Control Strategy For Vehicle Research Articles

Research on the Performance of Intelligent Vehicle Perception System Based on Environmental Controllable Testing Technology

As the decision-making data collection module of intelligent vehicle, the perception system has a significant impact on the vehicle planning and control strategy. This paper analyzes the significant factors (rainfall, visibility and light intensity) of external natural conditions on the vehicle perception system, and forms the evaluation rules for the controllable technology of simulation environment and the controllable technology of real vehicle field test environment. In combination with relevant national standards and technical capacity reserves of the supplier, the rainfall simulation system covering light rain, moderate rain, heavy rain, heavy rain and heavy rain will be built; Build a fog fall simulation system covering a minimum visibility of 15-2000 meters; And build a four-gear adjustable natural lighting simulation system, so that the test site has the ability of all-weather, uninterrupted and controllable environmental testing. Based on the key functional testing research system of typical working conditions, the test case development and test of the intelligent vehicle perception system are carried out through the environment-controlled test technology. The test statistics show that the environment controllable test technology can effectively cover typical test scenarios, and can promote the realization of the highly automatic driving phase of intelligent vehicles.

A distributed robust control strategy for electric vehicles to enhance resilience in urban energy systems

Resilient operation of multi-energy microgrid is a critical concept for decarbonization in modern power system. Its goal is to mitigate the low probability and high damaging impacts of electricity interruptions. Electrical vehicles, as a key flexibility provider, can react to unserved demand and autonomously schedule their operation in order to provide resilience. This paper presents a distributed control strategy for a population of electrical vehicles to enhance resilience of an urban energy system experiencing extreme contingency. Specifically, an iterative algorithm is developed to coordinate the charging/discharging schedules of heterogeneous electrical vehicles aiming at reducing the essential load shedding while considering the local constraints and multi-energy microgrid interconnection capacities. Additionally, the gap between electrical vehicle energy and the required energy level at the departure time is also minimised. The effectiveness of the introduced distributed coordinated approach on energy arbitrage and congestion management is tested and demonstrated by a series of case studies.

An Evolutionary Learning Framework of Lane-Changing Control for Autonomous Vehicles at Freeway Off-Ramps

This paper proposes a lateral control strategy for autonomous vehicles (AVs) and develops an evolutionary learning framework for off-ramps. Random forest (RF) and back-propagation neural network (BPNN) integrated with model predictive control (MPC) algorithm are respectively used to capture the decision-making and trajectory characteristics during the lane-changing maneuver based on the Next Generation Simulation (NGSIM) dataset. Then, a running cost function is calculated to optimize the trajectory dataset. Finally, the numerical simulation is conducted to investigate the characteristics of the proposed framework. Simulation results indicate that the performance of our method is much better than some other methods in lane-changing gap choice and trajectory execution. Moreover, the traffic system controlled by the evolutionary algorithms reaches the highest capacity and safest level when all vehicles are equipped with cooperative adaptive cruise control (CACC) systems. On the contrary, the scenario with 50% CACC vehicles shows the lowest travel efficiency and the worst safety because of the CACC vehicles’ degradation. Furthermore, three iterations and 500 vehicle trajectories at each optimization cycle are recommended for the application in the off-ramp traffic control.

Obstacle Avoidance Control of Unmanned Aerial Vehicle with Motor Loss-of-Effectiveness Fault Based on Improved Artificial Potential Field

This paper presents an obstacle avoidance control strategy for an underactuated quadrotor unmanned aerial vehicle with motor loss-of-effectiveness fault and disturbance. The control system is divided into two parts: the obstacle avoidance loop and the tracking loop. By introducing the height factor in the artificial potential field function, an improved obstacle avoidance strategy is designed in the obstacle avoidance loop. Compared with the existing literature, the proposed obstacle avoidance strategy can avoid falling into the trap of the local optimum when a UAV encounters obstacles. At the same time, considering the sudden motor loss-of-effectiveness fault of UAV, adaptive technology is used to estimate the fault parameters online to restrain the effects of motor loss-of-effectiveness fault in the tracking loop. The stability of the closed-loop UAV system is guaranteed by stabilizing each of the subsystems through backstepping technology. Simulations are conducted to demonstrate the effectiveness of the designed obstacle avoidance control strategy.

