Search Results (267)

In modern vehicles, Autonomous Driving Systems (ADSs) are designed to operate partially or fully without human intervention. The ADS pipeline comprises multiple layers, including sensors, perception, localization, mapping, path planning, and control. The Robot Operating System (ROS) is a widely adopted framework that supports the modular development and integration of these layers. Among them, the path-planning and control layers remain particularly challenging due to several limitations. Classical path planners often struggle with non-smooth trajectories and high computational demands. Meta-heuristic optimization algorithms have demonstrated strong theoretical potential in path planning; however, they are rarely implemented in real-time ROS-based systems due to integration challenges. Similarly, traditional PID controllers require manual tuning and are unable to adapt to system disturbances. This paper proposes a ROS-based ADS architecture composed of eight integrated nodes, designed to address these limitations. The path-planning node leverages a meta-heuristic optimization framework with a cost function that evaluates path feasibility using occupancy grids from the Hector SLAM and obstacle clusters detected through the DBSCAN algorithm. A dynamic goal-allocation strategy is introduced based on the LiDAR range and spatial boundaries to enhance planning flexibility. In the control layer, a modified Pure Pursuit algorithm is employed to translate target positions into velocity commands based on the drift angle. Additionally, an adaptive PID controller is tuned in real time using the Differential Evolution (DE) algorithm, ensuring robust speed regulation in the presence of external disturbances. The proposed system is practically validated on a four-wheel differential drive robot across six scenarios. Experimental results demonstrate that the proposed planner significantly outperforms state-of-the-art methods, ranking first in the Friedman test with a significance level less than 0.05, confirming the effectiveness of the proposed architecture. Full article

The design and optimization of drive distribution strategies are critical for enhancing the performance of Formula Student electric racing cars, which face demanding operational conditions such as rapid acceleration, tight cornering, and variable track surfaces. Given the increasing complexity of racing environments and the need for adaptive control solutions, a multi-mode adaptive drive distribution strategy for four-wheel-drive Formula Student electric racing cars is proposed in this study to meet specialized operational demands. Based on the dynamic characteristics of standardized test scenarios (e.g., straight-line acceleration and figure-eight loop), two control modes are designed: slip-ratio-based anti-slip control for longitudinal dynamics and direct yaw moment control for lateral stability. A CarSim–Simulink co-simulation platform is established, with test scenarios conforming to competition standards, including variable road adhesion coefficients (μ is 0.3–0.9) and composite curves. Simulation results indicate that, compared to conventional PID control, the proposed strategy reduces the peak slip ratio to the optimal range of 18% during acceleration and enhances lateral stability in the figure-eight loop, maintaining the sideslip angle around −0.3°. These findings demonstrate the potential for significant improvements in both performance and safety, offering a scalable framework for future developments in racing vehicle control systems. Full article

This manuscript investigates the feasibility of Four-In-Wheel Electronic Differential Systems (4 IW-EDSs) within contemporary electric vehicles (EVs), emphasizing their benefits for stability regulation predicated on steering angles. Through an extensive literature review, we conduct a comparative analysis of various in-wheel-motor models in terms of power output, efficiency, and torque characteristics. Furthermore, we explore the distinctions between IW-EDSs and steer-by-wire systems, as well as conventional systems, while evaluating recent research findings to determine their implications for the evolution of electric mobility. Moreover, this paper addresses the necessity for fault-tolerant methodologies to boost reliability in practical applications. The findings yield valuable insights into the challenges and impacts associated with the implementation of differential steering control in four-wheel independent-drive electric vehicles. This study aims to explore the interaction between these systems, optimize torque distribution, and discover the most ideal control strategy that will improve maneuverability, stability, and energy efficiency, thereby opening up new frontiers in the development of next-generation electric vehicles with unparalleled performance and safety features. Full article

To address the challenge of fault diagnosis for in-wheel motors in four-wheel independent driving systems under variable driving conditions and harsh environments, this paper proposes a novel method based on two-stream 2DCNNs (two-dimensional convolutional neural networks) with a DCBA (depthwise convolution block attention) module. The main contributions are twofold: (1) A DCBA module is introduced to extract multi-scale features—including prominent, local, and average information—from grayscale images reconstructed from vibration signals across different domains; and (2) a two-stream network architecture is designed to learn complementary feature representations from time-domain and time–frequency-domain signals, which are fused through fully connected layers to improve diagnostic accuracy. Experimental results demonstrate that the proposed method achieves high recognition accuracy under various working speeds, loads, and road surfaces. Comparative studies with SENet, ECANet, CBAM, and single-stream 2DCNN models confirm its superior performance and robustness. The integration of DCBA with dual-domain feature learning effectively enhances fault feature extraction under complex operating conditions. Full article

