The development of vehicles-to-everything (V2X) communication and autonomous driving technologies offers novel opportunities for eco-driving of connected hybrid electric vehicles (HEVs). To enhance vehicle energy efficiency in complex traffic scenarios, this study proposes a novel interaction-aware eco-driving strategy utilizing a Safe Soft Actor-Critic (Safe-SAC) deep reinforcement learning (DRL) algorithm for connected HEVs, which jointly optimizes speed planning and lane-changing decisions. By leveraging V2X technology, multi-source traffic information is integrated into the DRL environment, where the state space encompasses the states of the ego vehicle, traffic flow, signal phase and timing (SPaT), and the states of surrounding vehicles, while the action space contains the target lane and desired acceleration of the ego vehicle. In contrast to previous works, the integration of traffic flow speed and lane occupancy for each lane as the state variables enables a more accurate representation of the dynamic characteristics of surrounding traffic. Furthermore, safety constraints and a multi-objective reward function are meticulously designed to balance energy efficiency, driving comfort, and travel efficiency while ensuring safety. To thoroughly evaluate the energy efficiency of the proposed eco-driving strategy, both dynamic programming (DP) and equivalent consumption minimization strategy (ECMS) are employed as the underlying energy management strategies (EMS). Finally, the effectiveness of the Safe-SAC strategy is successfully validated on the co-simulation platform based on the Simulation of Urban Mobility (SUMO) and Python under various traffic scenarios. Compared to the Krauss-LC2013 model, the findings highlight the superiority of the proposed Safe-SAC strategy in achieving an average energy saving of 38.5%. This strategy also enhances driving comfort and maintains higher travel efficiency.

To address the vehicle lateral dynamics control in practical application with nonlinearity, uncertainty, detection faults and disturbances, this paper describes three key elements in the controller design that address the safety and comfort performance challenges, i.e., high precision modeling, redundant detection and robust control. Firstly, the vehicle dynamics model, tire model and preview driver model are augmented into a coupled lateral dynamics model, which is linearized and then transformed into a linear parameter varying (LPV) model with the varying motion states and tire cornering stiffness. Secondly, a redundant detection strategy is proposed for the lane-marker-based lateral control system to improve the reliability. According to the different detection states and sequences, an$H_{\infty } $state observer is designed for the vehicle motion state estimation, where a tracking and prediction strategy of the lane markers is considered for the constraint of the dwell time to guarantee the exponential stability. Considering the linearization errors, model uncertainty and disturbances in the LPV model, the$H_{\infty } $observer-based controller is designed to improve the stability and robustness based on the Lyapunov stability theory. Lastly, three similar experiment scenarios are given to demonstrate the effectiveness of the proposed method.

Conventional ACC method has great fluctuation and deviation when solving speed and distance control problems. Thus, this paper develops a reinforcement learning (RL) based robust optimal prescribed performance controller for ACC systems. To this end, we first construct a continuous time ACC system with unknown system dynamics (e.g., target vehicle acceleration, sensor and actuator attacks, etc). To estimate the unknown system dynamics, an unknown system dynamic estimator (USDE) is designed, where the unknown system dynamic can be accurately estimated by using the input-output information, this is helpful for controller design. Then, a RL based optimal control method is developed, where the prescribed performance function (PPF) is applied, the system states can be effectively defined within a certain range. To realize the online solution for optimal control, we design a new adaptive law based on the adaptive dynamic programming (ADP) framework to online learn the critic neural network (NN) weights, because of the strong convergence, the proposed learning algorithm can be effectively applied in practical industrial systems. Finally, the efficacy of the proposed control technique is tested through simulations and experiments.

Delayed measurements can create difficulties for real-time feedback control. To address the advanced driver assistant system (ADAS) in practical application with delayed measurements, false alarms, false dismissals and disturbances, we propose a novel mixed$H_{2}/H_\infty $observer-based controller in this study, which enables object tracking and car-following. Especially, the additive and multiplicative noises of the adaptive cruise control (ACC) system can be attenuated by the proposed mixed$H_{2}/H_\infty $method. We first analyze the adaptive cruise and Radar tracking characteristics. Then, a definition of$H_{2}/H_\infty $guarantee performance is introduced to ensure satisfying target tracking and safety car-following performances. Based on$H_\infty $theory, the design criterion of the proposed mixed$H_{2}/H_\infty $observer-based controller for ACC is established by linear matrix inequality (LMI) technique. Lastly, some experiment scenarios are given to demonstrate the effectiveness of the proposed method.

