Search Results (84)

Accurate estimation of vehicle sideslip angle is vital for the stability and safety of four-wheel independent drive electric vehicles (4WIDEVs), but it faces challenges, including model uncertainties caused by tire yaw stiffness variations and system delays. This paper proposes a novel adaptive fusion strategy that combines the dynamic robust observer (DRO) and the improved adaptive square-root unscented Kalman filter (ASUKF). The DRO is designed based on a two-degrees-of-freedom vehicle model and ensures stability through linear matrix inequalities (LMIs), effectively handling parameter uncertainties and time delays; the ASUKF utilizes a three-degrees-of-freedom model and the magic formula tire model, combined with Sage–Husa adaptive filtering, to address the nonlinear tire dynamics. The key innovation of this paper is the introduction of a fuzzy-rule-based adaptive weighting mechanism that dynamically adjusts the fusion weights of the DRO and ASUKF in real time, thereby exploiting their complementary advantages under uncertainty and nonlinear conditions. The simulation and experimental validations demonstrate that this method significantly improves estimation accuracy, reducing the estimation error of vehicle sideslip angle by an average of 9.36%, and maintains robust performance and dynamic adaptability in various conditions, providing a reliable solution for the real-time state estimation of intelligent electric vehicles. Full article

Distributed drive vehicles provide enhanced actuation flexibility, making longitudinal–lateral coordinated stability control essential for improving vehicle handling and safety under complex driving conditions. Nevertheless, the existing coordinated control strategies commonly employ stability reference models with fixed tire–road friction coefficients, which restrict their adaptability to time-varying adhesion environments. In addition, conventional sliding mode-based lateral stability controllers may exhibit limited performance when confronted with strong nonlinear coupling and external disturbances. To address these issues, this paper proposes an integrated longitudinal–lateral coordinated stability control framework for distributed drive vehicles. A dual unscented Kalman filter-based estimator is developed to identify the tire–road friction coefficients and construct a friction-adaptive reference model for yaw rate and sideslip angle. An adaptive fractional power speed controller with resistance compensation is designed to generate the total longitudinal driving torque, while an adaptive neural sliding mode controller produces the corrective yaw moment for lateral stability enhancement. Furthermore, a pseudoinverse-based torque distribution strategy is employed to allocate the longitudinal torque and yaw moment to individual wheels. Simulation results demonstrate that the proposed framework significantly improves vehicle stability and tracking accuracy compared with conventional control methods under varying road conditions. Full article

With the rapid development of unmanned agricultural machinery technology, the accuracy and stability of agricultural machinery path tracking have become key challenges in achieving precision agriculture. To address the issues of insufficient accuracy and stability in path tracking for rice transplanters in paddy fields, this study proposes a composite control strategy that integrates the extended line-of-sight (LOS) guidance law with an adaptive fuzzy sliding mode control law. By establishing a two degree of freedom dynamic model of the rice transplanter, two extended state observers are designed to estimate the longitudinal and lateral velocities of the rice transplanter in real time. A dynamic compensation mechanism for the sideslip angle is introduced, significantly enhancing the adaptability of the traditional look-ahead guidance law to soil slippage. Furthermore, by combining the approximation capability of fuzzy systems with the adaptive adjustment method of sliding mode control gains, a front wheel steering control law is designed to suppress complex environmental disturbances. The global stability of the closed-loop system is rigorously verified using the Lyapunov theory. Simulation results show that compared to the traditional Stanley algorithm, the proposed method reduces the maximum lateral error by 38.3%, shortens the online time by 23.9%, and decreases the steady-state error by 15.5% in straight-line path tracking. In curved path tracking, the lateral and heading steady-state errors are reduced by 19.2% and 14.6%, respectively. Field experiments validate the effectiveness of this method in paddy fields, with the absolute lateral error stably controlled within 0.1 m, an average error of 0.04 m, and a variance of 0.0027 m². Full article

