Steering Control Research Articles

Control of a three-wheeled omnidirectional mobile robot via a mixed FTB/H∞\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathcal H_\\infty $$\\end{document} approach

In this paper, the problem of guidance and motion control of mobile robots is addressed and solved within the novel framework of the mixed finite-time/H∞\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathcal H_\\infty $$\\end{document} control theory of nonlinear quadratic systems (NLQSs). Starting from a NLQS describing the dynamics of omnidirectional mobile platforms, the main tasks performed for controlling in closed loop the motion of omnidirectional robots can be conveniently formulated as a mixed finite-time/H∞\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathcal H_\\infty $$\\end{document} control problem. A robust motion controller, which can effectively rejects disturbances deviating the robot platform from a planned path, can be designed after choosing a linear state-feedback structure for the controller. The synthesis problem is solved through some sufficient conditions contemplating both norm-bounded disturbances and sets constraining initial and terminal conditions, together with a finite-time bound on the output transient. Therefore, for all the allowable uncertainties, in presence of nonzero initial conditions and exogenous disturbance inputs which are possible within an unstructured environment, the motion control tasks can be accomplished through optimal H∞\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathcal H_\\infty $$\\end{document} performance by simultaneously guaranteeing that the NLQS, which governs in closed loop the robot platform, is finite-time bounded. Finally, the applicability and control performance of the design approach have been evaluated through numerical simulations.

Automatic Braking System

With the proliferation of vehicles and the integration of technology into daily transportation, ensuring road safety has become paramount. Traffic accidents, often resulting in substantial damage and casualties, persist as a global concern. The Automatic Braking System (ABS) stands as a pivotal safety innovation adopted by vehicle manufacturers worldwide. This paper explores the significance and functionality of ABS in preventing wheel lock-up during braking, thereby enabling drivers to maintain steering control. Through an examination of ABS technology, its effectiveness, limitations, and potential advancements, this research aims to contribute to the ongoing discourse on enhancing road safety measures in the modern automotive landscape.

Distributed drive electric vehicle differential assistance particle swarm optimization PID control

To improve the vehicle’s handling stability and enhance the steering assist effect of distributed drive electric vehicles, a differential assist steering control strategy is proposed based on the Particle Swarm Optimization (PSO) algorithm to optimize PID control parameters. A dynamic model of steering torque considering the influence of tire force is established, based on the reference steering torque to calculate the required torque. The PSO algorithm with the Integral of Time-weighted Absolute Error (ITAE) is used to optimize the parameters in the PID steering control, and wheel torque allocation is performed by using the objective function constraint. Simulation results show that the differential assistance can meet the driving requirements of drivers at both high and low speeds, and the proposed control strategy is effective.

Fault-Tolerant Control Method for Active Steering Control of Rail Vehicles Based on Overdrive Characteristics

Fault-Tolerant Control Method for Active Steering Control of Rail Vehicles Based on Overdrive Characteristics

Differential Steering Control Based on CNF and ADRC Techniques for Distributed-Drive Articulated Heavy Vehicles

Differential Steering Control Based on CNF and ADRC Techniques for Distributed-Drive Articulated Heavy Vehicles

Combined Steering and Braking Collision Avoidance Control Method Based on Model Predictive Control

Combined Steering and Braking Collision Avoidance Control Method Based on Model Predictive Control

Estimation of the pavement skid resistance by automated vehicle based on one-dimensional convolutional neural network

ABSTRACT The pavement skid resistance is critical to the driving safety of a road. Automated vehicles, which have high requirements for environmental perception, are not yet commonly equipped with an estimation module that could monitor the real-time skid resistance, leading thus to instability in the steering and braking control. In this paper, we propose a data-driven approach incorporating vehicle dynamics implementing real-time pavement skid resistance estimation for autonomous driving. Feature data is collected through the automated vehicle dynamics calibration process, and the selection of features is essentially based on the use of the vehicle dynamics model. To extract the features of one-dimensional timing signals, a temporal one-dimensional convolutional neural network is proposed to improve the stability and robustness of the estimation and minimise the effect of small perturbations and noises. In addition, given the insufficient data that may be faced in practical situations, a transfer learning approach is further proposed to improve the model generalisation. Training data is collected in an autonomous driving simulation platform and the effectiveness of the method is then verified based on this platform. Satisfactory performance is demonstrated for different driving scenarios and under varied road conditions.

