Control Strategy For Vehicle Research Articles

Offline and Online Exergy-Based Strategies for Hybrid Electric Vehicles

Abstract Exergy-based control strategies for ground hybrid electric vehicles (HEVs) enable to pursue unconventional optimization goals that are inaccessible when standard energy-based modeling frameworks based on fuel consumption minimization are used. In this work, we formulate and solve offline and online exergy-based optimization strategies for military HEVs aimed at the minimization of genset exergy destruction and thermal emissions to increase vehicle efficiency and minimize the risk of thermal imaging detection, respectively. We refer to the offline version of these strategies as exergy minimization strategies (ExMSs). Adaptive ExMSs (A-ExMSs) are then formulated for online implementation. Moreover, charge increasing (CI) ExMSs and A-ExMSs are developed to charge the battery as much as possible during a driving mission that is followed by a silent watch phase. To assess the performance of the proposed strategies, the results obtained by the ExMSs and A-ExMSs are compared to the benchmark solutions obtained by Dynamic Programming.

Comparison of gap-based and flow-based control strategies using a new controlled stochastic cellular automaton model for traffic flow

ABSTRACT Autonomous vehicles are essential to future transportation systems, potentially reducing traffic congestion. This study examines the impact of different vehicle control strategies on traffic flow through simulations. We propose a novel stochastic cellular automaton model, the controlled stochastic optimal velocity (CSOV) model, which incorporates vehicle control effects. Within the CSOV model, two control strategies are implemented: gap-based control (GC), which adjusts vehicle velocity to balance the gaps between adjacent vehicles, and flow-based control (FC), which aims to maintain a consistent local flow between the front and rear vehicles. Results show that both control strategies improve traffic flow. However, under weaker control, the GC sometimes resulted in lower flow compared to no control. In contrast, the FC consistently enhanced flow across control strengths, yielding more robust outcomes. Furthermore, when both strategies achieved comparable flow rates, the FC provided a more stable velocity distribution under varying traffic densities than the GC.

Path tracking and energy efficiency coordination control strategy for skid-steering unmanned ground vehicle

The skid steering unmanned ground vehicle (SUGV) plays an important role in extremely harsh environments. Improving the autonomous control capability and energy efficiency of SUGV is urgently needed. This article presents a skid steering-based path tracking control strategy. In the upper controller, an improved model-free sliding mode controller (APMS) is used to calculate the yaw moment for tracking control. On the lower controller, the Snow Ablation Optimizer (SAO) is used to distribute the output torque of the drive motors, taking longitudinal force, yaw moment and energy consumption into account. Finally, the designed controller is validated through simulation under different operating conditions. The results show that the proposed coordination controller achieves good control performance, increases energy efficiency and at the same time ensures tracking accuracy.

Coordinated torque vectoring and direct yaw control based on Kalman filter control allocation for a four in-wheel electric vehicle

This paper presents a novel hierarchical control architecture designed for a four in-wheel electric vehicle (EV), integrating longitudinal and lateral control strategies to optimize performance and lateral stability. On one hand, a high-level longitudinal controller is synthesized to track a desired speed profile and generate a total torque. On the other hand, a direct yaw control (DYC) strategy is employed for steerability and stability control to generate a yaw moment. To manage the total torque as well as the yaw moment, a control allocation layer is employed. Subsequently, the formulation of the control allocation problem is presented, incorporating a gain-scheduling Daisy chaining Kalman filter (DCKF) control allocation to deal with actuator dynamics and fault tolerance. Results demonstrate enhanced performance in normal and emergency driving. Quick adaptability in case of emergency is achieved without overshooting or undershooting, with a smooth response.