This study investigates the dimensional synthesis of steering mechanisms for front-wheel-drive, two-axle, four-wheeled vehicles using two metaheuristic optimization algorithms: Differential Evolution with golden ratio (DE-gr) and Improved Particle Swarm Optimization (IPSO). The vehicle under consideration has a track-to-wheelbase ratio of 0.5 and an inner wheel steering angle of 70 degrees. The mechanisms synthesized include the Ackermann steering mechanism and two variants (Type I and Type II) of the Watt-I six-bar steering mechanisms, also known as central-lever steering mechanisms. To ensure accurate steering and minimize tire wear during cornering, adherence to the Ackermann steering condition is enforced. The objective function combines the mean squared structural error at selected steering positions with a penalty term for violations of the Grashoff inequality constraint. Each optimization run involved 100 or 200 iterations, with numerical experiments repeated 100 times to ensure robustness. Kinematic simulations were conducted in ADAMS v2015 to visualize and validate the synthesized mechanisms. Performance was evaluated based on maximum structural error (steering accuracy) and mechanical advantage (transmission efficiency). The results indicate that the optimized Watt-I six-bar steering mechanisms outperform the Ackermann mechanism in terms of steering accuracy. Among the Watt-I variants, the Type II designs demonstrated superior performance and convergence precision compared to the Type I designs, as well as improved results compared to prior studies. Additionally, the optimal Type I-2 and Type II-2 mechanisms consist of two symmetric Grashof mechanisms, can be classified as non-Ackermann-like steering mechanisms. Both optimization methods proved easy to implement and showed reliable, efficient convergence. The DE-gr algorithm exhibited slightly superior overall performance, achieving optimal solutions in seven cases compared to four for the IPSO method. Full article

For the longitudinal tire force estimation problem of four-wheel independent drive electric vehicles (4WIDEVs), traditional model-based observers have limitations such as high modeling complexity and strong parameter sensitivity, while pure data-driven methods are susceptible to noise interference and have insufficient generalization ability. Therefore, this study proposes a joint estimation framework that integrates data-driven and modified recursive subspace identification algorithms. Firstly, based on the electromechanical coupling mechanism, an electric drive wheel dynamics model (EDWM) is constructed, and multidimensional driving data is collected through a chassis dynamometer experimental platform. Secondly, an improved proportional integral observer (PIO) is designed to decouple the longitudinal force from the system input into a state variable, and a subspace identification recursive algorithm based on correction term with forgetting factor (CFF-SIR) is introduced to suppress the residual influence of historical data and enhance the ability to track time-varying parameters. The simulation and experimental results show that under complex working conditions without noise and interference, with noise influence (5% white noise), and with interference (5% irregular signal), the mean and mean square error of longitudinal force estimation under the CFF-SIR algorithm are significantly reduced compared to the correction-based subspace identification recursive (C-SIR) algorithm, and the comprehensive estimation accuracy is improved by 8.37%. It can provide a high-precision and highly adaptive longitudinal force estimation solution for vehicle dynamics control and intelligent driving systems. Full article

This study develops a real-time energy-efficient control strategy for distributed-drive electric tractors (DDETs) to minimize electrical energy consumption during traction operations. Taking a four-wheel independently driven DDET as the research object, we conduct dynamic analysis of draft operations and establish dynamic models of individual components in the tractor’s drive and transmission system. A backpropagation (BP) neural network-based operational speed prediction model is constructed to forecast operational speed within a finite prediction horizon. Within the model predictive control (MPC) framework, a real-time energy-efficient control strategy is formulated, employing a dynamic programming algorithm for receding horizon optimization of energy consumption minimization. Through plowing operation simulation with comparative analysis against a conventional equal torque distribution strategy, the results indicate that the proposed real-time energy-efficient control strategy exhibits superior performance across all evaluation metrics, providing valuable technical guidance for future research on energy-efficient control strategies in agricultural electric vehicles. Full article

Trajectory tracking is a crucial task for autonomous mobile robots, requiring smooth and safe execution in dynamic environments. This study uses a nonlinear model predictive controller (MPC) to ensure accurate trajectory tracking of a four-wheel drive, four-wheel steer (4WD-4WS) mobile robot. However, the MPC’s performance depends on the optimal tuning of its key parameters, a challenge addressed using the Multivariate Gaussian Mixture Model Continuous Ant Colony Optimization (MGMM-${\mathrm{ACO}}_R$) algorithm. This method improves on the classic ${\mathrm{ACO}}_R$ algorithm by overcoming two major limitations: the lack of consideration for interdependencies between optimized variables, and an inadequate balance between global exploration and local exploitation. The proposed approach is validated by a two-phase evaluation. Firstly, benchmark function evaluations demonstrate its superiority over established optimization algorithms, including ACO, ${\mathrm{ACO}}_R$, and PSO and its variants, in terms of convergence speed and solution accuracy. Secondly, MGMM-${\mathrm{ACO}}_R$ is integrated into the MPC framework and tested in various scenarios, including trajectory tracking with circular and eight trajectories and dynamic obstacle avoidance during trajectory tracking. The results, evaluated based on trajectory error, control effort, and computational latency, confirm the robustness of the proposed method. In particular, the explicit modeling of correlations between variables in MGMM-${\mathrm{ACO}}_R$ guarantees stable, collision-free trajectory tracking, outperforming conventional ${\mathrm{ACO}}_R$-based approaches that optimize variables independently. Full article