Modulation Steering Motion by Quantitative Electrical Stimulation in Pigeon Robots.

The pigeon robot has attracted significant attention in the field of animal robotics thanks to its outstanding mobility and adaptive capability in complex environments. However, research on pigeon robots is currently facing bottlenecks, and achieving fine control over the motion behavior of pigeon robots through brain-machine interfaces remains challenging. Here, we systematically quantify the relationship between electrical stimulation and stimulus-induced motion behaviors, and provide an analytical method to demonstrate the effectiveness of pigeon robots based on electrical stimulation. In this study, we investigated the influence of gradient voltage intensity (1.2-3.0 V) on the indoor steering motion control of pigeon robots. Additionally, we discussed the response time of electrical stimulation and the effective period of the brain-machine interface. The results indicate that pigeon robots typically exhibit noticeable behavioral responses at a 2.0 V voltage stimulus. Increasing the stimulation intensity significantly controls the steering angle and turning radius (p < 0.05), enabling precise control of pigeon robot steering motion through stimulation intensity regulation. When the threshold voltage is reached, the average response time of a pigeon robot to the electrical stimulation is 220 ms. This study quantifies the role of each stimulation parameter in controlling pigeon robot steering behavior, providing valuable reference information for the precise steering control of pigeon robots. Based on these findings, we offer a solution for achieving precise control of pigeon robot steering motion and contribute to solving the problem of encoding complex trajectory motion in pigeon robots.

High-performance steering tracking control of open circuit variable-speed pump-controlled steering system for heavy-duty vehicles based on flow nonlinearity compensation

Heavy-duty vehicles with long bodies, a large number of axles and large loads are subject to increasingly high requirements for precise steering technology due to the increasing trend toward energy conservation and intelligent assisted driving as well as variable driving conditions. In this paper, an energy-efficient open circuit variable-speed pump-controlled steering system (OPCEHSSS) adapted for heavy loads is used, but its strong flow output nonlinearity and system nonlinear dynamic behavior greatly impede the steering performance. Therefore, in order to reduce the influence of the flow leakage of the fixed-displacement pump on the system and to ensure that the flow output of the system matches the control model, a mapping model based on the fitting of a two-layer neural network algorithm with a dynamic real-time compensation strategy (FNC) is proposed. In addition, considering the strong robustness of the system even under parameter uncertainty and unknown disturbance, a complex nonlinear mathematical model is established based on OPCEHSSS physical characteristics, and a dual-objective control strategy of steering angle and pressure based on sliding mode control (SMC) is proposed. However, in order to reduce the influence of high-order switching discontinuity on the steering and ensure the fast convergence of the control system, a fast super twisting algorithm (STA) based on double saturation function of the boundary layer is proposed. The experimental results show that the three different controllers can effectively reduce the steering angle error after the introduction of FNC. And in the case of a single axle loaded with 6 tons, the improved new FNC+STA integrated dual-objective control strategy improves the accuracy by 53.16% compared with PID and 40.67% compared with SMC. The steady-state error is maintained within 0.9°, realizing the high-performance steering tracking control of OPCEHSSS for heavy vehicles.

Human–Machine Cooperative Steering Control Based on Non-cooperative Nash Game

Human–Machine Cooperative Steering Control Based on Non-cooperative Nash Game

COVID-19 case management in primary dental care settings in Yorkshire and the Humber.

Introduction Emerging guidance during the COVID-19 pandemic aimed to minimise transmission risk within dental settings. This service evaluation provides an overview of reported cases of COVID-19 among primary dental care staff within Yorkshire and the Humber.Methods Data for COVID-19 cases reported within dental practices between November 2020 and August 2021 were extracted from the Public Health England, Health Protection Zone database and summarised using descriptive statistics.Results In total, 421 cases across 223 dental practices were reported, with 221 close contacts and 77 outbreaks identified. Cases/contacts were highest among dental nurses (51/49%). Household and community (42/22%) were the most frequently reported sources of transmission, with the majority of staff-to-staff contacts (60%) occurring in communal/non-clinical areas.Discussion There was a low number of reported COVID-19 cases/contacts among dental professionals, with most cases suspected to have acquired the infection from outside of the dental setting. The majority of personal protective equipment breaches occurred within communal/non-clinical areas. When infection prevention and control guidance was followed, there were no incidences of staff or patients becoming contacts or cases.Conclusion With adherence to strict protocols, the risk of COVID-19 transmission in the dental setting was low within the context of this review. The evaluation highlighted the importance of collaborative working during the pandemic.

