Steering Control Research Articles

Research and Flight Test on the Terminal Guidance Control Technology for Cruising Unmanned Aerial Vehicles

The Cruising Unmanned Aerial Vehicle (CUAV) exemplifies an advanced system integrating UAV and munitions technology. The primary challenge lies in devising strategies to achieve precision strikes. Achieving precision strikes is a critical combat mission for CUAV and serves as the primary focus of this research. This paper introduces a three-loop pseudo-angle-of-attack acceleration autopilot design with proportional–integral (PI) correction, overcoming the limitations of existing two-loop and three-loop systems. The existing two-loop acceleration autopilot with PI correction suffers from low robustness, whereas the three-loop autopilot exhibits slow response to acceleration deviations. The proposed design overcomes these shortcomings by optimizing the autopilot for high-dynamic and fast-response scenarios. The design methodology leverages pole assignment, referencing the pole co-circle method and accommodating servo bandwidth limitations. A comparison of the designed three-loop acceleration autopilot with PI correction with other acceleration autopilots reveals superior accuracy advantages. The reliability and robustness of the proposed autopilot were validated through flight tests, achieving a drop point accuracy of less than 0.5 m. This study demonstrates the engineering application of a pseudo-angle-of-attack three-loop acceleration autopilot with PI correction, designed using the pole assignment method, on a short-range CUAV. Future efforts will focus on optimizing the autopilot to further enhance its adaptability and robustness.

Design and Testing of a Tractor Automatic Navigation System Based on Dynamic Path Search and a Fuzzy Stanley Model

Design and Testing of a Tractor Automatic Navigation System Based on Dynamic Path Search and a Fuzzy Stanley Model

Generalized Time-to-Go Inversion Guidance with Impact Time and Angle Constraints

Generalized Time-to-Go Inversion Guidance with Impact Time and Angle Constraints

Research on Control Strategy of Distributed Articulated Robot Based on Vehicle-arm Coordination

In order to improve the steering ability of articulated mobile robot, an articulated robot equipped with encoder telescopic rod and distributed electric wheel is designed. The rationality of the structure is verified by finite element simulation. The dynamic modeling of the robot is carried out by Newton-Euler method, and a steering control strategy is proposed. Based on Adams / Simulink joint simulation, the steering control strategy model is built and the simulation experiment is carried out. The motion of the articulated mobile robot under various working conditions is analyzed. The lateral displacement of the rear body is greatly reduced, which effectively avoids the occurrence of ' tail flick '. The physical experiment shows that the turning radius and turning time of the articulated mobile robot under the steering control strategy are better, which can effectively improve the steering ability of the robot.

Research on Cooperative Driving Steering Control for Intelligent Vehicles Based on Lateral Deviation Prediction

Research on Cooperative Driving Steering Control for Intelligent Vehicles Based on Lateral Deviation Prediction

A Fuzzy-Immune-Regulated Single-Neuron Proportional–Integral–Derivative Control System for Robust Trajectory Tracking in a Lawn-Mowing Robot

This paper presents the constitution of a computationally intelligent self-adaptive steering controller for a lawn-mowing robot to yield robust trajectory tracking and disturbance rejection behavior. The conventional fixed-gain proportional–integral–derivative (PID) control procedure lacks the flexibility to deal with the environmental indeterminacies, coupling issues, and intrinsic nonlinear dynamics associated with the aforementioned nonholonomic system. Hence, this article contributes to formulating a self-adaptive single-neuron PID control system that is driven by an extended Kalman filter (EKF) to ensure efficient learning and faster convergence speeds. The neural adaptive PID control formulation improves the controller’s design flexibility, which allows it to effectively attenuate the tracking errors and improve the system’s trajectory tracking accuracy. To supplement the controller’s robustness to exogenous disturbances, the adaptive PID control signal is modulated with an auxiliary fuzzy-immune system. The fuzzy-immune system imitates the automatic self-learning and self-tuning characteristics of the biological immune system to suppress bounded disturbances and parametric variations. The propositions above are verified by performing the tailored hardware in the loop experiments on a differentially driven lawn-mowing robot. The results of these experiments confirm the enhanced trajectory tracking precision and disturbance compensation ability of the prescribed control method.