As a core component of agricultural machinery autonomous navigation, path tracking control holds significant research value. The pure pursuit algorithm has become a prevalent method for agricultural vehicle navigation due to its effectiveness at low speeds, yet its performance critically depends on the selection of the look-ahead distance. The conventional approaches require extensive parameter tuning due to the complex influencing factors, while fixed look-ahead distances struggle to balance the tracking accuracy and adaptability. Considerable effort is required to fine-tune the system to achieve optimal performance, which directly affects the accuracy of the path tracking and the results in the cumbersome task of selecting an appropriate goal point for the tracking path. To address these challenges, this paper introduces a pure pursuit algorithm for high-clearance sprayers in agricultural machinery, utilizing a self-adjusting look-ahead distance. By developing a kinematic model of the pure pursuit algorithm for agricultural machinery, an evaluation function is then employed to estimate the pose of the machinery and identify the corresponding optimal look-ahead distance within the designated area. This is done based on the principle of minimizing the overall error, enabling the dynamic and adaptive optimization of the look-ahead distance within the pure pursuit algorithm. Finally, this algorithm was verified in simulations and bumpy field tests under various different conditions, with the average value of the lateral error reduced by more than 0.06 m and the tuning steps also significantly reduced compared to the fixed look-ahead distance in field tests. The tracking accuracy has been improved and the applicability of the algorithm for rapid deployment has been enhanced. Full article

Aiming at the problems of energy utilization efficiency and braking stability in electric vehicles, a high-efficiency and energy-saving control strategy that takes both driving and braking into account is proposed with the distributed hub motor-driven vehicle as the research object. Under regular driving and braking conditions, the front and rear axle torque distribution coefficients are optimized by an adaptive particle swarm algorithm based on simulated annealing and a multi-objective co-optimization strategy based on variable weight coefficients, respectively. During emergency braking, the anti-lock braking strategy (ABS) based on sliding mode control realizes the independent distribution of torque among four wheels. The joint simulation verification based on MATLAB R2023a/Simulink-Carsim 2020.0 shows that under World Light Vehicle Test Cycle (WLTC) conditions, the optimization strategy reduces the driving energy consumption by 3.20% and 2.00%, respectively, compared with the average allocation and the traditional strategy. The braking recovery energy increases by 4.07% compared with the fixed proportion allocation, improving the energy utilization rate of the entire vehicle. The wheel slip rate can be quickly stabilized near the optimal value during emergency braking under different adhesion coefficients, which ensures the braking stability of the vehicle. The effectiveness of the strategy is verified. Full article

Precise modeling of differential drive robots is crucial for effective control and trajectory planning in autonomous systems. A comparative analysis of two modeling approaches for a four-wheel differential drive robot is presented in this paper. The first approach, named Motor-Based Model (MBM), identifies four transfer functions, one for each motor, while the second approach, named Simplified Model (SM), uses only two transfer functions, one for linear velocity and another for angular velocity. Both models were validated by comparing their predicted trajectories against real odometry data obtained from a SLAM system implemented on a differential-drive robot. This provided a practical assessment of each model’s accuracy and underscored the importance of model selection in control design and navigation tasks. The results showed that the Motor-Based Model (MBM) consistently outperformed the Simplified Model (SM) in terms of odometry accuracy, both in position and orientation. Across all trajectories, the average RMSE for position using MBM was 0.309 m, while the SM recorded a higher average RMSE of 0.414 m. Similarly, the maximum position error averaged 0.522 m for MBM and 0.710 m for SM, confirming that MBM is more accurate and consistent in position tracking. Regarding the results of orientation estimation, when averaged across all experiments, the MBM maintained a lower angular RMSE of 0.170 rad in contrast to SM, which achieves an RMSE of 0.239 rad. The maximum angular error was also higher for the MBM at 0.316 rad, compared to 0.447 rad for the SM. Moreover, the computational performance evaluation indicated that the SM consistently outperformed MBM, achieving a 30% reduction in simulation time and substantially lower memory usage. These results demonstrate the relationship between model complexity and accuracy and suggest that the motor-specific model is more appropriate for applications requiring precise mapping or localization, such as SLAM, while the simplified model may be suitable for simpler use cases with lower computational requirements, such as embedded systems with limited resources. This paper provides a practical evaluation of the accuracy and computational performance of two modeling approaches, highlighting the implications of model selection for the design of navigation tasks. Full article