Stability coordinated control strategy of autonomous unmanned 4WID-4WIS vehicle based on APD-LQR and AFSMC

To improve the four-wheel independent driving and four-wheel independent steering (4WID-4WIS) autonomous unmanned vehicles’ active safety performance at their maximum operating capacity, the control of path following, four-wheel steering (4WS) and lateral stability should be coordinated. This paper introduces a hierarchical coordinated control framework, in which several independent controllers collaborate to enhance the overall performance of the vehicle system. Initially, in the upper controller, an adaptive preview distance linear quadratic regulator (APD-LQR) path following algorithm is proposed, considering the dynamic characteristics of the tire. It aimed at making the following error converge and obtaining the optimal front wheel steering angle. Meanwhile, an adaptive fuzzy sliding mode control (AFSMC) algorithm is implemented for obtaining additional yaw moment. It aimed at coordinating and improving effectively path following performance and lateral stability. Using the phase plane approach, the sliding surface is dynamically modified. Subsequently, in the lower controller, a rear wheel angle controller based on vehicle speed is proposed. It aimed at further enhancing vehicle’s maneuverability and stability by fully utilizing its steering redundancy features. Moreover, a torque distribution controller establishes a comprehensive cost function and then the NSGA-III algorithm is employed to optimize the torque distribution coefficients. Finally, simulation and Hardware-in-Loop (HIL) test results demonstrates that the proposed coordination algorithm can fully utilize the redundancy features of the 4WID-4WIS vehicle, and significantly enhances the vehicle’s path following accuracy and lateral stability, especially under extreme driving conditions.

A learning‐based hierarchical energy management control strategy for hybrid electric vehicles

AbstractIn this work, a novel energy management control framework is developed for hybrid electric vehicles (HEVs) driving in car‐following scenarios. In order to enhance the energy efficiency while maintaining the driving safety, a hierarchical control approach consisting of an upper level speed tracking control scheme and a lower level energy management control strategy is proposed. For the upper level tracking control system, an iterative learning model predictive control (ILMPC) scheme is developed to guarantee the tracking performance and the driving safety simultaneously. Additionally, a model predictive control (MPC) algorithm is adopted at the lower level to optimize the torque distribution in real‐time based on the driving cycles generated by the upper level control system. With the proposed hierarchical control framework, HEVs are able to improve the energy efficiency significantly by taking the advantages of the operational repeatability. The convergence of the proposed control strategy is analyzed rigorously, and its effectiveness is illustrated through numerical simulations.

Electronic brake system-based hierarchical motion control of commercial vehicle for autonomous obstacle avoidance

As a high-level autonomous driving technology, efficient and safe automatic obstacle avoidance on highway is one of the critical and ongoing topics that requires in-depth research for commercial vehicles. Electronic control air pressure brake system, owing to its fast response and active braking ability, is an effective active-chassis technology to function path tracking and stability control in such condition. Yaw motion stability control problem of autonomous driving commercial vehicle based on active braking control with electronic air brake system for automatic obstacle avoidance is investigated. First, a hierarchical yaw motion dynamics control architecture is introduced after vehicle and brake system modeling. The quintic polynomial curve-based trajectory planning method and a Model Predictive Control based trajectory tracking control strategy are formulated. Second, to evade the possible stability losing issue in this circumstance, Cubature Kalman Filter (CKF) is utilized to estimate the vehicle motion states, and a fuzzy PID control-based differential brake stability control strategy is designed for the electronic brake system composed of front-and-rear two sets of dual-channel pressure regulator and ABS solenoid valve. Finally, the proposed hierarchical yaw stability control strategy for a commercial vehicle in typical obstacle avoidance conditions is comparatively verified based on the joint simulation tools. After the yaw stability control, the peak value of the lateral displacement error of trajectory tracking can be reduced by 44.7%. The yaw stability control strategy based on fuzzy PID control can reduce the yaw rate by 46%–57% and the sideslip angle by 60%–73% under different conditions. The results show that the proposed control strategy can effectively improve the yaw stability of the vehicle while avoiding obstacles at high speed.

Robot control architecture for autonomous sound source mapping

Acoustic imaging for the monitoring of industrial infrastructures remains a common feature to address numerous issues, such as diagnosing faults in rotating machinery or detecting gas leaks. Simultaneously, autonomous vehicles have emerged as a strong tool to automate various maintenance and diagnostic tasks, incorporating increasingly diverse sensors, including microphone arrays. The objective of this work is to present a new control strategy for an autonomous vehicle equipped with a microphone array, aimed at mapping its environment completely blindly. The system must be robust to background noise and to a wide range of sound levels from sources, in order to be effective in an industrial setting. The control strategy proposed in this preliminary work meets these expectations and allows for mapping a large number of sources in a noisy environment, with a wide range of sources sound levels. Its versatile approach allows adapting the localization algorithm used between MUSIC and constrained-MUSIC depending on the situation and acoustic environment, leveraging the information the robot will have collected during its measurement. Its performances are evaluated in a simulation study.