Brake-actuated steering control strategy for turning of articulated vehicles

The use of high-capacity vehicles has been shown to significantly reduce carbon dioxide emissions from heavy goods transport and improve the efficiency of the transport sector. Despite their benefits, current legislations regarding the dimensions and weight of the vehicles have limited the widespread use of heavier and longer vehicles. Long vehicles are less manoeuvrable and often necessitate technological interventions to pass manoeuvrability tests. Various rear-steering systems proposed to solve these issues lead to high costs and mass penalties compared to standard fixed axles. This paper presents a new steering concept which utilises the axles’ existing brake actuators to steer, eliminating the need for dedicated actuators on the axle and associated steering hardware. Brakes are individually controlled on each side of a freely steerable axle to generate the necessary torque about the kingpins causing it to steer. A validated vehicle model is used to design a feedback steering strategy based on path-following steering control for low-speed manoeuvring. The proposed system's performance is compared to conventional vehicles and current state-of-the-art active-steering systems through simulations. It demonstrates comparable steering performance to that of electro-hydraulic active-steer systems, with an estimated 50% reduction in both mass and costs.

Trajectory Planning on Rolling Locomotion of Spherical Movable Tensegrity Robots with Multi-Gait Patterns.

Spherical movable tensegrity robots, resorting to the intrinsic hallmark of being lightweight and resilient, have exhibited tremendous potential in exploring unpredictable terrains and extreme environments where traditional robots often struggle. The geometry of spherical tensegrities is suitable for rolling locomotion, which guarantees the system to react to changing demands, navigate unexplored terrain, and perform missions even after suffering massive damage. The objective of this article is to enrich the type of spherical movable tensegrity robots with multiple kinematic gait patterns and to gain superior motion paths that are in conformity with the intrinsic features of structural rolling locomotion. Aiming at this purpose, three 12-rod spherical tensegrities with multi-gait patterns are investigated, and the dynamic simulation on independent (or evolutionary) gait patterns is conducted and testified on ADAMS. The routing spaces and the blind zones formed by single kinematic gait are compared to assess the suitability of the assigned kinematic gait pattern. Accordingly, we develop a trajectory planning method with the embedding of the steering control strategy into a modified rapidly exploring random tree (MRRT) algorithm to produce qualified marching routes. In the meantime, two momentous evaluation indictors, applicable to multi-gaits tensegrities, are introduced in searching the corresponding optimal gait patterns that conform to specified needs. The techniques are illustrated and validated in simulation with comparisons on several prototypes of tensegrity robots, indicating that the proposed method is a viable means of attaining marching routes on rolling locomotion of spherical movable tensegrity robots.

Mission analysis and guidance and control for the SpEye inspection CubeSat

The space community is moving forward with the development of in-orbit servicing and removal technologies to enable the circular economy in space and improve future space missions’ scientific and commercial return and space environment exploitation. The application of nanosatellites to carry out proximity operations presents an additional opportunity to support various novel and cost-effective distributed mission architectures. The SpEye CubeSat mission, funded by the Agenzia Spaziale Italiana in the framework of the Alcor Program, aims at demonstrating inspection and rendezvous guidance navigation and control capabilities of a nanosatellite in close-proximity of the satellite carrier in a low Earth orbit environment. This paper focuses on the design of the SpEye mission and on the guidance and control strategies to enable its technological demonstrations. Particular attention is placed on the safety aspects of trajectory design, considered of paramount importance for the robust autonomous operations of a low-cost CubeSat in proximity to another satellite.

A novel impact angle control guidance law design via predefined time convergence theory

A predefined time guidance law with impact angle control is proposed for intercepting the stationary target. With such a guidance law, the convergence time of the impact angle error for the missile is independent of the initial conditions, and it can be selected at the will of the designer. First, a heading error shaping method based on the Lyapunov asymptotic stability theory will be introduced, which ensures the missile hits the target. Then, a bias term will be designed and introduced into this method, which can make sure the impact angle error converges to zero in the predefined time. The Lyapunov stability analysis is discussed in detail for the resulting predefined-time convergent guidance system. Finally, simulation examples are carried out to illustrate the effectiveness of the proposed guidance law.

