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The differential steering can be used not only as the backup system of steer-by-wire, but also as the only steering system. Because the differential steering is realized through the differential moment between the coaxial left and right driving wheels, the sharp reduction of the load on the inner driving wheel will directly lead to the failure of t...
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... so-called differential steering refers to the steering through the difference of the driving torques between the coaxial left and right wheels. Its structure is shown in Fig 2. When the driver's intention is provided to the electronic control unit (ECU) through the steering wheel, the ECU will give a command to the hub motors of the left and right front wheels respectively to generate two different driving forces. Due to the existence of kingpin offset, r σ , these two driving forces generate two torques around their respective kingpins, τ dr and τ dl , to deflect the wheel to the longitudinal centerline of the vehicle. ...
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Citations
... In recent decades, modern engines have been widely used in various kinds of industry fields. This equipment can conveniently provide enough power for the application of engineering machinery and also results in high enough strength demands for the main parts of itself, especially for the power transmission parts such as the connecting rod and the crankshaft [1,2]. In addition, the lightweight design methods have been extensively used in engine parts, which also leads to the higher strength demands for these parts [3,4]. ...
The high-cycle bending fatigue experiment is one of the most important necessary steps in guiding the crankshaft manufacturing process, especially for high-power engines. In this paper, an accelerated method was proposed to shorten the time period of this experiment. First, the loading period was quickened through the prediction of the residual fatigue life based on the unscented Kalman filtering algorithm approach and the crack growth speed. Then, the accuracy of the predictions was improved obviously based on the modified training section based on the theory of fracture mechanics. Finally, the fatigue limit load analysis result was proposed based on the predicted fatigue life and the modified SAFL (statistical analysis for the fatigue limit) method. The main conclusion proposed from this paper is that compared with the conventional training sections, the modified training sections based on the theory of fracture mechanics can obviously improve the accuracy of the remaining fatigue life prediction results, which makes this approach more suitable for the application. In addition, compared with the system’s inherent natural frequency, the fatigue crack can save the experiment time more effectively and thus is superior to the former factor as the failure criterion parameter.
... Many related studies integrated ACC and steer-by-wire (SBW) to ensure the stable driving of ACC vehicles [21,22]. As a kind of SBW and new type of steering mechanism, differential steering can generate different driving torques by controlling left and right wheels, which simplifies the complexity of the steering system [23,24]. It is usually applied to trajectory tracking control. ...
To ensure lateral stability during the cruising of a differential steering vehicle (DSV), this paper presents a curving adaptive cruise control (ACC) system coordinated with a differential steering control (DSC) system, which considers both longitudinal cruising capability and lateral stability on curved roads. Firstly, a DSV dynamics model is developed and a control strategy architecture for a curving ACC system is designed. Then, the car-following control strategy for the curving ACC system is designed based on the fuzzy model predictive control (FMPC) algorithm. The strategy aims to improve the economy and balances car following, safety, comfort and economy. Moreover, fuzzy logic rules are designed to update the weight coefficients of the performance indicators in real time. Finally, the lateral stability controller is designed based on the preview algorithm and the sliding mode control (SMC) algorithm. The simulation results show that the lateral stability of the DSV during the curving cruise is realized via the control of the differential drive torque of the two front wheels. The proposed FMPC controller and SMC controller based on the preview control algorithm satisfy the performance in terms of vehicle following and lateral stable driving in the process of cruising.
... As mentioned in the former chapter, usually great dispersion exists in the test results of the fatigue experiment, especially for the high cycle fatigue test of the solid parts. On the other hand, the service life of a crankshaft is limited to a certain number of cycles depending on the demand of the travelling distance [40][41][42]. As a result of this, compared with the common fatigue property evaluation parameter (usually the fatigue life under a given load), it is more important to correctly evaluate the high-cycle fatigue load of a crankshaft under a specified fatigue life. ...
In modern engineering application, enough high cycle bending fatigue strength is the necessary factor to provide the basic safety security for the application of the crankshaft in automobile engines (both diesel and gasoline types). At present, this parameter is usually obtained through the standard bending fatigue experiment process, which is time consuming and expensive. In this paper, a new accelerated crankshaft bending fatigue experiment was proposed step by step. First the loading procedure was quickened through the prediction of the residual fatigue life based on the UKF (unscented Kalman filtering algorithm). Then the accuracy of the predictions was improved based on the modified sampling range and the theory of fracture mechanics. Finally the statistical analysis method of the fatigue limit load was performed based on the above predictions. The main conclusion of this paper is that the proposed accelerated bending fatigue experiment can save more than 30% of the bending fatigue experiment period and provide nearly the same fatigue limit load analysis result. In addition, compared with the particle filtering algorithm method, the modified UKF can provide much higher accuracy in predicting the residual bending fatigue life of the crankshaft , which makes this method more superior to be applied in actual engineering.