The sideslip angle is a key state for evaluating the lateral stability of a vehicle. Its accurate estimation is crucial for active safety and intelligent driving assistance systems. To improve the estimation accuracy and robustness of the sideslip angle for distributed drive electric vehicles (DDEV) under extreme maneuvering conditions, this paper proposes a redundant estimation scheme based on multi-source sensor information fusion. Firstly, a dynamic model of the DDEV is established, including the vehicle body dynamics model, wheel rotation dynamics model, tire model, and hub motor model. Subsequently, robust unscented particle filtering (RUPF) and backpropagation (BP) neural network algorithms are developed to estimate the sideslip angle from both the vehicle dynamics and data-driven perspectives. Based on this, a redundant estimation scheme for the sideslip angle is developed. Finally, the effectiveness of the redundant estimation scheme is validated through the Matlab/Simulink-CarSim co-simulation platform using MATLAB R2022b and CarSim 2020.0. Full article

Real-time estimation of vehicle sideslip angle is essential for both safety and performance applications. This study presents a temperature-adaptive Extended Kalman Filter (EKF) that estimates the sideslip angle of a racing vehicle by integrating dynamic and kinematic information. A temperature-dependent Pacejka tire model, derived directly from track tests, is embedded in a 3-degree-of-freedom dual-track vehicle model and used within the EKF to compensate for temperature-induced variations in tire behavior. The adaptive model parameters are identified from standard on-track maneuvers conducted at different tire temperatures, without the need for additional indoor rig testing. Experimental validation on a race track demonstrates that incorporating tire temperature adaptation and combining dynamic and kinematic estimation significantly enhance estimation accuracy, particularly underow-grip and high-performance driving conditions attested by a reduction of 40–50% in RMS error and a further reduction in maximum absolute error. Full article

The vehicle sideslip angle is a core state parameter in vehicle dynamics control. Its accurate estimation is critical for vehicle stability control and the development of active safety systems. In the vehicle sideslip angle estimation method using the traditional Unscented Kalman Filter (UKF), the process noise covariance matrix Q and observation noise covariance matrix R are difficult to adjust adaptively, leading to estimation accuracy degradation under complex driving conditions. This paper proposes a vehicle sideslip angle estimation method that integrates UKF and Deep Reinforcement Learning (DRL), leveraging the adaptive decision-making capability of DRL to dynamically optimize the noise parameters in UKF. A state space incorporating vehicle motion states and filtering performance metrics is constructed, along with an action space that outputs adjustment quantities for the noise covariance matrices. A reward function based on estimation errors and uncertainties is formulated, and the Proximal Policy Optimization (PPO) algorithm is employed to train the policy network. The results indicate that the proposed method effectively improves vehicle sideslip angle estimation accuracy under various driving conditions, including different vehicle speeds, road surface adhesion coefficients, and sensor noise disturbances. Compared with the traditional UKF method, the Root Mean Square Error (RMSE) is reduced by over 30%, and the method demonstrates strong stability and robustness under complex scenarios. This approach provides a new solution for the accurate estimation of key vehicle state parameters and can be extended to fields such as autonomous driving and vehicle active safety. Full article

The Light Electric Vehicle (LEV) possesses the advantages of energy efficiency, environmental friendliness, and excellent control in traffic. Nevertheless, a decrease in vehicle mass and size directly affects the active safety system control and state estimation system of LEVs. This paper presents a novel and efficient analysis technique for evaluating the sensitivity of payload parameter variations on the response and state estimation of the LEV system. Firstly, a dynamic model of the LEV is established, taking into account the payload parameters. Next, the accuracy of the payload parameter trajectory sensitivity is calculated using a combination of the perturbation analysis method and the second-order central difference method. An unscented Kalman filter is specifically developed to estimate various vehicle states, including the sideslip angle, longitudinal velocity, and roll angle. The significance of payload parameters on observation accuracy when payload parameters vary during basic state estimation is assessed. The simulation results obtained using Matlab/Simulink-Carsim^® 2019 demonstrate the ability of the method to effectively depict the correlation between the payload parameters and the system state estimation. Quantitatively, the sideslip angle is most sensitive to the payload mass (average sensitivity: 2.15 × 10⁻²), while the longitudinal velocity is predominantly affected by the payload’s longitudinal position. By integrating perturbation analysis with the central difference method, this approach provides a novel and efficient framework for LEV sensitivity analysis, and it is valuable for the design and estimation of the LEV controller. Full article

