Steering Control Research Articles

Manual steering and robust adaptive constrained control of ship autopilot

This study proposes two schemes for ship autopilot: the first is a follow-up mode, applying manual dynamics to the state-space-based underactuated large merchant ship (ULMS) model, using a steering wheel and joystick to emulate manual operation; the second is a Nav mode developed by path-following control. In this mode, a novel path-following control strategy is developed for ULMS with input saturation and delay, two novel auxiliary systems are employed to stabilize the plant with input constraints. The input delay auxiliary system (IDAS) effectively tackles the design challenges posed by input delay, while the input saturation auxiliary system (ISAS) is dedicated to generating actual control inputs under the constraint of saturation. Furthermore, the dynamic surface control (DSC) technique and the roust neural damping technique are introduced to mitigate the computational burden and simplify the controller structure, thereby enhancing the efficiency and performance of the entire control system. Lyapunov stability analysis proves the ship motion system is semi-globally uniformly ultimately bounded (SGUUB). Finally, the effectiveness of these two schemes is verified through turning experiment, comparative simulations and a semi-physical simulation platform that integrates physical models with computer simulation technology.

Redundancy Control Strategy for a Dual-Redundancy Steer-by-Wire System

Currently, key factors hindering application of steer-by-wire systems are their inadequate safety and reliability, which are significant criteria for evaluating automotive active safety. Based on the steer-by-wire platform, a dual-redundant steering motor control system is proposed, featuring dual three-phase permanent magnet synchronous motors as execution motors, achieving redundancy from hardware. A torque vector-space-decoupling control method is introduced for these motors to ensure balanced and stable torque output. Upon a fault, fault-tolerant measures are taken by disconnecting power supply to the affected motor, which, despite reducing system functionality, allows for normal steering control. This research starts with modeling the dual three-phase motors to construct a simulation model. It then proceeds with hardware-in-the-loop testing integrated with the dual-redundancy steer-by-wire control system, conducting tests under dual-lane-change trajectory conditions. Finally, a steering system fault is simulated to assess fault handling and functional degradation. These experiments confirmed that the proposed method enabled balanced torque output from the dual three-phase motors in the redundant steering control and facilitated fault-tolerant processing post fault, ensuring the vehicle’s steering functions were maintained.

Coordinated Control of Differential Drive-Assist Steering and Direct Yaw Moment Control for Distributed-Drive Electric Vehicles

Direct yaw moment control (DYC) and differential drive-assist steering (DDAS) for distributed-drive vehicles are both realized by allocating the in-wheel motor torque. To address the interference caused by overlapping control objectives, this paper proposes a multilayer control strategy that integrates DYC and DDAS, consisting of an upper controller, a coordinated decision layer, and a torque distribution layer. The upper controller, designed based on the vehicle’s dynamic characteristics, incorporates an adaptive fuzzy control DYC system and a dual PID control DDAS system. The coordinated decision layer is developed utilizing a phase-plane dynamic weighting method, delineating region boundaries by applying the double-line and limit cycle methods. The torque distribution strategy is formulated considering motor peak torque and road adhesion conditions. Multi-condition joint simulation experiments indicate that the proposed multilayer control strategy, integrating the advantages of DYC and DDAS, reduces peak steering wheel torque by approximately 10%, peak yaw rate by around 25%, peak sideslip angle by roughly 29%, and peak sideslip angle rate by about 19%, significantly improving driving stability and maneuvering flexibility.

Development of a Robotic Platform with Autonomous Navigation System for Agriculture

The development of autonomous agricultural robots using a global navigation satellite system aided by real-time kinematics and an inertial measurement unit for position and orientation determination must address the accuracy, reliability, and cost of these components. This study aims to develop and evaluate a robotic platform with autonomous navigation using low-cost components. A navigation algorithm was developed based on the kinematics of a differential vehicle, combined with a proportional and integral steering controller that followed a point-to-point route until the desired route was completed. Two route mapping methods were tested. The performance of the platform control algorithm was evaluated by following a predefined route and calculating metrics such as the maximum cross-track error, mean absolute error, standard deviation of the error, and root mean squared error. The strategy of planning routes with closer waypoints reduces cross-track errors. The results showed that when adopting waypoints every 3 m, better performance was obtained compared to waypoints only at the vertices, with maximum cross-track error being 44.4% lower, MAE 64.1% lower, SD 39.4% lower, and RMSE 52.5% lower. This study demonstrates the feasibility of developing autonomous agricultural robots with low-cost components and highlights the importance of careful route planning to optimize navigation accuracy.