... Path tracking control is based on the planned path at the upper level and uses a specific control algorithm for tracking control [28]. Existing issues in vehicle path tracking primarily include inaccuracies in path tracking and control instability [29], as well as the presence of dynamic and kinematic constraints in the vehicle [30]. To address these concerns, researchers from both domestic and foreign backgrounds have implemented various improvement strategies for enhancing path-tracking performance. ...
Path planning and tracking control is an essential part of autonomous vehicle research. In terms of path planning, the artificial potential field (APF) algorithm has attracted much attention due to its completeness. However, it has many limitations, such as local minima, unreachable targets, and inadequate safety. This study proposes an improved APF algorithm that addresses these issues. Firstly, a repulsion field action area is designed to consider the velocity of the nearest obstacle. Secondly, a road repulsion field is introduced to ensure the safety of the vehicle while driving. Thirdly, the distance factor between the target point and the virtual sub-target point is established to facilitate smooth driving and parking. Fourthly, a velocity repulsion field is created to avoid collisions. Finally, these repulsive fields are merged to derive a new formula, which facilitates the planning of a route that aligns with the structured road. After path planning, a cubic B-spline path optimization method is proposed to optimize the path obtained using the improved APF algorithm. In terms of path tracking, an improved sliding mode controller is designed. This controller integrates lateral and heading errors, improves the sliding mode function, and enhances the accuracy of path tracking. The MATLAB platform is used to verify the effectiveness of the improved APF algorithm. The results demonstrate that it effectively plans a path that considers car kinematics, resulting in smaller and more continuous heading angles and curvatures compared with general APF planning. In a tracking control experiment conducted on the Carsim–Simulink platform, the lateral error of the vehicle is controlled within 0.06 m at both high and low speeds, and the yaw angle error is controlled within 0.3 rad. These results validate the traceability of the improved APF method proposed in this study and the high tracking accuracy of the controller.
... In recent years, with the rapid development of intelligent manufacturing and the industrial Internet, health monitoring has been widely used in vehicle control systems [1,2], battery health management systems [3][4][5], operation and maintenance systems of key components of equipment [6][7][8], and other engineering systems. As aero-engines operate in harsh working conditions (high temperature, high load, etc.) repeatedly, the performance of each aero-engine will degrade gradually from the time it is put into use, and such performance degradation will increase the risk of aircraft accidents [9][10][11]. ...
This paper proposes a dynamic health monitoring method for aero-engines by extracting more hidden information from the raw values of gas-path parameters based on slow feature analysis (SFA) and the Gaussian mixture model (GMM) to improve the capability of detecting gas-path faults of aero-engines. First, an SFA algorithm is used to process the raw values of gas-path parameters, extracting the effective features reflecting the slow variation of the gas-path state. Then, a GMM is established based on the slow features of the target aero-engine in a normal state to measure its health status. Moreover, an indicator based on the Bayesian inference distance (BID) is constructed to quantitatively characterize the performance degradation degree of the target aero-engine. Considering that the fixed threshold does not suit the time-varying characteristics of the gas-path state, a dynamic threshold based on the maximum information coefficient is designed for aero-engine health monitoring. The proposed method is verified using a set of actual operation data of a certain aero-engine. The results show that the proposed method can better reflect the degradation process of the aero-engine and identify aero-engine anomalies earlier than other aero-engine fault detection methods. In addition, the dynamic threshold can reduce the occurrence of false alarms. All these advantages give the proposed method high value in real-world applications.
... The control method based on a dynamic model improves the safety and reliability of vehicles under complex working conditions [23]. There are many control methods based on dynamic models, such as Proportional-Integral-Differential (PID) control [24], Fuzzy Logic control [25], Sliding Mode control [26], Model Predictive control [27], and Linear Quadratic Regulator (LQR) control [28]. LQR controllers are widely used in autonomous vehicle path tracking due to their high-precision tracking performance, algorithmic simplicity, applicability to nonlinear systems, and ability to consider dynamic constraints. ...