Aiming at the problem of model parameter perturbation in vehicle trajectory tracking control, an improved robust model predictive control (RMPC) method is proposed. Based on the two-degree-of-freedom vehicle model and Serret Frenet error model, a multi-cell hypercube vertex modeling is adopted to map the disturbance range of parameters such as vehicle speed and lateral stiffness to a set of vertices, and dynamic linear combination is achieved through normalized weights. The algorithm design mainly focuses on the dual-layer optimization of the switching mechanism, decomposing the infinite time domain problem into finite time domain optimization and terminal constraints. At the same time, it dynamically updates the vertex parameters to match time-varying uncertainties and then combines Lyapunov theory to design a control invariant set. The results show that in complex road conditions and vehicle state transitions, RMPC can reduce the peak lateral deviation from 1.0 m to 0.2 m, converge the heading deviation to within 2 deg, and significantly reduce the mean and root mean square values of control errors compared to traditional MPC, under the influence of vehicle model parameter perturbations. RMPC has good robustness and real-time performance. Full article

Global agriculture is confronting unprecedented pressures from population growth, diminishing arable land, and severe rural labor scarcity, necessitating the advancement of intelligent agricultural equipment. As a core component of precision farming, unmanned agricultural tractors demand highly accurate and robust path tracking control. However, conventional methods often fail to cope with unstructured terrain and dynamic wheel slip under real field conditions. This paper proposes an extended state observer (ESO)-based improved Stanley guidance law, which incorporates real-time sideslip angle observation, adaptive preview-based path curvature compensation, and a sliding mode heading controller. The ESO estimates lateral slip caused by varying soil conditions, while the modified Stanley law utilizes look-ahead path information to proactively adjust the desired heading angle during high-curvature turns. Both co-simulation in Matlab-Carsim and field experiments demonstrate that the proposed method significantly reduces lateral tracking error and overshoot, outperforming classical algorithms such as fuzzy Stanley and sliding mode controller, especially in U-turn scenarios and under low-adhesion conditions. Full article

With the rapid development of deep-sea resource exploration and marine scientific research, wheeled remotely operated vehicles (ROVs) have become crucial for seabed operations. However, under complex seabed conditions, traditional ROV control systems suffer from insufficient trajectory tracking accuracy, poor disturbance rejection capability, and low dynamic torque distribution efficiency. These issues lead to poor motion stability and high energy consumption on sloped terrains and soft substrates, which limits the effectiveness of deep-sea engineering. To address this, we proposed a comprehensive motion control solution for deep-sea wheeled ROVs. To improve modeling accuracy, a coupled kinematic and dynamic model was developed, together with a body-to-terrain coordinate frame transformation. Based on rigid-body kinematics, three-degree-of-freedom kinematic equations incorporating the slip ratio and sideslip angle were derived. By integrating hydrodynamic effects, seabed reaction forces, the Janosi soil model, and the impact of subsidence depth, a dynamic model that reflects nonlinear wheel–seabed interactions was established. For optimizing disturbance rejection and trajectory tracking, a hierarchical control method was designed. At the kinematic level, an improved model predictive control framework with terminal constraints and quadratic programming was adopted. At the dynamic level, non-singular fast terminal sliding mode control combined with a fixed-time nonlinear observer enabled rapid disturbance estimation. Additionally, a dynamic torque distribution algorithm enhanced traction performance and trajectory tracking accuracy. Full article

To address the issue of poor yaw stability in distributed-drive electric pickup trucks at medium-to-high speeds, particularly under the influence of continuously varying tire forces and road adhesion coefficients, a novel Kalman filter-based method for estimating the road adhesion coefficient, combined with a Tube-based Model Predictive Control (Tube-MPC) algorithm, is proposed. This integrated approach enables real-time estimation of the dynamically changing road adhesion coefficient while simultaneously ensuring vehicle yaw stability is maintained under rapid response requirements. The developed hierarchical yaw stability control architecture for distributed-drive electric pickup trucks employs a square root cubature Kalman filter (SRCKF) in its upper layer for accurate road adhesion coefficient estimation; this estimated coefficient is subsequently fed into the intermediate layer’s corrective yaw moment solver where Tube-based Model Predictive Control (Tube-MPC) tracks desired sideslip angle and yaw rate trajectories to derive the stability-critical corrective yaw moment, while the lower layer utilizes a quadratic programming (QP) algorithm for precise four-wheel torque distribution. The proposed control strategy was verified through co-simulation using Simulink and Carsim, with results demonstrating that, compared to conventional MPC and PID algorithms, it significantly improves both the driving stability and control responsiveness of distributed-drive electric pickup trucks under medium- to high-speed conditions. Full article