3D Path Following Control of an Autonomous Underwater Robotic Vehicle Using Backstepping Approach Based Robust State Feedback Optimal Control Law

This work renders the design of a robust state feedback optimal control strategy for an Autonomous Underwater Robotic Vehicle (AURV). The control strategy is developed using a polytopic approach based on hydrodynamic parameter variation. Besides, a backstepping approach is designed to control the kinematics of the system. However, the dynamics of the AURV system are controlled by a robust optimal control technique. In this work, the decoupled systems for both horizontal and vertical dynamics of AURV are used for the development of the control algorithms. Furthermore, the 3-D path following is achieved by integrating the control algorithms of both horizontal and vertical dynamics of AURV. The proposed controller is formulated using semi-definite programming (SDP). To track the 3-D path, it is intended to track both the desired depth and desired yaw in diving and steering planes. The simulation studies are conducted through MATLAB/Simulink environment using the YALMIP tool. Furthermore, the robust behavior of the proposed control algorithm is verified by considering the uncertain hydrodynamic parameters.

Explicitly incorporating surrogate safety measures into connected and automated vehicle longitudinal control objectives for enhancing platoon safety

The concepts of Connected and Automated Vehicles (CAV) and vehicle platooning have generated high expectations regarding the safety performance of future transportation systems. Existing CAV longitudinal control research primarily focuses on efficiency and control stability, by considering different inter-vehicle spacing policies. In very few cases, safety was also considered as a constraint, but not in the main control objectives. Theoretically, stability can only guarantee that CAV platoons eventually achieve an equilibrium state but is unable to promise safety along the process of achieving equilibrium. It is important to note that CAV does not mean absolutely safe, and its longitudinal or platoon control safety performance depends on how the control algorithms are designed, how accurately it can detect and predict its lead vehicle’s (could be a human-driven vehicle) next move, and other practical factors such as control and communication delays. To optimize CAV platoon safety, this study integrates surrogate safety measures (SSM) and model predictive control (MPC) into CAV longitudinal control for trajectory optimization. SSM has been widely adopted for modeling the safety consequences of various vehicle control strategies and identifying near-crash events from either simulated or field-captured traffic data. This study directly incorporates three typical SSM into the longitudinal control objectives of CAV and constructs a state-space MPC algorithm to model how these SSM vary as a result of CAV dynamics. Numerical examples are provided to show the performance of these SSM-based optimal CAV longitudinal control methods under traffic flow perturbations. To further confirm the necessity of explicitly considering SSM in CAV longitudinal control and its effectiveness in reducing rear-end collision risk, the proposed methods are compared with three classical longitudinal control models that do not consider SSM based on microscopic traffic simulation. It is noted that all SSM-based optimal control methods perform better than others as manifested by some key risk indicators, demonstrating the importance of explicitly considering SSM and safety in CAV longitudinal control.

Improved PBFT Algorithm Based on Comprehensive Evaluation Model

Blockchain technology is well known due to the advent of Bitcoin. With the development of recent years, blockchain technology has been widely used in medicine, digital currency, energy, etc. The practical Byzantine fault-tolerant (PBFT) algorithm is a consensus algorithm widely used in consortium blockchains. Aiming to address the problems of the PBFT algorithm, low consensus efficiency due to high communication complexity, and malicious behavior of the primary node leading to consensus failure, an improved PBFT algorithm based on a comprehensive evaluation model (TB-PBFT) is proposed. First, nodes are divided into several groups based on the multi-formation control strategy of an unmanned aerial vehicle (UAV) cluster, which significantly reduces the communication complexity. Second, a comprehensive evaluation model combining the entropy method, TOPSIS method, and Borda count is proposed, which uses the behavior of nodes as an evaluation index, and the comprehensive score of nodes is obtained according to the preferences of other nodes. Finally, the highest ranking node is selected as the primary node through the comprehensive evaluation model to ensure the security and stability of the blockchain network. We analyze TB-PBFT algorithms and compare them with other Byzantine fault tolerance algorithms. Theoretical analysis and simulation results show that the TB-PBFT algorithm can improve node scalability and fault tolerance and reduce communication complexity and view switching probability. We also prove that the comprehensive evaluation model can improve the consensus success rate of the algorithm, and the feasibility and effectiveness of the improved consensus algorithm are verified. Hence, it can be applied to the consortium blockchain system effectively and efficiently.

