Control Strategy For Vehicle Research Articles

An adaptive prediction method based on VMD-GRU for future driving condition of vehicle

Driving conditions prediction plays an important role in energy-saving control strategy for electric vehicle. However, the complexity of the changes in road conditions poses a great challenge to the accurate prediction of driving condition. To address this problem, this paper proposes an adaptive Sliding Window (SW) and Gated Recurrent Unit (GRU) algorithm to predict the driving conditions in a short period, and the algorithm enables to adjust the size of the SW promptly when the driving conditions change frequently. A smaller window is adopted in the case of drastically changing speeds, and a larger window is adopted in the case of smooth speeds. Firstly, Principal Component Analysis (PCA) and k-means clustering are used to construct sample driving conditions with the same characteristics. Then the instantaneous frequency of the sample driving conditions is calculated by Hilbert transform and Variational Mode Decomposition (VMD), and the optimal window size applicable to the conditions with different frequencies is quantitatively calculated. The model provides precise predictions with root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of 0.8799, 0.5443 and 0.8362%, respective. The ablation experiments show that the improved SW enables the GRU prediction model to capture the trends of different driving conditions more accurately, and improves the accuracy and robustness of the prediction model.

DC microgrid for EV charging station with EV control by using STSM controllers

Abstract This study explores innovative control strategies for electric vehicle (EV) charging stations in a DC Microgrid powered by solar and wind energy. A new methodology regulates the dc-link voltage through various converters while a modified vector control method enhances the performance of switched reluctance motors (SRMs). The implementation of super twisting sliding mode (STSM) controllers show superior performance compared to traditional PI and Fuzzy controllers. The design of an asymmetrical converter with four battery banks also minimizes charging durations. The real-time test system (RTS) effectively managed power generation within a DC microgrid, demonstrating a stable voltage at the DC bus despite variations in total generation from photovoltaic (PVS) and wind systems. In Case 1, the controller successfully maintained power balance while charging electric vehicles and managing DC loads. During load torque adjustments, the system maintained a steady motor speed of 320 RPM even with a load torque increase from 5 Nm to 50 Nm at 3 s, showcasing its robust vector control strategy. Notably, the system facilitated a reverse operation at 10 km h−1 (80 RPM) by seamlessly adjusting the engine’s reference speed from 80 RPM to −80 RPM at t = 4.0 s. The vector control causes the engine speed heading to be opposite in a natural manner., indicating its innovative capability to handle diverse operational scenarios. The MATLAB/Simulink package serves as the foundation for the proposed model, which is then integrated into OPAL-RT modules to create a Hardware-in-the-Loop (HIL) system for showcasing diverse outcomes. Different outcomes are deliberated with validated justifications of the suggested approach. The research is linked to Sustainable Development Goals 7 (Affordable and clean energy) and 13 (Climate action).

A velocity adaptive steering control strategy of autonomous vehicle based on double deep Q-learning network with varied agents

Autonomous vehicle steering control is sensitive to the vehicle driving speed and traditional model-based approaches are limited by the accuracy of the control model in various driving speed scenarios. To address these challenges, this study proposes a model-free control strategy based on deep reinforcement learning (DRL). In this strategy, the improved double deep Q-learning network (DDQN) with varied agents is employed for steering control to minimize the tracking errors across varying speeds. According to the kinematic characteristics of the vehicle, a dynamic action space is applied to enhance the tracking capability at high speeds. Furthermore, to ensure the output of the agent is more stable, a velocity adaptive reward function is designed by incorporating an action penalty factor. The performance of the proposed strategy is evaluated through simulation and experimental comparisons with other existing algorithms at a double-lane change maneuver. The results demonstrate that the DDQN-based strategy can effectively adapt to various vehicle speeds and perform the tracking task more accurately and stably. Finally, the feasibility of this strategy is verified using an actual prototype vehicle.

Research on the Smooth Switching Control Strategy of Electric Vehicle Charging Stations Based on Photovoltaic–Storage–Charging Integration

To facilitate seamless transitions between grid-connected and islanded modes in PV–storage–charging integration, an energy storage system converter is designated as the subject of investigation, and its operational principles are examined. Feed-forward decoupling, double closed-loop, constant-power (PQ), constant-voltage–constant-frequency (V/F), and constant-voltage charge and discharge control strategies are developed. The PQ and V/F control framework of the energy storage battery comprises an enhanced common current inner loop and a switching voltage outer loop. The current reference value output by the voltage outer loop and the voltage signal output by the current inner loop are compensated. The transient impact is reduced, and the smooth switching of the microgrid from the grid-connected mode to the island mode is realized, which significantly improves the power quality and ensures the uninterrupted charging of electric vehicles and the stable operation of the key load of the system. By constructing a simulation model of the photovoltaic energy storage microgrid on the MATLAB/Simulink platform, the practicability of the control strategy proposed in this paper is verified.

An Adaptive Cruise Control Strategy for Intelligent Vehicles Based on Hierarchical Control

To minimize the occurrence of traffic accidents, such as vehicle rear-end collisions, while enhancing vehicle following, stability, economy, and ride comfort, a hierarchical adaptive cruise control strategy for vehicles is proposed. The upper-level controller computes the desired vehicle output acceleration based on model predictive control and switches between speed and spacing control in accordance with driving conditions. The brake/throttle opening switching model, brake control inverse model, and throttle opening inverse model in the lower-level controller of ACC are designed to obtain the desired throttle opening and braking pressure of the vehicle, thereby achieving control of the vehicle. A joint simulation platform was established using PreScan, CarSim and Matlab/Simulink. Finally, simulations for three typical working conditions were conducted in Simulink to verify the performance of the adaptive cruise control strategy. The results indicate that, in both the constant-speed cruise and vehicle-following cruise conditions, the vehicle can rapidly and stably follow the set initial speed and consistently maintain a safe distance from the preceding vehicle. Under the emergency braking condition, the vehicle can promptly respond with deceleration, ensuring driving safety. The proposed control strategy can accurately and safely track the target vehicle in diverse driving conditions and can concurrently fulfill the requirements of economy and comfort during vehicle travel.

Composite braking control strategy for electric vehicles based on different adhesion coefficients

Composite braking control strategy for electric vehicles based on different adhesion coefficients

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.

Optimization and Control Strategies for Mechanical Vehicle Trajectory Research

With the rapid advancement of contemporary transportation and automation technology, the optimization of mechanical vehicle trajectories and control strategies have gradually become a focal area of research. Precise trajectory optimization not only enhances the driving efficiency of vehicles but also significantly reduces energy consumption and safety hazards. This paper reviews the basic theory of mechanical vehicle motion trajectories, focusing on the methods of trajectory optimization and control strategies. It covers model-based trajectory optimization, data-driven optimization methods, and various control strategies. Through an analysis of existing research, it points out the challenges and prospects of applying trajectory optimization and control strategies in complex environments.

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.