Optimal Economic Analysis of Battery Energy Storage System Integrated with Electric Vehicles for Voltage Regulation in Photovoltaics Connected Distribution System

The integration of photovoltaic and electric vehicles in distribution networks is rapidly increasing due to the shortage of fossil fuels and the need for environmental protection. However, the randomness of photovoltaic and the disordered charging loads of electric vehicles cause imbalances in power flow within the distribution system. These imbalances complicate voltage management and cause economic inefficiencies in power dispatching. This study proposes an innovative economic strategy utilizing battery energy storage system and electric vehicles cooperation to achieve voltage regulation in photovoltaic-connected distribution system. Firstly, a novel pelican optimization algorithm-XGBoost is introduced to enhance the accuracy of photovoltaic power prediction. To address the challenge of disordered electric vehicles charging loads, a wide-local area scheduling method is implemented using Monte Carlo simulations. Additionally, a scheme for the allocation of battery energy storage system and a novel slack management method are proposed to optimize both the available capacity and the economic efficiency of battery energy storage system. Finally, we recommend a day-ahead real-time control strategy for battery energy storage system and electric vehicles to regulate voltage. This strategy utilizes a multi-particle swarm algorithm to optimize economic power dispatching between battery energy storage system on the distribution side and electric vehicles on the user side during the day-ahead stage. At the real-time stage, the superior control capabilities of the battery energy storage system address photovoltaic power prediction errors and electric vehicle reservation defaults. This study models an IEEE 33 system that incorporates high-penetration photovoltaics, electric vehicles, and battery storage energy systems. A comparative analysis of four scenarios revealed significant financial benefits. This approach ensures economic cooperation between devices on both the user and distribution system sides for effective voltage management. Additionally, it encourages trading activities of these devices in the power market and establishes a foundation for economic cooperation between devices on both the user and distribution system sides.

Efficient nonlinear model predictive and active disturbance rejection control for trajectory tracking of unmanned vehicles

This article proposes an efficient trajectory tracking control strategy of unmanned vehicles. The method is based on nonlinear model predictive control (NMPC) and active disturbance reject control (ADRC). The designed control algorithm considers three challenges including nonlinear characteristics, multiple constraints, and external disturbance. First, NMPC method is presented for the nonlinear vehicle model with multiple constraints. To relax inequality constraints and reduce the heavy calculation burden, the penalty term with the variable factor is added to the cost function, an improved continuous/generalized minimum residual method is proposed to solve NMPC online optimization problem. Then, an ADRC scheme is designed to estimate the unknown disturbance via extended state observer, and compensate them by feedback control law in real time, the corresponding parameters are obtained by a novel particle swarm optimization algorithm to further improve the control precision. It ensures the stability to a certain extent. Finally, the results indicate the designed algorithm can raise the calculation efficiency and meet the real-time requirements, and obviously increase the tracking accuracy and robustness performance.

Finite-time formation trajectory tracking control for unmanned underwater vehicles with time delays and Markov-switch topology

ABSTRACT The formation trajectory tracking control strategy of unmanned underwater vehicles is proposed for communication time delay and communication topology random switching. The methods ensure that formation systems are finite-time bounded under discrete time. Firstly, based on the consistency theory and the exact feedback linearised dynamics model of unmanned underwater vehicle, a control strategy is proposed for discrete formation system with communication time delay. Secondly, a cooperative control method is designed for communication problems of Markovian switching topology and time delay. Then, definition of finite-time bounded and linear matrix inequality technique are applied to sufficient conditions of system stability for the above problem. Finally, the formation motion of discrete unmanned underwater vehicle system in three-dimensional space is simulated, fully demonstrating the feasibility of the method.