Research on characteristic parameter selection and attention-GRU-based model for braking intention identification

The braking intention is of great significance to the realization of driver assistant features, the improvement of braking safety, and the maximization of energy recovery efficiency for electric vehicles. With the aim of accurate identification of braking intention, an identification model based on Gated Recurrent Unit (GRU) Network with Attention mechanism is proposed in this paper. Based on numerous vehicle braking test data, braking process analysis, characteristic parameters selection, identification model training, and verification are carried out. Through the difference analysis based on the Kruskal-Wallis test and the importance evaluation based on random forest, combined with the real-time requirements of practical application, the appropriate characteristic parameters are selected as the model input. The attention mechanism is introduced into the proposed model, which can improve identification accuracy by capturing valuable feature information. The comparative verification results show that the Attention-GRU model performs better than the other three comparison models, and its identification accuracy is 96.7%, of which the accuracy of slight braking, normal braking, and emergency braking are 96.3%, 95.8%, and 100% respectively. The identified braking intention can provide an effective basis for the establishment of vehicle control strategies.

An occlusion- and interaction-aware safe control strategy for autonomous vehicles

We consider the problem of safe autonomous driving in the presence of occlusions. Dealing with latent risks arising from occlusions is challenging because there does not exist direct mapping from sensor input to visible threats; attempts to ensure safety for all worst-case latent threats can be infeasible or overly conservative, and accounting for a multitude of latent risks for sufficient future horizon may require prohibitive computation in real-time. To address these issues, in this paper, we propose to use a probability-based predictive controller to make safe decisions for autonomous vehicles. We prove that the proposed safety controller can generate vehicle control profiles that yield the desired safety probability. Numerical and onboard experiments on a visual occluded pedestrian crossing scenario verifies the efficacy of the proposed method in real-time. The merits of the proposed control strategy include being able to guarantee long-term safety under occlusions without being over-conservative, handling latent risks caused by on-road interactions in real-time, and ease of design with transparency to the exposed risks.

Retracted: Research on Compound Braking Control Strategy of Extended-Range Electric Vehicle Based on Driving Intention Recognition.

[This retracts the article DOI: 10.1155/2022/8382873.].

Motion Sickness Mitigating Algorithms and Control Strategy for Autonomous Vehicles

Autonomous Vehicles (AVs) have the potential to generate socioeconomic benefits by reducing commute times, accidents and energy consumption. Ergonomists have foreseen the future of these vehicles as comfortable pods where the passengers will be watching movies, reading or simply relaxing. Motion Sickness (MS) is thought to be a human comfort issue which needs to be addressed to enable such a vision. The present study endeavours to address this issue by firstly proposing three algorithms for mitigating MS during various manoeuvres, namely: (1) Single Lane Change (SLC), (2) Double Lane Change (DLC) and (3) Cornering. This is done in 3 phases; phase 1 is the identification of coordinates using Particle Swarm Optimization (PSO), phase 2 is the MS-free block that minimizes MS, and the last phase is shaping and symmetry block for minimizing postural instability and maximizing handling comfort. This is believed to be the first time an ultimate motion sickness mitigating algorithm is introduced. The second proposed solution is a control strategy to ensure real-time adaptive MS reduction while the AV is on the move with the help of modern & traditional controllers. Ultimately, The proposed hybrid solution is compared against other recent studies, with results illustrating a substantial MS reduction in AV’s of about 75%.