Modelling, Simulation and Testing of Steer-by-Wire System with Variable Steering Ratio Control Strategy in 14-DOF Full Vehicle Model

This research explores the application of Proportional-Integral-Derivative (PID) controllers and Variable Steering Ratio (VSR) control strategies within a Steer-by-Wire (SbW) system, utilizing a comprehensive 14 Degrees of Freedom (DOF) full vehicle dynamic model. The study aims to advance vehicle dynamics and control through SbW technology. This research addresses the challenge of optimizing steering control by integrating an SbW system with PID and VSR strategies to overcome the limitations of traditional steering mechanisms and enhance vehicle responsiveness and stability. The SbW system, using a PID-based controller, was rigorously tested and validated using MATLAB Simulink and Hardware-in-the-Loop System (HiLS) method. The performance of the system was assessed through four distinct signal tests: step, sine, square, and sawtooth inputs. These tests confirmed the precision of the PID controller in position tracking. To improve the practical applicability of the SbW system, a 14-DOF vehicle dynamic model was developed and validated before integration with the SbW system. The integrated system accurately replicated conventional steering behavior, as validated by Step Steer Input (SSI) and Double Lane Change (DLC) tests, demonstrating less than 11% RMS percentage error - well within the acceptable threshold of less than 15%. Additionally, the study proposed and validated VSR control strategies through the DLC test. Results indicate that the PID controller effectively tracked desired signals, while the VSR strategy reduced driver workload and improved steering stability. This research offers valuable insights into enhancing SbW system performance and lays a foundation for future advancements in vehicle control systems.

Integrating an Ethics Advisory Committee Into Public Health Response: A Case Study of COVID-19, Infection Prevention and Control, and Essential Work in the United States.

A lack of infection prevention and control protections for essential industries in the United States led to increased risk and incidence of COVID-19 among essential workers during the COVID-19 pandemic. When the nation deems an industry essential during a disease outbreak, an ethical obligation exists to safeguard the health of workers who are at increased risk of being exposed to disease. The Global Center for Health Security at the University of Nebraska Medical Center began work to rapidly develop and disseminate infection prevention and control guidance for essential industries, such as meat processing. The Global Center for Health Security established an ethics advisory committee to support COVID-19 response efforts. The ethics advisory committee supported the development of guidance on infection prevention and control to promote justice, reciprocity, health, safety, and equity for workers in the meat processing industry. Our experience highlighted the fundamental role of ethical analysis in public health response efforts, but ethical analysis in this case required an interdisciplinary approach, including the need for effective community-relevant solutions. The integration of an ethics committee into public health emergency response efforts can address ethical concerns for workers in industries that must remain operational during public health emergencies.

A Control Method for Path Following of AUVs Considering Multiple Factors Under Ocean Currents

To improve the path-following performance of autonomous underwater vehicles (AUVs) under ocean currents, a control method based on line-of-sight with fuzzy controller (FLOS) guidance and the fuzzy sliding mode controller (FSMC) is proposed. This method considers multiple factors affecting guidance and adaptively determines the optimal heading angle through the fuzzy controller to enhance guidance capability. Additionally, a novel FSMC based on Lyapunov stability theory is designed to suppress the influence of model uncertainty and external disturbances on the control system. Simulations and experiments of the proposed control method demonstrate that it can maintain precise tracking under disturbances, improving path-following performance metrics by more than 15%.

Time‐Varying Sliding Mode Based Fixed‐Time Missile Guidance and Attitude Control With Impact Angle Constraints

Time‐Varying Sliding Mode Based Fixed‐Time Missile Guidance and Attitude Control With Impact Angle Constraints

Coupling of Advanced Guidance and Robust Control for the Descent and Precise Landing of Reusable Launchers

This paper investigates the coupling of successive convex optimization guidance with robust structured H∞ control for the descent and precise landing of Reusable Launch Vehicles (RLVs). More particularly, this Guidance and Control (G&C) system is foreseen to be integrated into a nonlinear six-degree-of-freedom RLV controlled dynamics simulator which covers the aerodynamic and powered descent phase until vertical landing of a first-stage rocket equipped with a thrust vector control system and steerable planar fins. A cost function strategy analysis is performed to find out the most efficient one to be implemented in closed-loop with the robust control system and the vehicle flight mechanics involved. In addition, the controller synthesis via structured H∞ is thoroughly described. The latter are built at different points of the descent trajectory using Proportional-Integral-Derivative (PID)-like structures with feedback on the attitude angles, rates, and lateral body velocities. The architecture is verified through linear analyses as well as nonlinear cases with the aforementioned simulator, and the G&C approach is validated by comparing the performance and robustness with a baseline system in nominal conditions as well as in the presence of perturbations. The overall results show that the proposed G&C system represents a relevant candidate for realistic descent flight and precise landing phase for reusable launchers.