To address the advanced driver assistant system (ADAS) in practical application with some disturbances, we propose a novel finite frequency H∞observer-based method in this study, which enables effectively object tracking to restrain the disturbance during middle and low frequency ranges. We first analyze the Radar tracking characteristics. Then, an H∞observer is established. Based on the H∞theory and Kalman-Yakubovic-Popov (KYP) lemma, the design criterion of the finite frequency H∞observer is established by a linear matrix inequality (LMI). Lastly, two real world experiments are given to demonstrate the effectiveness of the proposed method.

To address the advanced driver assistant system (ADAS) in practical application with some packet dropouts, false alarms, false dismissals and disturbances, we propose a novel$H_\infty $observer-based method in this study, which enables object tracking and car-following. We first analyze the adaptive cruise and Radar tracking characteristics. Then, the adaptive cruise control (ACC) system with packet dropouts is modeled as a class of discrete-time linear switched system with four subsystems. Combining of switched system and$H_\infty $theory, the design criterion of the proposed$H_{\infty }$observer-based ACC is established. Lastly, four similar experiment scenarios are given to demonstrate the effectiveness of the proposed method.

A novel distributed cooperative formation control method inspired by the aggregate behaviors of fish groups is proposed for plug-in hybrid electric vehicle (PHEV) formation. Firstly, a hierarchical control architecture is established for formation keeping with fuel consumption (FC) optimization simultaneously. The top layer is to generate a leader with the optimal performance based on the nonlinear model predictive control (MPC) technique. A fish swarm optimization (FSO) algorithm is proposed to solve the nonlinear MPC problem by imitating the predation behaviors of the fish swarm. The middle layer is a decentralized intelligent cruise control (ICC) for follower vehicles to track their leader imitated the behaviors of fish swarm, and some design criteria are presented based on the Lyapunov stability theory. The under layer is to achieve a satisfying performance for the hybrid powertrain systems of followers. Finally, the bio-inspired method applied for PHEV formation is verified with a satisfying robustness, fuel economy, car-following and also real-time processing performances. According to the results, the PHEV formation using the proposed method represents a better car-following performance compared with the normal adaptive cruise control (ACC) method and a 21.26% improvement of FC compared with the rule-based energy management strategy (EMS). The computational burden is also reduced by the bio-inspired method.

Autonomous vehicles’ dynamics stability control is one key issue to ensure safety of self-driving. However, vehicle uncertainties and time-varying parameters could weaken the performance of autonomous vehicle stability control. Therefore, this article proposes a novel robust linear matrix inequality (LMI)-based$H$-infinite feedback algorithm for vehicle dynamics stability control, and this algorithm controls vehicle steering system and brake system via direct yaw moment control (DYC) and active front steering control (AFS). The presented controller is robust against vehicle parametric uncertainties, including the vehicle mass and vehicle longitudinal velocity. A linear parameter varying lateral model is constructed utilizing polytopic uncertainty method considering time-varying vehicle longitudinal velocity and mass, where a polytope that contains finite vertices is established to contain all of the possible selections for uncertainty parameters. Then, the$H$-infinite feedback controller integrating DYC and AFS is derived via LMI technique. Finally, experimental results based on hardware-in-the-loop (HIL) platform illustrate that the presented controller has better performance of ensuring autonomous vehicle dynamics stability than other controllers.

Gear engagement is the most important part in gear-shift process of automated manual transmission (AMT). However, it is practical to encounter complicated nonlinearities, uncertainties, and multistage characteristics in the system model, so the controller design for the AMT gear engagement becomes challenging. This paper proposes a dual-loop self-learning fuzzy control framework. In the outer loop, the self-learning rules based on fuzzy logic is designed to adjust desired trajectory of actuator motor. In the inner loop, the gear engagement is divided into three stages, and a fuzzy controller with model reference self-learning algorithm is designed, which controls the actuator motor to track the desired trajectory. Besides, the control parameters could be adjusted to be optimal automatically when the parameters change. Results of simulations and experiments indicate that the proposed method is able to realize the smooth and fast control of gear engagement. In addition, the self-learning fuzzy controller can be extended to deal with other nonlinear systems with uncertain and even unknown parameters.