Research on Active Trailer Steering Control Strategy of Tractor Semitrailer under Medium-/High-Speed Conditions

Abstract: The study proposes an active trailer steering control method for tractor semitrailers to promote the path tracking effect of the trailer portion as well as lateral stability during lane changing. Firstly, a simplified model of a tractor semitrailer is constructed, and the MAP map is formed based on the genetic algorithm for the identification of the key parameters, which improves the model’s accuracy. Then the tractor and trailer’s yaw rate and sideslip angle at CG are tracked as the control objective and the trailer angle distribution strategy is given. Then the LQR-based corner controller is designed to control the steering actuators of each axle of the trailer. Finally, the effectiveness of the designed control strategy is verified based on the Trucksim/Simulink joint simulation platform and the semi-physical HiL test platform. The simulation results show that the designed controller can effectively improve the path tracking effect of the tractor and the trailer, and at the same time, the lateral stability parameters of the tractor and the trailer are also significantly improved, which improves the driving stability of the tractor semitrailer.

261 Vitamin A administration in pregnant cows or newborn calves: Growth performance and carcass traits

Abstract This study aimed to evaluate the effects of Vitamin A (VA) injection on the growth performance and carcass traits of Nellore × Angus crossbred steers and heifers. For that, the same sire inseminated Nellore cows from two artificial insemination (AI) groups were randomly assigned to one of three treatments at 250 d of gestation: no VA injection (Control); VA injection in pregnant cows at 250 d of gestation (VAcow; 2,000,000 IU single dose); VA injection in newborn calves at birth and 60 d of age (VAcalf; 200,000 IU each dose). Cow-calf pairs were kept in the same paddock, and calves received creep-feeding supplementation from 90 d of age to weaning. After weaning, steers and heifers [n = 36; body weight (BW) 213 ± 25.7 kg] were stratified by treatment and BW and allotted into 16 pens. After 207 d of feeding (77 d of growing diet + 130 d of finishing diet), steers and heifers were slaughtered at a commercial abattoir. Data were analyzed using the MIXED procedure of SAS 9.4 with fixed effects of VA injection, sex, and VA injection*sex interaction. The random effects were animal, AI group, and cow parity. Average daily gain before weaning (P < 0.01) and final BW in the finishing phase were greater (P = 0.04) in VAcow steers compared with heifers from all treatments (Table 1). Vitamin A had a positive impact on weaning BW of male calves and a negative impact on heifers (P < 0.01). There was no treatment difference (P = 0.35) in feedlot average daily gain (ADG), but steers had greater (P = 0.03) ADG compared with heifers. Moreover, VAcow steers had greater dry matter intake (DMI) in the feedlot (P = 0.04) compared with other treatments, and there was no effect of VA on DMI of heifers. On the other hand, feedlot feed efficiency tended to be greater (P = 0.07) in Control steers compared with other treatments, with no VA effect on heifers too. The ribeye area was greater (P = 0.04) in VAcow steers than in Control steers and heifers from all treatments. Intramuscular fat content was greater (P = 0.04) in VAcow steers than in Control and VAcalf steers. However, there was no effect of VA on heifers and VAcow steers. In addition, VAcow treatment tended (P = 0.08) to increase backfat thickness. In conclusion, VA injection in pregnant cows can be a strategy to improve marbling in steers, but there is no effect on intramuscular fat in heifers. Additionally, VA injection in pregnant cows produced intramuscular content in beef of steers, similar to heifers.

Transfer Learning-Based Steering Angle Prediction and Control with Fuzzy Signatures-Enhanced Fuzzy Systems for Autonomous Vehicles

This research introduces an innovative approach for End-to-End steering angle prediction and its control in electric power steering (EPS) systems. The methodology integrates transfer learning-based computer vision techniques for prediction and control with fuzzy signatures-enhanced fuzzy systems. Fuzzy signatures are unique multidimensional data structures that represent data symbolically. This enhancement enables the fuzzy systems to effectively manage the inherent imprecision and uncertainty in various driving scenarios. The ultimate goal of this work is to assess the efficiency and performance of this combined approach by highlighting the pivotal role of steering angle prediction and control in the field of autonomous driving systems. Specifically, within EPS systems, the control of the motor directly influences the vehicle’s path and maneuverability. A significant breakthrough of this study is the successful application of transfer learning-based computer vision techniques to extract respective visual data without the need for large datasets. This represents an advancement in reducing the extensive data collection and computational load typically required. The findings of this research reveal the potential of this approach within EPS systems, with an MSE score of 0.0386 against 0.0476, by outperforming the existing NVIDIA model. This result provides a 22.63% better Mean Squared Error (MSE) score than NVIDIA’s model. The proposed model also showed better performance compared with all other three references found in the literature. Furthermore, we identify potential areas for refinement, such as decreasing model loss and simplifying the complex decision model of fuzzy systems, which can represent the symmetry and asymmetry of human decision-making systems. This study, therefore, contributes significantly to the ongoing evolution of autonomous driving systems.