The Utilization of Fuzzy Logic Controllers in Steering Control Systems for Electric Ambulance Golf Carts

This study investigates methods to improve steering control for electric ambulance golf carts by conducting a comparative analysis of fuzzy logic controllers. The research assesses four control systems, PD controller, fuzzy PD controller, fuzzy PD+I controller, and PBC and PD+I type fuzzy logic controller, to determine their effectiveness in enhancing steering control. Simulink simulations are employed to evaluate the performance of these controllers under various conditions. Results indicate that the PBC and PD+I type fuzzy logic controller demonstrates superior performance, showing significant reductions in both rise time and settling time with minimal overshoot compared to other controllers. The findings underscore the potential of fuzzy logic controllers in enhancing steering control for electric vehicles. Future research should explore alternative control strategies and assess controller robustness under diverse operating conditions.

Hybrid model predictive control for vehicle trajectory tracking with integrated attitude adjustment

This paper presents a vehicle trajectory tracking method that integrates attitude adjustment. The method utilizes the asymmetric adjustment of the damping of the adjustable damping shock absorber to lift the left and right vehicle body and carry out the attitude adjustment of the vehicle. Additionally, it actively tilts toward the inside of the curve during turning. In order to address the nonlinear constraints of damping asymmetry regulation and the mixed logic relationship between suspension asymmetry damping switching and left and right body lifting during body attitude regulation, a mixed logic dynamic system (MLD) for vehicle attitude adjustment is established. Subsequently, the vehicle trajectory tracking controller is designed using the hybrid model predictive control theory (HMPC), which integrates active steering control and attitude adjustment. The optimal control problem of hybrid systems is transformed into a hybrid integer quadratic programming problem and solved through finite time domain rolling optimization. The effectiveness of the proposed integrated controller is confirmed through Simulink simulation. Compared with pure tracking control and roll tracking control with zero roll angle as the control target, this controller demonstrates its ability to effectively track the reference trajectory and enhance the vehicle’s dynamic performance.

Deployment Actuator Logic Design for Enhancing Aircraft Armament Stability

For the high reliability of guidance weapon, it is important to maintain the stable supply voltage for guidance system while the bomb is gliding. However, the DC motor drive for guidance control in the low temperature can lead to the critical voltage drop due to a large inrush current.In this paper, we propose the wing deployment logic design to enhance the guidance system stability. To derive the stable drive logic based on PWM, we controlled the duty cycles for input voltage of DC motor in low temperature. The initial duty cycle was set to 40 % to relieve the inrush current. Moreover, the proper time, which is less than 1 second for controlling duty cycles, was determined by the iterative wing deployments of guidance kit. Finally, the validity of proposed logic was confirmed from the result the maximum voltage drop for the wing deployment was decreased by 23 %.

Optimal Vision-Based Orientation Steering Control for a 3-D Printed Dexterous Snake-Like Manipulator to Assist Teleoperation

Optimal Vision-Based Orientation Steering Control for a 3-D Printed Dexterous Snake-Like Manipulator to Assist Teleoperation

A self‐learning human‐machine cooperative control method based on driver intention recognition

AbstractHuman‐machine cooperative control has become an important area of intelligent driving, where driver intention recognition and dynamic control authority allocation are key factors for improving the performance of cooperative decision‐making and control. In this paper, an online learning method is proposed for human‐machine cooperative control, which introduces a priority control parameter in the reward function to achieve optimal allocation of control authority under different driver intentions and driving safety conditions. Firstly, a two‐layer LSTM‐based sequence prediction algorithm is proposed to recognise the driver's lane change (LC) intention for human‐machine cooperative steering control. Secondly, an online reinforcement learning method is developed for optimising the steering authority to reduce driver workload and improve driving safety. The driver‐in‐the‐loop simulation results show that our method can accurately predict the driver's LC intention in cooperative driving and effectively compensate for the driver's non‐optimal driving actions. The experimental results on a real intelligent vehicle further demonstrate the online optimisation capability of the proposed RL‐based control authority allocation algorithm and its effectiveness in improving driving safety.