value of the control object, and then corrects it based on feedback to achieve the optimal control effect [9]. Luo Q. et al. [10], aiming at the control problem of heterogeneous platoon affected by perturbations and modeling differences, proposed an improved model prediction controller, which used a particle swarm optimization algorithm with an H∞ performance index of augmenting difference system to optimize the gain in the integrated control strategy, effectively reducing the platoon following difference. Hu X. et al. [11] proposed a distributed MPC scheme to solve the problems of model uncertainty, external interference and time delay existing in platoon control, which carried out unbiased estimation of system state and disturbance, eliminated vehicle following difference, and ensured the stability of platoon following. Ju Z. et al. [12] proposed a distributed stochastic MPC algorithm for vehicle platoon, aiming at the performance of vehicle platoon following control. Under the condition that vehicle driving is full of uncertainties, the algorithm transformed the stochastic MPC problem into a deterministic problem through constraint tightening. Through simulation analysis, the algorithm ensured the stability of vehicle platoon difference convergence. This type of method can explicitly handle the constraints of the controlled system and has the advantage of performance optimization, but its complex computational complexity can affect the timeliness of the system.
- (3)
- Neural network control: This involves statistical analysis of platoon trajectory data, mining relevant driving behavior patterns, establishing corresponding fitting relationships, and achieving effective control of vehicle formation [13]. Huang J. et al. [14] proposed a vehicle platoon following control strategy based on adaptive neural network algorithm. This method uses adaptive neural network to estimate the nonlinear terms in the vehicle model and compensates the control input with the estimated value, effectively improving the vehicle platoon following performance. Wu Z. et al. [15] aiming at the problem that it is difficult to control nonlinear vehicle platoon following due to measurement differences and environmental interference, applied radial basis function neural network to approximate the nonlinear function, and designed an adaptive distributed controller by using reverse step control method to ensure the consistency of vehicle formation following. An J. et al. [16] solved the distributed adaptive singular free fixed time neural network tracking control problem for vehicle platoon with model uncertainty. They used adaptive neural networks and H∞ control theory to deal with unknown nonlinearity and mismatched complete disturbances in third-order vehicle dynamics. The effectiveness of the proposed algorithm for platoon following control was verified through simulation experiments. Such methods can learn from data sets, extract model features, solve nonlinear problems, and have a certain generalization ability, but their data demand is large, training equipment and time costs are high, and the application threshold is high.
2. System Model
2.1. Problem Description
2.2. Intelligent Vehicle Platoon Monorail Model
2.3. Control Framework
3. Controller Design
3.1. Prediction Model
3.2. Objective Function
- (1)
- One of the main objectives of the longitudinal driving control of the intelligent vehicle platoon is to maintain the desired longitudinal workshop distance and ensure that the following difference is as small as possible. This control objective is achieved by solving the objective function , as shown in Equation (29):
- (2)
- In order to ensure the driving effect of the vehicle following, it is necessary to control the velocity of the following vehicle so that the velocity of the following vehicle is as consistent as possible with the velocity of the leading vehicle. This control goal is achieved by solving the objective function , as shown in Equation (31):
- (3)
- The main objective of lateral driving control of the intelligent vehicle platoon is to minimize the difference between current lateral driving position of the following vehicle and the lateral position of the reference trajectory, and to ensure that the yaw angle difference is as small as possible, This control goal is achieved by solving the objective function , as shown in Equation (32):
3.3. Optimization Solution
4. Simulation Analysis
4.1. Simulation Parameter Setting
4.2. Controller Performance Analysis
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Nomenclature
Name | Abbreviation |
Model Predictive Control | MPC |
Kalman Filtering | KF |
Sliding Mode Control | SMC |
Neural Network Control | NNC |
3-Degree-of-Freedom Model | 3-DOF |
Name | Implication |
mass | |
Moment of inertia of yaw motion | |
Yaw angular velocity | |
angle | |
Lateral velocity at the center of mass | |
Longitudinal velocity at the center of mass | |
, | Longitudinal acceleration and velocity |
, | Lateral acceleration and velocity |
, | Front and rear lateral forces |
, | Front and rear longitudinal forces |
, | The distance from the center of mass to the front axle and the distance from the center of mass to the rear axle |
, | The side Angle of the front wheel and the side Angle of the rear wheel |
, | Longitudinal stiffness of front and rear tires |
, | Lateral stiffness of front and rear tires |
, | Slip rate of front and rear tires |
ζ | System state variable |
u | System input variable |
, | Control variable acceleration and steering Angle |
Output variable | |
Constant matrix of vehicle parameter correlation | |
Nonlinear input to the equation of state | |
Output variable constant matrix | |
Input parameter | |
Observation matrix | |
, | Process noise and measurement noise |
The initial status of the system | |
Initial covariance matrix | |
Error between the observed and predicted values of the system | |
Predicted value of system covariance | |
Kalman gain | |
, | Prediction domain and control domain |
Expected workshop spacing | |
Safe distance | |
Head time |
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Parameter Name | Value | Unit |
---|---|---|
Vehicle mass | 1270 | kg |
Distance from center of mass to front axis | 1015 | mm |
Front and rear axle distance | 2910 | mm |
Body height | 1610 | mm |
Height of center of mass | 540 | mm |
Tyre radius | 325 | mm |
Moment of inertia about the z axis | 1536.7 | kg/m2 |
Parameter Name | Range | Unit |
---|---|---|
m/s | ||
m/s2 | ||
° | ||
m/s2 | ||
° | ||
6 | m | |
1 | - |
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Sun, N.; Liu, J.; Wang, P.; Xiao, G. Research on Intelligent Platoon Formation Control Based on Kalman Filtering and Model Predictive Control. World Electr. Veh. J. 2024, 15, 144. https://doi.org/10.3390/wevj15040144
Sun N, Liu J, Wang P, Xiao G. Research on Intelligent Platoon Formation Control Based on Kalman Filtering and Model Predictive Control. World Electric Vehicle Journal. 2024; 15(4):144. https://doi.org/10.3390/wevj15040144
Chicago/Turabian StyleSun, Ning, Jinqiang Liu, Peng Wang, and Guangbing Xiao. 2024. "Research on Intelligent Platoon Formation Control Based on Kalman Filtering and Model Predictive Control" World Electric Vehicle Journal 15, no. 4: 144. https://doi.org/10.3390/wevj15040144