Accurate estimation of the vehicle sideslip angle is critical for the effective operation of advanced driver assistance systems and active safety functions such as electronic stability control. However, direct measurement of sideslip angle is impractical in series-production vehicles due to high sensor cost. Furthermore, existing estimation methods often neglect the impact of model uncertainties on estimation error, which can compromise estimation reliability and, consequently, vehicle stability. To address these limitations, this paper proposes an interval observer based on a Kalman filter that accounts explicitly for model uncertainties in the sideslip angle estimation process. The proposed method generates both upper and lower bounds of the estimated sideslip angle, providing a quantifiable measure of uncertainty that enhances the robustness of control systems that depend on this measurement. Given the limitations of simplified vehicle models, a combined vehicle roll and lateral dynamics model is utilized to improve estimation accuracy. The effectiveness of the proposed methodology is demonstrated through a series of simulation experiments conducted using CarSim. Full article

To improve the stability and safety of electric vehicles during medium-to-high-speed cornering, this paper investigates torque differential control for dual rear-wheel hub motor drive systems, extending beyond traditional speed control based on the Ackermann steering model. A nonlinear three-degree-of-freedom vehicle dynamics model incorporating the Dugoff tire model was established. By introducing the maximum correntropy criterion, an unscented Kalman filter was developed to estimate longitudinal velocity, sideslip angle at the center of mass, and yaw rate. Building upon the speed differential control achieved through Ackermann steering model-based rear-wheel speed calculation, improvements were made to the conventional exponential reaching law, while a novel switching function was proposed to formulate a new sliding mode controller for computing an additional yaw moment to realize torque differential control. Finally, simulations conducted on the Carsim/Simulink platform demonstrated that the maximum correntropy criterion unscented Kalman filter effectively improves estimation accuracy, achieving at least a 22.00% reduction in RMSE metrics compared to conventional unscented Kalman filter. With torque control exhibiting higher vehicle stability than speed control, the RMSE values of yaw rate and sideslip angle at the center of mass are reduced by at least 20.00% and 4.55%, respectively, enabling stable operation during medium-to-high-speed cornering conditions. Full article

Addressing lateral-directional control challenges during unmanned aerial vehicle (UAV) landing in complex wind fields, this study proposes a drift angle control strategy that integrates coordinated heading and trajectory regulation. An adaptive radius optimization method for the Dubins approach path is designed using wind speed estimation. By developing a wind-coupled flight dynamics model, we establish a roll angle control loop combining the $L_1$ nonlinear guidance law with Linear Active Disturbance Rejection Control (LADRC). Simulation tests against conventional sideslip approach and crab approach, along with flight tests, confirm that the proposed autonomous landing system achieves smoother attitude transitions during landing while meeting all touchdown performance requirements. This solution provides a theoretically rigorous and practically viable approach for safe UAV landings in challenging wind conditions. Full article

Coordinating vehicle handling stability and energy consumption remains a key challenge for distributed driving electric vehicles (DDEVs). In this paper, a hierarchical torque vectoring control strategy is proposed to address this issue. First, a tire road friction coefficient (TRFC) estimator based on the fusion of vision and dynamic is developed to accurately and promptly obtain the TRFC in the upper layer. Second, a direct yaw moment control (DYC) strategy based on the adaptive model predictive control (MPC) is designed to ensure vehicle stability in the middle layer, where tire cornering stiffness is updated dynamically based on the estimated TRFC. Then, the lower layer develops the torque vectoring allocation controller, which comprehensively considers handling stability and energy consumption and distributes the driving torques among the wheels. The weight between stability and economy is coordinated according to the stability boundaries derived from an extended phase-plane correlated with the TRFC. Finally, Hardware-in-the-Loop (HIL) simulations are conducted to validate the effectiveness of the proposed strategy. The results demonstrate that compared with the conventional stability torque distribution strategy, the proposed control strategy not only reduces the RMSE of sideslip angle by 44.88% but also decreases the motor power consumption by 24.45% under DLC conditions, which indicates that the proposed method can significantly enhance vehicle handling stability while reducing energy consumption. Full article