PACC: A platoon-based adaptive cruise control strategy based on leader-following information topology to mitigate traffic oscillations under CAV environment

Traffic oscillations, often induced by repetitive acceleration and deceleration maneuvers in vehicles’ car-following behaviors, can cause many negative impacts on the traffic flow. With the development of connected and automated vehicle (CAV) technologies recently, scholars have made numerous effects on mitigating the propagation of traffic oscillations through a variety of advanced CAV control strategies, especially those related to the CAV platoon control. Different from the previous works, this paper proposed a platoon-based adaptive cruise control (PACC) strategy for CAV platoon to mitigate the traffic oscillations. The strategy is designed based on the novel and unique leader-following (LF) information topology. Built on the classical proportional-derivative (PD) controller that is implemented in the adaptive cruise control (ACC) strategy of autonomous vehicles (AVs), the PACC strategy is exquisitely designed to ensure the string stability of entire CAV platoon and the critically damped condition of each following CAV in the platoon. Credit to the rapid response of following CAV to the vibration of leading CAV’s dynamic status under LF information topology and the thorough consideration of string stability and damping characteristics in the PD controller design, the PACC strategy enables the CAV platoon to mitigate the traffic oscillations more efficiently than the existing cooperative adaptive cruise control (CACC) and ACC strategies. The numerical experiment for the mixed traffic flow on a single-lane ring road indicates that, when the CAV platoon adopts the PACC strategy, the performance of traffic flow in terms of operational efficiency, driving safety, passenger’s comfort, and fuel economy is substantially enhanced, compared with CACC and ACC strategies. In addition, the performance of PACC strategy gradually improves with the increase of market penetration rate (MPR) of CAVs and length of CAV platoon. Overall, the proposed PACC strategy is a promising solution to the mitigation of traffic oscillation under the CAV environment.

Distributed-observer-based adaptive fault tolerant formation maneuver for autonomous surface vehicles under stochastic cyber-attack

This paper investigates the distributed adaptive fault-tolerant formation maneuver control strategy for multiple autonomous surface vehicles (ASVs) systems subject to stochastic cyber-attack and actuator failures. Existing works have provided solutions against continuous attacks on communication channels while ignoring the increasingly prevalent intermittent attacks. Compared to the former, the latter are more challenging to detect, having been insufficiently described and addressed. Motivated by this observation, this article is concerned with the stochastic intermittent injection attacks, which are modeled as stochastic impulsive sequences, characterized by stochastic injection instant and intensity, leading to unpredictable changes in the state information transmitted by the neighbors. Taking this dilemma into consideration, a novel distributed observer is proposed, capable of effectively observing the target formation even in the presence of stochastic intermittent injection attacks. Meanwhile, a large feedback gain is incorporated into the observer to reduce the impact of attacks on system stability. A linear sliding manifold, together with the adaptive algorithm, is utilized to construct a fault tolerant control architecture for each follower ASV without employing model parameters which can confront the embarrassing scenario of actuator failures. Eventually, theoretical deduction and numerical simulations are conducted to confirm the feasibility of the proposed collaborative control scheme.

RSSR Mechanism Design and Motion Control Strategy of a Carbon-Free Vehicle for Obstacle Avoidance Competition

RSSR Mechanism Design and Motion Control Strategy of a Carbon-Free Vehicle for Obstacle Avoidance Competition

Research on anti-rollover control of three-axle rescue vehicle based on active suspension and differential braking

Abstract. Due to its intricate operational environment, substantial mass, and elevated center of mass, the three-axle emergency rescue vehicle is more prone to rollover. This study aims to enhance the operational stability and mitigate rollover risks by establishing a rollover dynamic model with 11 degrees of freedom. Using the coupling characteristics of vehicle dynamics and tire force, an anti-rollover control strategy of the three-axle vehicle based on the cooperative work of differential braking and active suspension is proposed. According to the vehicle motion characteristics, the active suspension controller is built by using a fuzzy PID algorithm, and the differential braking controller is built by using the improved adaptive model predictive control algorithm. The step and fishhook tests of differential braking, active suspension control and joint control are carried out respectively. The simulation results show that the joint control strategy can effectively reduce the rollover tendency of the vehicle, and further improve the anti-rollover ability of the vehicle compared with the single system control.

