Vehicle platoon control based on vehicle-to-vehicle (V2V) communication is one of the promising technologies to improve the performance of transportation systems. This paper presents a distributed controller to optimize a vehicle platoon’s fuel consumption by combining the switching feedback control and economic model predictive control (EMPC) methods. The closed-loop dynamics involving switching feedback gains with the constant time headway (CTH) policy are established firstly, and the multiple-predecessor following (MPF) communication topology is considered. In order to obtain the economy optimal feedback gain, we design a local optimal control problem for each vehicle, based on which the average dwell time is defined and the distributed EMPC algorithm is designed. Based on linear matrix inequalities (LMIs) and the Lyapunov theorem, the feedback gain selection method and the lower bound for average dwell time that guarantees asymptotic stability are analyzed rigorously. Then a modified algorithm that can ensure string stability is designed. Numerical simulations show a maximum of 6.84% fuel benefit compared with pure tracking-oriented methods.

In order to improve the trajectory tracking accuracy and vehicle lateral stability, the paper proposes a lateral and longitudinal dynamics control framework of autonomous vehicles considering multi-parameter joint estimation. First, the multi-parameter joint estimation based on adaptive unscented Kalman filter (AUKF) is constructed to decouple and estimate the longitudinal position of vehicle's center of gravity (CG), tire-road friction coefficient (TRFC), tire cornering stiffness (TCS), tire vertical force (TVF) and road grade. Then, focusing on the large lateral acceleration condition, a lateral control based on the optimal front-tire lateral force is proposed by constructing the linear quadratic regulator (LQR) and combining the multi-parameter joint estimation. Additionally, a longitudinal control based on the drive and brake force compensation to realize the accurate speed tracking by combining road slop estimation is achieved. The fuzzy system employs the drive and brake force deviation as the input to implement the compensation of the throttle and brake. The proposed estimation and control framework are verified by co-simulation on PreScan and CarSim preliminarily. In order to further verify the effectiveness and practicability of algorithm, experiments are implemented on an autonomous vehicle platform, a hybrid Lincoln MKZ. Simulation and experimental results show that the proposed estimation and control framework possess excellent performance and enhance the tracking accuracy and lateral stability.

The complex slip characteristics between the tracks and the terrain make it difficult to build an accurate model for intelligent tracked vehicles. Both the path planning and lateral control, however, are highly dependent on the accurate tracked vehicle model. To overcome the issue, a slip model based on the instantaneous centers of rotation (ICRs) of tracks and a dual-layer adaptive unscented Kalman filter (DAUKF) are used to estimate the ICR locations in real-time without requiring prior knowledge of terrain parameters. First, the historical trajectory information is used to estimate ICR locations by the upper-layer AUKF estimator preliminarily. The ICR locations estimated in the upper-layer and the current vehicle state are then imported into the model to predict the future trajectory which can be used to estimate the offset as compensation of preliminarily estimated ICR locations by the lower-layer AUKF estimator. The proposed DAUKF is verified by simulations on MATLAB/Simulink. In order to further verify the effectiveness and practicability of the algorithm, a large number of experiments under different terrains and road conditions are implemented on the electric-drive tracked vehicle. The simulation and experimental results illustrate that the proposed DAUKF can estimate the ICR locations efficiently and accurately, which can improve the accuracy of the tracked vehicle model compared with those of extended Kalman filter (EKF), UKF, and AUKF.

The tire-road friction coefficient (TRFC) is the critical parameter that significantly improves the control performance of distributed electric vehicles. Nonetheless, achieving precise TRFC estimation during straight stationary driving maneuvers, characterized by constant longitudinal speed (e.g., where the longitudinal acceleration is nearly zero) on a straight road, poses a particularly formidable challenge. In the paper, we propose a new learning strategy that leverages multi-domain fusion feature extraction in both the time domain and time-frequency domain to estimate the TRFC during straight stationary driving maneuvers. Specifically, the frequency response function of the in-wheel-motor-drive system first is inferred from the longitudinal dynamics model and single wheel dynamics model. Then, the input selection of learning strategy is determined through frequency response characteristics analysis and explainable dynamics mechanism. In addition, a parallel spatial-temporal convolutional neural network (PSTCNN) is built to extract features in both the time domain and in the time-frequency domain, respectively. Finally, the TRFC learning strategy is verified by experimental tests on different road surfaces. Our results demonstrate that the proposed methodology is capable of estimating the TRFC with a lower error than the traditional learning-based method and the classical slip-slope method.

The tire-road friction coefficient (TRFC) is a key parameter for precise motion control of electric-wheel vehicle. In the article, a data-driven method is proposed, focusing on multidomain feature fusion to achieve TRFC estimation under longitudinal maneuvering signal excitation. The longitudinal maneuvers encompass both the stationary maneuvers (e.g., longitudinal acceleration is around 0) and nonstationary maneuvers (e.g., all maneuvers except stationary ones). First, a scheme is introduced for analyzing vehicle states and parameters related to TRFC, which can provide the data category selection for the data-driven method. Moreover, a stochastic variational deep kernel learning framework is devised to efficiently map spatial–temporal features and assess estimation uncertainty. Subsequently, the TRFC estimation method is validated through simulations on various road surfaces. Results demonstrate the superiority of the proposed method over classical techniques, including a data-driven method and a model-driven method. Furthermore, experimental validation confirms the effectiveness of the proposed method.

In order to improve the control accuracy and stability of autonomous vehicles under large delay conditions, the paper proposes a longitudinal and lateral coordinated control method considering fixed sensor delay, actuator lag, and timevarying CAN communication delay. An adaptive all-pass filter time-delay estimator (AAPF-TDE) based on finite impulse response (FIR) and its improved strategies are designed to realize accurate online estimation of CAN communication delay. According to the estimated delay, a longitudinal and lateral coupling time-delay dynamics model with prediction revision and delay augmentation is constructed. A model predictive controller (MPC) combined with Lyapunov asymptotic stability constraints is then designed. CarSim/Simulink co-simulation and vehicle experiment results show that the proposed controller can improve the vehicle stability effectively while ensuring the longitudinal and lateral control accuracy under large delay conditions compared with the controller without considering delay or only considering fixed delay.