Integrated cooling/HVAC system design and control strategy for reconfigurable light electric vehicle

In the recent panorama, the use of conventional passenger vehicles has been discouraged from European level up to local municipalities. “Reconfigurable light electric vehicle” REFLECTIVE project targets at developing a light, fully electric vehicle capable of performing safely, efficiently, and multifacetedly in urban environments. By this way, it would be possible to fill the gap with the final user perception and electric vehicles market penetration. Moreover, in order to improve occupants’ comfort, HVAC system has been considered as well, a feature which is not usually implemented in such a kind of vehicles. This paper will show results obtained for an integrated solution for cooling/HVAC circuits in terms of energy efficiency and occupants’ comfort, with particular focus on proper battery operation. In fact, the battery thermal management is integrated into the HVAC circuit. The integrated circuit will take care of components’ cooling so to guarantee proper operating conditions, HVAC for passenger comfort and battery thermal management so to assure proper operating conditions at the battery itself throughout different scenarios. Finally, the effectiveness of the control logic specifically developed for such an integrated circuit (HVAC/Cooling/Battery) will be shown in a urban driving cycle during winter season, so to maximize the heating loads both at powertrain and battery, with always assuring comfort conditions into the cabin.

Simulation Analysis Of Vehicle Four-Wheel Steering Control

In order to study the handling stability of four-wheel steering vehicle, the vehicle is simplified into a linear two-degree-of-freedom model. The fuzzy control technology is used to study the proportional feedforward + fuzzy feedback control strategy of four-wheel steering vehicle, and the fuzzy control toolbox in Matlab/Simulink is used to establish the four-wheel steering control system. The simulation results show that the fuzzy control can effectively improve the response of rear wheel steering, improve the maneuverability of the vehicle at low speed, reduce the turning radius, and improve the handling stability of the vehicle at high speed.

Hierarchical eco‐driving control strategy for hybrid electric vehicle platoon at signalized intersections under partially connected and automated vehicle environment

AbstractIn recent years, eco‐driving for hybrid electric vehicles (HEVs) has been studied with the emerging connected and automated vehicle (CAV) technologies to improve the mobility, fuel economy and safety of HEVs. This paper develops a hierarchical eco‐driving control strategy for HEV platoons consisting of CAVs and human‐driven vehicles (HDVs) to improve fuel economy at signalized intersections. For each platoon, the speed trajectories of CAVs are optimized in the upper layer controller using model predictive control (MPC) to minimize the total fuel consumption of the whole platoon. For each HEV, the optimal power split between the engine and the battery is obtained in the lower layer controller using adaptive equivalent consumption minimization strategy (A‐ECMS). The time‐varying powertrain efficiencies of HEVs are explicitly considered in the speed trajectory optimization. At last, simulation studies are conducted using MATLAB and VISSIM to evaluate the performances of the strategy in mixed traffic scenarios and different CAV penetration rates. Simulation results indicate that compared with the single vehicle control strategy, the proposed strategy can improve the average fuel economy by up to 8.34% and considering the time‐varying powertrain efficiencies of HEVs in the optimization can further reduce the fuel consumption by up to 1.23%.

A layered roll stability control strategy for commercial vehicles based on adaptive model predictive control

Due to the high centre of mass, commercial vehicles are prone to ultimate rollover under extreme steering conditions. The rollover of commercial vehicles leads to serious traffic accidents and poses a threat to road traffic. In this paper, the mechanism of vehicle rollover is revealed by static equilibrium and phase plane analysis. Meanwhile, a robust roll angle observer is designed by combining Kalman filter and sliding mode observation. Considering the influence of wheel lifting on roll dynamics, a novel layered control strategy is proposed by employing engine torque limit, differential braking and active front-wheel steering. Moreover, the layered control of roll stability is realised by adaptive model predictive control with time-varying weights and constraints. To verify the validity and reliability of the proposed algorithm, the hardware-in-loop tests under typical driving conditions have been carried out. The results show that the proposed method can effectively restrain the divergence of roll state and prevent vehicle rollover.