Design of adaptive barrier function-based backstepping finite time guidance control for interceptor-target systems

This study introduces an optimal finite-time backstepping sliding mode guidance law, leveraging an adaptive continuous barrier function to examine the interceptor-target system’s motion in cylindrical coordinates. The integration of sliding mode and backstepping controllers enhances the robustness and performance of the proposed guidance method amidst external disturbances and model uncertainties. This combination also improves transient response, reduces chattering, and lowers control efforts. The adaptive continuous barrier function employed in this method eliminates the need for prior knowledge of the upper bounds of uncertainties and disturbances. Additionally, it ensures that adaptation gains are not overestimated, leading to more accurate performance and complete elimination of chattering. The Lyapunov stability theory is used to prove the finite-time convergence of the state trajectories of the interceptor-target system to a predefined neighborhood around the origin, despite external disturbances and uncertainties. A genetic optimization algorithm is utilized to optimally select the parameters of the guidance method. The efficacy of the proposed technique is validated through simulation results and real-time experiments on the Baseline Speedgoat Real-Time Target Machine platform. The method’s effectiveness and performance are further demonstrated through analysis and simulation in the MATLAB/Simulink environment.

Research on a Novel Citrus Reticulata ‘Chachi’ Orientation Adjustment Mechanism (COAM) and Machine Vision Guidance Control

The initial processing of Citrus Reticulata ‘Chachi’ involves peeling as a crucial step. Currently, there is some semi-automatic peeling equipment available. However, due to the requirement of adjusting the orientation of Citrus Reticulata ‘Chachi’ to ensure the stem (or navel) is facing upwards before peeling and because the peeling process must retain the stem as a marker for fresh fruit picking, the loading of Citrus Reticulata ‘Chachi’ for peeling still solely relies on manual operation, resulting in low efficiency and poor standardization. With the rapid growth of the pericarp of the Citrus Reticulata ‘Chachi’ industry, semi-automatic processing equipment is no longer able to meet production demands. The loading issue before peeling Citrus Reticulata ‘Chachi’ is a complex hand–eye coordination problem. In response to this issue, this paper proposes a novel Citrus Reticulata ‘Chachi’ orientation adjustment mechanism (COAM). This mechanism utilizes frictional force to adjust the orientation of Citrus Reticulata ‘Chachi’. First, the conceptual design and kinematic modelling analysis of the mechanism were conducted. Next, the omnidirectional friction-driven wheels were optimized in design. Subsequently, a prototype was manufactured and assembled to conduct validation tests on its open-loop motion performance. Finally, a visual feedback-guided algorithm was introduced to complement the kinematic model, enabling the automatic and rapid adjustment of Citrus Reticulata ‘Chachi’ orientation. The experimental results indicate that the COAM designed in this study can effectively and rapidly adjust the orientation of Citrus Reticulata ‘Chachi’ fruits of different sizes and shapes. It demonstrates strong adaptability, and under visual feedback guidance, the orientation adjustment error is less than 10% of the fruit’s diameter. This meets the requirements for automated production in the initial processing of Citrus Reticulata ‘Chachi’. The research presented in this paper also provides new insights for the orientation adjustment and loading of similar spherical fruits.

Fine-grained descending control of steering in walking Drosophila

Locomotion involves rhythmic limb movement patterns that originate in circuits outside the brain. Purposeful locomotion requires descending commands from the brain, but we do not understand how these commands are structured. Here, we investigate this issue, focusing on the control of steering in walking Drosophila. First, we describe different limb "gestures" associated with different steering maneuvers. Next, we identify a set of descending neurons whose activity predicts steering. Focusing on two descending cell types downstream of distinct brain networks, we show that they evoke specific limb gestures: one lengthens strides on the outside of a turn, while the other attenuates strides on the inside of a turn. Our results suggest that a single descending neuron can have opposite effects during different locomotor rhythm phases, and we identify networks positioned to implement this phase-specific gating. Together, our results show how purposeful locomotion emerges from specific, coordinated modulations of low-level patterns.