Path Tracking Control for Four-Wheel Independent Steering and Driving Vehicles Based on Improved Deep Reinforcement Learning

We propose a compound control framework to improve the path tracking accuracy of a four-wheel independent steering and driving (4WISD) vehicle in complex environments. The framework consists of a deep reinforcement learning (DRL)-based auxiliary controller and a dual-layer controller. Samples in the 4WISD vehicle control framework have the issues of skewness and sparsity, which makes it difficult for the DRL to converge. We propose a group intelligent experience replay (GER) mechanism that non-dominantly sorts the samples in the experience buffer, which facilitates within-group and between-group collaboration to achieve a balance between exploration and exploitation. To address the generalization problem in the complex nonlinear dynamics of 4WISD vehicles, we propose an actor-critic architecture based on the method of two-stream information bottleneck (TIB). The TIB method is used to remove redundant information and extract high-dimensional features from the samples, thereby reducing generalization errors. To alleviate the overfitting of DRL to known data caused by IB, the reverse information bottleneck (RIB) alters the optimization objective of IB, preserving the discriminative features that are highly correlated with actions and improving the generalization ability of DRL. The proposed method significantly improves the convergence and generalization capabilities of DRL, while effectively enhancing the path tracking accuracy of 4WISD vehicles in high-speed, large-curvature, and complex environments.

Evaluation of Guanidinoacetic Acid Supplementation on Finishing Beef Steer Growth Performance, Skeletal Muscle Cellular Response, and Carcass Characteristics.

This study elucidated the effects of dosage-dependent guanidinoacetic acid (GAA) supplementation on growth performance, muscle responses, and carcass characteristics in finishing beef steers. Thirty crossbred Red Angus beef steers (395 ± 28.09kg) were randomly assigned one of three treatments during a 146-day feedlot study: basal diet without GAA supplementation (CONTROL), 1g of GAA per 100kg of BW daily (LOWGAA), and 2g of GAA per 100kg of BW daily (HIGHGAA). Individual feed intake was monitored daily, growth performance parameters were collected every 28 days, and longissimus muscle (LM) biopsies occurred every 56 days. In biopsied LM, greater (P = 0.048) mRNA expression of IGF-1 was observed in LOWGAA steers on d 112 compared to the CONTROL group. LOWGAA steers also exhibited greater expression of myosin heavy chain (MHC) I compared to CONTROL steers (P < 0.05) and MHC IIA compared to both CONTROL and HIGHGAA treatment groups (P < 0.01) on d 112. GAA supplementation resulted in no change in carcass characteristics, serum and LM tissue metabolites, LM composition, and Warner-Bratzler shear force (WBSF) values (P > 0.05). Data collected from this study demonstrate the influence of GAA supplementation on the gene expression of MHC isoforms and their role in skeletal muscle growth, differentiation, and muscle fiber-typing.

Integrated Intelligent Guidance and Motion Control of USVs With Anticipatory Collision Avoidance Decision-Making

Integrated Intelligent Guidance and Motion Control of USVs With Anticipatory Collision Avoidance Decision-Making

Human-Vehicle Shared Steering Control for Obstacle Avoidance: A Reference-Free Approach With Reinforcement Learning

Human-Vehicle Shared Steering Control for Obstacle Avoidance: A Reference-Free Approach With Reinforcement Learning

Continuous integration of heading and goal directions guides steering.

Navigating animals must integrate a diverse array of sensory cues into a single locomotor decision. Insects perform intricate navigational feats using a brain region termed the central complex in which an animal's heading direction is transformed through several layers of circuitry to elicit goal-directed locomotion. These transformations occur mostly in the fan-shaped body (FB), a major locus of multi-sensory integration in the central complex. Key aspects of these sensorimotor computations have been extensively characterized by functional studies, leveraging the genetic tools available in the fruit fly. However, our understanding of how neuronal activity in the FB dictates locomotor behaviors during navigation remains enigmatic. Here, we manipulate the activity of two key neuronal populations that input into the FB-the PFNa and PFNd neurons-used to encode the direction of two complex navigational cues: wind plumes and optic flow, respectively. We find that flies presented with unidirectional optic flow steer along curved walking trajectories, but silencing PFNd neurons abolishes this curvature. We next use optogenetic activation to introduce a fictive heading signal in the PFNs to establish the causal relationship between their activity and steering behavior. Our studies reveal that the central complex guides locomotion by summing the PFN-borne directional signals and shifting movement trajectories left or right accordingly. Based on these results, we propose a model of central complex-mediated locomotion wherein the fly achieves fine-grained control of sensory-guided steering by continuously integrating its heading and goal directions over time.