A Fast-Computing Path Tracking Control Strategy for Autonomous Multiaxle Electric Vehicle Considering Safety and Stability

A Fast-Computing Path Tracking Control Strategy for Autonomous Multiaxle Electric Vehicle Considering Safety and Stability

Technical study of magnetorheological semi-active suspension control strategy for vehicles based on reinforcement learning

The suspension system is an important part of the car, the main function is to alleviate the road surface does not level brought about by the shake, to ensure that the vehicle driving smoothness and comfort, so as to improve the vehicle handling performance and safety, improve the vehicle to reduce the vibration performance more and more become a hot topic in the industry. Among them, semi-active suspension is a popular suspension system. This paper explains the significance and classification of the role of the suspension, and gives a relevant introduction to the magnetorheological fluid, on the basis of which it combines the domestic and international research results, looks forward to the development trend of the semi-active suspension of the vehicle, analyzes the advantages and shortcomings of four important semi-active suspension control strategies, and finally introduces the reinforcement learning and combines the reinforcement learning with the existing control strategies of the semi-active suspension, and designs a reliable reward function to enhance the suspension's damping performance by taking into account the multifaceted driving factors. The reward function to enhance the damping ability of the suspension by considering multiple driving factors, which can provide a reference for further research on the control of magnetorheological semi-active suspensions for vehicles.

Optimized Longitudinal and Lateral Control Strategy of Intelligent Vehicles Based on Adaptive Sliding Mode Control

To improve the tracking accuracy and robustness of the path-tracking control model for intelligent vehicles under longitudinal and lateral coupling constraints, this paper utilizes the Kalman filter algorithm to design a longitudinal and lateral coordinated control (LLCC) strategy optimized by adaptive sliding mode control (ASMC). First, a three-degree-of-freedom (3-DOF) vehicle dynamics model was established. Next, under the fuzzy adaptive Unscented Kalman filter (UKF) theory, the vehicle state parameter estimation and road adhesion coefficient (RAC) observer were designed to estimate vehicle speed (VS), yaw rate (YR), sideslip angle (SA), and RAC. Then, a layered control concept was adopted to design the path-tracking controller, with a target VS, YR, and SA as control objectives. An upper-level adaptive sliding mode controller was designed using RBF neural networks, while a lower-level tire force distribution controller was designed using distributed sequential quadratic programming (DSQP) to obtain an optimal tire driving force. Finally, the control strategy was validated using Carsim and Matlab/Simulink software under different road adhesion coefficients and speeds. The findings indicate that the optimized control strategy is capable of adaptively adjusting control parameters to accommodate various complex conditions, enhancing the tracking precision and robustness of vehicles even further.

Trajectory Tracking and Docking Control Strategy for Unmanned Surface Vehicles in Water-Based Search and Rescue Missions

Trajectory Tracking and Docking Control Strategy for Unmanned Surface Vehicles in Water-Based Search and Rescue Missions

An adaptive fuzzy coordinated control strategy for hybrid electric vehicles considering clutch wear and engine temperature variation

AbstractAn improved method of clutch coordinated control based on the Kalman filter was proposed to solve the problem that the existing mode switching strategy of hybrid electric vehicles could not adapt to engine temperature changes and clutch wear. First, taking advantage of the relationship between the torque transmitted by the clutch and the starting resistance of the engine, combined with the characteristics of the clutch, the clutch wear was roughly calculated. Accordingly, the control strategy of the clutch in the existing mode switching was improved to adapt to the clutch wear. The adaptive control strategy proposed for clutch wear included the fuzzy control module of the initial engagement pressure, the fuzzy inference module of the clutch engaging pressure change, the clutch wear estimation module and so on. Second, the Kalman filter was used to process the results to improve the estimation accuracy of clutch wear. The engine starting resistance related to starting speed and temperature was modeled to enhance the adaptability of the control strategy to engine temperature. Finally, the designed control strategy was verified in simulation. The results show that the improved control strategy can complete the mode switching when the engine temperature is variable and the clutch is worn. The maximum impact degree increased from 5 m/s3 without wear to 8.5 m/s3 with wear, but it is still less than the index limit, and it can be considered that the proposed strategy can achieve the desired control effect. The fuzzy control algorithm proposed enhances the vehicle's ride comfort during mode switching from pure electric driving to hybrid driving.