A Robust Cooperative Game Theory-Based Human–Machine Shared Steering Control Framework

A Robust Cooperative Game Theory-Based Human–Machine Shared Steering Control Framework

Inverse-Kinematics Steering Laws for Double-Gimbal Scissored-Pair Control Moment Gyros

The generalized singularity robust inverse steering law (GSRISL) is widely used to avoid singularity problems in control moment gyroscopes (CMGs). However, GSRISL tends to generate perturbation torques near singularities, which hinder highly accurate attitude tracking control. Model predictive steering control can eliminate the necessity of considering singularities, but its implementation in satellites remains challenging because of its high computational cost. Therefore, the development of a steering law that avoids explicit consideration of internal singularities and costly calculations is critical. The inverse-kinematics steering law (IKSL) is one such law removing the need for the calculation of the inverse matrix. The double-gimbal scissored-pair CMG (DGSPCMG) addresses the singularity problem as it does not have internal singularities except along the outer gimbal axis. To enhance the attitude control by removing the need for the calculation of the inverse matrix, this study proposes the combining of IKSL with DGSPCMG. Additionally, to mitigate the risk of gimbal mechanical failure, the IKSL for inner or outer gimbal axes failure was designed by relaxing the identical steering constraint of the scissored-pair gimbal angles. Numerical simulations were performed to evaluate the effectiveness of the combination of IKSL and DGSPCMG in terms of settling time and maneuvering axis accuracy.

ALL-OPTICAL LOGIC GATE OPERATION WITH A MULTI-CORE NONLINEAR PHOTONIC CRYSTAL FIBER

In this paper, we propose the design of a photonic crystal fiber with four central cores infiltrated by a high-index liquid to achieve highly efficient control of light guidance. We analyze the field distribution, effective mode area, and dispersion characteristics of the fundamental guided modes of the fiber. Within the coupled-mode theory, the pulse propagation in the fiber is governed by coupled nonlinear Schrödinger equations. We use the split-step Fourier method to simulate the propagation of pulses numerically. The results show three features of the dynamics: oscillation, switching, and self-trapping. We predict that the fiber could operate as a logic-gate device by introducing suitable input and control signals.

Nonlinear steering control law under input magnitude and rate constraints with exponential convergence

AbstractA ship steering control law is designed for a nonlinear maneuvering model whose rudder manipulation is constrained in both magnitude and rate. In our method, the tracking problem of the target heading angle with input constraints is converted into the tracking problem for a strict-feedback system without any input constraints. To derive this system, hyperbolic tangent ($$\tanh$$ tanh ) function and auxiliary variables are introduced to deal with the input constraints. Furthermore, using the feature of the derivative of $$\tanh$$ tanh function, auxiliary systems are successfully derived in the strict-feedback form. The backstepping method is utilized to construct the control input for the resulting cascade system. The proposed steering control law is verified in numerical experiments, and the result shows that the tracking of the target heading angle is successful using the proposed control law.

自动驾驶拖拉机电液耦合转向特性研究

To solve the problem of poor steering performance of existing self-driving tractors based on electro-hydraulic coupled steering systems (E-HCSS) under multiple influencing factors, the research on electro-hydraulic coupled steering characteristics of self-driving tractors was carried out in this paper. Taking the electro-hydraulic coupled steering system as the research object, the E-HCSS test bench of the tractor was built, and the influencing factors affecting the responsiveness of the steering process were obtained through theoretical analysis: hydraulic fluid temperature, oil supply pressure and driving speed. The hydraulic fluid temperature, oil supply pressure and driving speed were taken as the test factors, and the steering system response time and response error were taken as the performance indexes for the single-factor steering test and orthogonal test. By establishing the regression mathematical model between the influencing factors and the indexes, the interactive influence of the factors on the indexes was analyzed, the optimal parameter combination was obtained, and the optimization results were verified. The test results indicated that the tractor electro-hydraulic coupling steering system could achieve good steering performance under the optimal parameter combination, and the optimal parameter combination was: hydraulic oil temperature 60°C, hydraulic oil pressure 15 MPa and driving speed 8 km/h. The study's results were as follows: hydraulic oil temperature 60°C, hydraulic oil pressure 15 MPa and driving speed 8 km/h. The study's results could provide a reference for the steering control of the self-driving tractor, the design of the self-driving steering system and the optimization of the parameters.