A review of path following control strategies for autonomous robotic vehicles: Theory, simulations, and experiments

AbstractThis article presents an in‐depth review of path following control strategies that are applicable to a wide range class of marine, ground, and aerial autonomous robotic vehicles. From a control system standpoint, path following can be formulated as the problem of stabilizing a path following error system that describes the dynamics of position and possibly orientation errors of a vehicle with respect to a path, with the errors defined in an appropriate reference frame. In spite of the large variety of path following methods described in the literature we show that, in principle, most of them can be categorized in two groups: stabilization of the path following error system expressed either in the vehicle's body frame or in a frame attached to a “reference point” moving along the path, such as a Frenet‐Serret (F‐S) frame or a Parallel Transport (P‐T) frame. With this observation, we provide a unified formulation that is simple but general enough to cover many methods available in the literature. We then discuss the advantages and disadvantages of each method, comparing them from the design and implementation standpoint. We further show experimental results of the path following methods obtained from field trials testing with under‐actuated and over‐actuated autonomous marine vehicles. In addition, we introduce open‐source Matlab and Gazebo/ROS simulation toolboxes that are helpful in testing path following methods before their integration in the combined guidance, navigation, and control systems of autonomous vehicles.

NMPC-Based Path Tracking Control Strategy for Autonomous Vehicles With Stable Limit Handling

A novel nonlinear model predictive control (NMPC) strategy for path tracking of autonomous vehicles with stable limit handling is proposed in this article. To improve the path tracking performance under complex driving maneuvers, a tire lateral force model, which treats road adhesion coefficient and vertical load as variable parameters, is discussed first. It has the advantage that the dynamics model can be applied under different driving conditions. In addition, the stability properties of the dynamics system with the front tire lateral force as the virtual control input are analyzed. The global asymptotical stability with zero input and local input-to-state stability with nonzero input of the dynamics system are proved respectively. To perform path tracking within the handling limits, both constraints of front tire lateral force and rear tire slip angle are included in the NMPC controller. The constraint of front tire lateral force is considered as peak limits related to load transfer and adhesion coefficient. And for the rear tire slip angle, a combining constraint scheme is proposed, where the indirect constraint is implemented by a penalty term that predicts the saturation of rear tire lateral force, while the direct constraint is implemented by a dead-zone exponential penalty function. The Continuation/Generalized Minimum Residual (C/GMRES) algorithm is applied to improve the computation efficiency of the NMPC. The performance of the controller is evaluated in simulations and hardware-in-the-loop (HIL) tests, and the results show that the controller is able to track the reference path with high precision in real-time.

A Cooperative Charging Control Strategy for Electric Vehicles Based on Multiagent Deep Reinforcement Learning

The growth of electric vehicles (EVs) significantly increases the residential electricity demand and potentially leads to the overload of the transformer in the distribution grid. Aiming to coordinate the charging control of EVs, this article formulates the EVs charging problem as a Markov game with an unknown transition function and proposes a cooperative charging control strategy based on the multiagent deep reinforcement learning. The uncertainties from the dynamic electricity price, non-EV residential load consumption and drivers’ individual behaviors are considered to construct the dynamic charging environment. Each agent contains a collective-policy model and an independent learner. The collective-policy model is introduced to model other agent's behaviors by approximating their power consumption. The independent learner is used to learn the optimal charging strategy by interacting with the environment. The soft-actor-critic framework is adopted to train the independent learner, enabling the proposed method to address the continuous state and action. Agents are trained with only the local observation and approximation, indicating that the proposed approach is fully decentralized and scalable to the problem with multiple agents. Finally, several numerical studies constructed based on the real-world data demonstrate the effectiveness and scalability of the proposed approach.

Position Tracking Control of Fully-Actuated Underwater Vehicles With Constrained Attitude and Velocities

This article presents a redundant control strategy of fully-actuated underwater vehicles. The proposed strategy offers vehicles more control flexibility for addressing the position tracking problem under the attitudes and velocities constraints. In kinematics, position tracking is formulated as a convex optimization problem, where the velocity constraints are considered as a feasible region and the attitudes are bounded via a barrier function. Then, a conservative boundary is determined in the context of stability analysis of the dynamic controller with a disturbance observer and an auxiliary system so that kinematic variables lie in the constraints even with the velocity tracking error. Also, it is proven that the position tracking error can converge to a neighborhood about zero, which can be made arbitrarily small. The simulations and experimental results through multiple scenarios have illustrated the efficiency of the proposed strategy.

Hierarchical control strategy of electric vehicles with demand response in retail electricity markets

Hierarchical control strategy of electric vehicles with demand response in retail electricity markets