With the ongoing advancements in automation technology, four-wheeled independent drive and steering (4WID-4WIS) automated guided vehicles (AGVs) are increasingly employed in intelligent logistics and warehousing systems. To enhance the performance of path tracking accuracy and cruising stability of AGVs, an automatic cruising methodology is proposed operating in complex environments. The approach integrates lateral control through model predictive control (MPC), which is optimized by a Long Short-Term Memory (LSTM) network, alongside fuzzy PID control for longitudinal management. By utilizing the LSTM network for trajectory prediction, the system can anticipate future vehicle states and outputs, thereby facilitating proactive adjustments that enhance the performance of the MPC lateral controller and improve both trajectory tracking accuracy and response speed. Concurrently, the fuzzy PID control strategy for longitudinal management increases the system’s adaptability to dynamic environments. The proposed methodology has been demonstrated in a physical prototype operating in real practical environments. Comparative results demonstrate that the LSTM-MPC significantly outperforms conventional MPC in lateral control accuracy. Additionally, the fuzzy PID controller yields superior longitudinal performance compared to traditional dual-PID and constant-speed strategies. This advantage is particularly evident in curved path segments, where the proposed fuzzy PID–LSTM–MPC framework achieves significantly higher lateral and longitudinal tracking accuracy compared to other control strategies. Full article

To address the challenge of optimizing system adaptability, disturbance rejection, control precision, and convergence speed simultaneously in four-wheel steering (4WS) stability control, a 4WS controller with a variable steering ratio (VSR) strategy and fast adaptive super-twisting (FAST) sliding mode control is proposed to control and output the steering angles of four wheels. The ideal VSR strategy is designed based on the constant yaw rate gain, and a cubic quasi-uniform B-spline curve fitting method is innovatively used to optimize the VSR curve, effectively mitigating steering fluctuations and obtaining precise reference front wheel angles. A controller based on FAST is designed for active rear wheel steering control using a symmetric 4WS vehicle model. Under double-lane change conditions with varying speeds, the simulations show that, compared with the constant steering ratio, the proposed VSR strategy enhances low-speed sensitivity and high-speed stability, improving the system’s adaptability to different operating conditions. Compared with conventional sliding mode control methods, the proposed FAST algorithm reduces chattering while increasing convergence speed and control precision. The VSR-FAST controller achieves optimization levels of more than 7.3% in sideslip angle and over 41% in yaw rate across different speeds, achieving an overall improvement in the stability control performance of the 4WS system. Full article

A four-steering-wheel heavy-duty Automated Guided Vehicle (AGV) is prone to lateral instability and wheel slippage during acceleration, climbing, and small-radius turns. To address this issue, a trajectory tracking strategy considering lateral stability and an optimal driving torque distribution strategy considering load transfer and tire adhesion coefficient are proposed. Firstly, a three-degree-of-freedom AGV trajectory tracking model is established, tracking error and sideslip angle are incorporated into the cost function, and an improved model predictive trajectory tracking controller is proposed. Secondly, the longitudinal and yaw dynamic model of AGV is established, and vertical load transfer is analyzed. With the goal of minimizing tire adhesion utilization rate, quadratic programming is used for the optimal distribution of driving torque. Finally, through co-simulation using ADAMS and MATLAB on a narrow “climbing straight+ S-curve” road, the maximum tracking error is 0.0443 m. Compared to the unimproved model predictive control and average driving torque distribution strategy, the sideslip angle is reduced by 58.18%, the maximum tire adhesion utilization rate is reduced by 6.62%, and climbing gradeability on wet roads is enhanced. Full article

This paper presents a method for designing a static output feedback integrated path tracking controller for autonomous vehicles. For path tracking, state–space model-based control methods, such as linear quadratic regulator, H_∞ control, sliding mode control, and model predictive control, have been selected as controller design methodologies. However, these methods adopt full-state feedback. Among the state variables, the lateral velocity, or the side-slip angle, is hard to measure in real vehicles. To cope with this problem, it is desirable to use a state estimator or static output feedback (SOF) control. In this paper, an SOF control is selected as the controller structure. To design the SOF controller, a linear quadratic optimal control and sliding mode control are adopted as controller design methodologies. Front wheel steering (FWS), rear wheel steering (RWS), four-wheel steering (4WS), four-wheel independent braking (4WIB), and driving (4WID) are adopted as actuators for path tracking and integrated as several actuator configurations. For better performance, a lookahead or preview function is introduced into the state–space model built for path tracking. To verify the performance of the SOF path tracking controller, simulations are conducted on vehicle simulation software. From the simulation results, it is shown that the SOF path tracking controller presented in this paper is effective for path tracking with limited sensor outputs. Full article