Four-wheel steering vehicle trajectory tracking control based on PSO optimized MPC

In order to address the difficulty induced by controller parameter uncertainty in trajectory tracking control of four-wheel steering vehicles(4WS), a trajectory tracking control method for unmanned vehicles based on particle swarm optimization (PSO) is proposed to improve the robustness of the controller. The approach involves the use of model predictive control (MPC) for implementing trajectory tracking control for the unmanned vehicle. Iterative optimization is conducted by utilizing the integral time absolute error (ITAE) as the objective function, which involves multiplying the time integral of lateral deviation and yaw rate deviation. This process ultimately determines the optimized MPC weight matrix parameters. Co-simulation using CarSim/Simulink reveals a remarkable reduction of 46.1% in the maximum longitudinal error, and the optimization proves effective across various vehicle speed conditions. Experimental results validate the effectiveness of the proposed control strategy, with the 4WS control strategy yielding a maximum longitudinal error of 0.28 meters, affirming that the overall controller design successfully accomplishes its intended objectives.

Particle swarm optimization based neural network automatic controller for stability steering control of four-wheel drive electric vehicle

Particle swarm optimization based neural network automatic controller for stability steering control of four-wheel drive electric vehicle

A text guided cross modal joint inpainting algorithm for ancient murals

Existing deep learning algorithms for ancient mural inpainting only consider prior information of mural images to guide the inpainting of damaged areas, lacking guidance from textual semantic information, which often leads to problems such as inconsistent semantics and disordered textures in the inpainting results. In this work, we propose a text guided cross modal joint inpainting algorithm for ancient murals. Firstly, a cross modal joint inpainting network for ancient murals was constructed, which introduced text semantic information as the control guidance for mural inpainting, providing contextual information for mural image inpainting and guiding the inpainting of damaged areas. Then, design a text encoder and an image encoder to extract text and image features respectively, including word and sentence features. Input the sentence features into the conditional enhancement module, resample the text sentences, and solve the problem of text sparsity. Secondly, design a text decoupling module that utilizes a multimodal attention mechanism to extract the semantic information of the missing parts of damaged murals, and design a text semantic enhancement module to improve the semantic representation ability of the text, achieve the repair of local details of murals, and solve the problem of texture disorder. Finally, the reconstruction and repair of murals are completed through a residual discriminator game and a spectral normalized Markov dual discriminator against the mural generation network. Through inpainting experiments on real Dunhuang murals, it has been shown that the proposed method is more effective than comparative methods in repairing damaged murals, achieving better visual perception and coordination in reconstruction, and outperforming comparative algorithms in both subjective and objective evaluations.

Impact of Intersection Left Turn Guide Lines Configuration on Novice Drivers’ Behavior

Novice drivers often face challenges such as misjudgment, inappropriate steering control, distraction, and insufficient speed control when making left turns at intersections, leading to safety hazards. Installing intersection guide lines offers a solution by providing clear path directions, mitigating safety concerns associated with novice drivers’ left-turn actions. This study explored the impact of intersection guide line configurations on the driving behavior of novice drivers during left turns, utilizing large, medium, and small typical intersections to create six categories of left-turn simulation scenarios in a driving simulator. Data on vehicle trajectory, steering angle, steering speed, and eye-tracking were collected and analyzed. The study revealed that guide line arrangement significantly influences novice drivers’ left-turning behavior, enhancing path guidance while reducing trajectory and steering angle fluctuations, speed variations, and driver attention dispersion. This improvement in stability is particularly notable as intersection size and the number of left-turn lanes increase. The study’s findings offer robust theoretical support and guidance for the development and widespread adoption of intersection guide lines.

Design, Control, and Clinical Applications of Magnetic Actuation Systems: Challenges and Opportunities

The enhancement of medical services relies significantly on engineering research. Magnetic actuation systems (MASs) are designed to be safe for biomedical applications and offer long‐distance and dynamic control capabilities, rendering them highly favorable for clinical applications. The integration of MASs with sophisticated perception and positioning methods enhances their suitability for clinical applications. Achieving this goal hinges on the development of MASs and their control methods; thus, a comprehensive review of current technical and scientific issues is indispensable for addressing the related challenges. This review encompasses both the classical and state‐of‐the‐art research, providing readers with a thorough understanding of the intricacies involved in MAS design, system control methods, clinical applications, and guidance toward the next phase of exploration. In addition, a detailed illustration of the underlying principles of the magnetic actuation method is provided, which helps delineate the design principles. A detailed analysis of potential clinical applications in hard‐to‐reach environments inside the human body provides guidance for interdisciplinary researchers. Challenges and opportunities associated with MAS design and application are summarized to inspire researchers and stimulate innovative explorations. Finally, this review concludes that sustained research in this field has the potential to yield groundbreaking advancements capable of revolutionizing modern medicine.