Path planning and tracking control are essential parts of autonomous vehicle research. Regarding path planning, the Rapid Exploration Random Tree Star (RRT*) algorithm has attracted much attention due to its completeness. However, the algorithm still suffers from slow convergence and high randomness. Regarding path tracking, the Linear Quadratic Regulator (LQR) algorithm is widely used in various control applications due to its efficient stability and ease of implementation. However, the relatively empirical selection of its weight matrix can affect the control effect. This study suggests a path planning and tracking control framework for autonomous vehicles based on an upgraded RRT* and Particle Swarm Optimization Linear Quadratic Regulator (PSO-LQR) to address the abovementioned issues. Firstly, according to the driving characteristics of autonomous vehicles, a variable sampling area is used to limit the generation of random sampling points, significantly reducing the number of iterations. At the same time, an improved Artificial Potential Field (APF) method was introduced into the RRT* algorithm, which improved the convergence speed of the algorithm. Utilizing path pruning based on the maximum steering angle constraint of the vehicle and the cubic B-spline algorithm to achieve path optimization, a continuous curvature path that conforms to the precise tracking of the vehicle was obtained. In addition, optimizing the weight matrix of LQR using POS improved path-tracking accuracy. Finally, this article’s improved RRT* algorithm was simulated and compared with the RRT*, target bias RRT*, and P-RRT*. At the same time, on the Simulink–Carsim joint simulation platform, the PSO-LQR is used to track the planned path at different vehicle speeds. The results show that the improved RRT* algorithm optimizes the path search speed by 34.40% and the iteration number by 33.97%, respectively, and the generated paths are curvature continuous. The tracking accuracy of the PSO-LQR was improved by about 59% compared to LQR, and its stability was higher. The position error and heading error were controlled within 0.06 m and 0.05 rad, respectively, verifying the effectiveness and feasibility of the proposed path planning and tracking control framework.
... Nonlinear control is the dominating approach for trajectory tracking, being used in mobile robots, such as the work of [31] and [32], but also in underwater systems, such as the work of [34], or flying robotics, such as the work of [35]. The aforementioned approaches, start from a basic nonlinear control formulation and extend the control synthesis with either discrete event triggering of state space changes [34], Euclidean distance damping-based adaptation [36], or explicit physics priors [37]. In our study, we introduce a novel con- trol strategy termed ANTIFRAGILE control, which has the benefit of gaining from continuous exposure to uncertainty and reaching performance that is beyond robust. ...
Assistive care robots operate in cluttered, complex environments, akin to human residences, and need to face sensor and actuator faults during their operation without compromising safety. In these situations, the system must foresee and provide reactions that are beyond robust in addition to compensating for uncertainty and volatility in its functioning. We propose a new control design framework based on the principles of antifragility. In this work, we provide a formal concept of antifragile control and outline the design procedures for creating a mobile robot trajectory-tracking antifragile controller. An extended comparative evaluation against other controllers and a methodical investigation of the performance under parametrizable uncertainty and errors is also provided. Our findings demonstrate the effectiveness of antifragile closed-loop control in volatile and unanticipated circumstances.
Achieving accurate path tracking and vehicle stability control for four-wheel independent drive electric trucks under complex driving conditions, such as high speed and low adhesion, remains a major challenge in current research. Poor coordination control may cause the vehicle to deviate from its intended path and become unstable. To address this issue, this article proposes a coordinated control strategy consisting of a three-layer control framework. In the upper layer controller design, establish a linear quadratic regulator (LQR) path tracking controller to ensure precise steering control by eliminating steady-state errors through feedforward control. The middle layer controller utilizes the fractional order sliding mode control (FOSMC) yaw moment controller to calculate the additional yaw moment based on the steering angle of the upper input, utilizing the error of yaw rate and sideslip angle as the state variable. To collectively optimize the control system, establish a coordinated optimization objective function and utilize the hybrid genetic-particle swarm optimization algorithm (GA-PSO) to optimize the weight coefficient of LQR and sliding mode parameters of FOSMC, effectively improving the performance of the controller. In the lower layer torque distribution controller, use the quadratic programming method to achieve real-time optimal torque distribution based on tire utilization, which improves vehicle stability control. Through simulations conducted under four different working conditions, the proposed control scheme demonstrates a 15.54% to 23.17% improvement in tracking performance and a 10.83% to 23.88% optimization in vehicle driving stability compared to other control methods. This scheme provides a theoretical reference for path tracking and stability control in four-wheel independent drive electric trucks.