Dynamic Self‐Triggered Parallel Control of Guidance Intercept Systems With Constrained Inputs via Adaptive Dynamic Programming

ABSTRACTThis paper introduces an anti‐saturation optimization algorithm based on dynamic self‐triggered adaptive dynamic programming (ADP) for bounded acceleration guidance interception. By establishing a nonlinear input‐constrained guidance intercept and control system, the smooth bounded function is used to constrain the system input to ensure that the system operates in a controllable range. Subsequently, the appropriate performance function that can accurately reflect the system is designed. In alignment with the advantages of parallel control and ADP, a group of parallel systems are constructed by modeling the derivation of control input. A self‐learning control framework is explored, facilitating virtual‐actual interaction and mutual reinforcement between multiple controllers, optimizing the management of the interception system. Furthermore, event‐triggered control (ETC) policies are devised, which can be updated only when needed by setting trigger conditions, thus saving data resources. The stability proof of the closed‐loop system is given to ensure that the system can keep stable operation when the trigger condition is satisfied. On this basis, a dynamic self‐triggered control (DSTC) with soft computing is put forward, enabling trigger instant calculation without real‐time system monitoring and further extending the trigger interval. Simulation results evidence that the devised guidance scheme can intercept the maneuvering target at a reduced expenditure of communication resources.

Strange nonchaotic attractors and bifurcation of a four-wheel-steering vehicle system with driver steering control

In this paper, a dynamical model is developed for the four-wheel-steering(4WS)vehicles, with nonlinear lateral tyre forces and quasi-periodic disturbances from the steering mechanism and quasi-periodic pavement external deformation. Initially, the stability and Hopf bifurcation of the 4WS vehicle system without disturbance are analyzed employing the central manifold theory and projection method. The effects of parameters on the stability and the types of Hopf bifurcation for the vehicle system are also discussed. It is shown that both Hopf bifurcation and degenerate Hopf bifurcation occur in the vehicle system. The critical speed decreases with increased driver’s perceptual time delay and distance from the vehicle’s center of gravity to the front axles, while it increases with the control strategy coefficient. However, the increase of the frontal visibility distance of the driver leads to an initial increase of the critical speed, decreasing afterwards. Subsequently, the strange nonchaotic dynamical behaviour of the disturbed 4WS system is investigated. Several routes and mechanisms for the birth of strange nonchaotic attractors (SNAs) are identified, including the torus doubling bifurcation, the torus fractalization, and the loss of transverse stability of a torus. Finally, the nonchaotic property is verified through the calculation of the maximum Lyapunov exponent, and the strange property is characterized using power spectra and rational approximation.

A Vision-Guided Robotic System for Safe Dental Implant Surgery.

Background: Recent advancements in dental implantology have significantly improved outcomes, with success rates of 90-95% over a 10-year period. Key improvements include enhanced preplanning processes, such as precise implant positioning, model selection, and optimal insertion depth. However, challenges remain, particularly in achieving correct spatial positioning and alignment of implants for optimal occlusion. These challenges are pronounced in patients with reduced bone substance or complex anthropometric features, where even minor misalignments can result in complications or defects. Methods: This paper introduces a vision-guided robotic system designed to improve spatial positioning accuracy during dental implant surgery. The system incorporates advanced force-feedback control to regulate the pressure applied to bone, minimizing the risk of bone damage. A preoperative CBCT scan, combined with real-time images from a robot-mounted camera, guides implant positioning. A personalized marker holder guide, developed from the initial CBCT scan, is used for patient-robot calibration. The robot-mounted camera provides continuous visual feedback of the oral cavity during surgery, enabling precise registration of the patient with the robotic system. Results: Initial experiments were conducted on a 3D-printed mandible using a personalized marker holder. Following successful patient-robot registration, the robotic system autonomously performed implant drilling. To evaluate the accuracy of the robotic-assisted procedure, further tests were conducted on 40 identical molds, followed by measurements of implant positioning. The results demonstrated improved positioning accuracy compared to the manual procedure. Conclusions: The vision-guided robotic system significantly enhances the spatial accuracy of dental implants compared to traditional manual methods. By integrating advanced force-feedback control and real-time visual guidance, the system addresses key challenges in implant positioning, particularly for patients with complex anatomical structures. These findings suggest that robotic-assisted implant surgery could offer a safer and more precise alternative to manual procedures, reducing the risk of implant misalignment and associated complications.

Driver–Automated Cooperation Driving Authority Optimization Framework for Shared Steering Control

In this paper, we introduce the preview-follower theory for modeling the trajectory-tracking controller of an automated system using model predictive control (MPC). The primary contribution of this research lies in enhancing tracking accuracy and driving safety when the driver and automated system share similar driving intentions, while also enabling a rapid transfer of driving authority to the human driver in cases of differing intentions. To verify the effectiveness of the proposed driving authority optimization framework, both simulation and driver-in-the-loop experiments were conducted under conditions of consistent and inconsistent driving intentions between the human driver and the autonomous driving system. The results of both experiments demonstrated that the proposed shared steering cooperative control and driving authority optimization framework not only significantly improves vehicle tracking accuracy but also promptly aligns with the driver’s intentions.