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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. [ABSTRACT FROM AUTHOR]

This article presents a cooperative controller that is specifically designed to enhance the stability of a distributed-drive vehicle during steering. The controller focuses on improving lateral stability during steering and achieving optimal torque allocation to meet numerous objectives. The article proposes a novel approach to improve the performance of the sliding mode controller for transverse stability control during steering. This is achieved by designing a fractional-order non-singular fast terminal sliding mode surface function, a fractional-order double-power exponential convergence law, and introducing a weighted integration term. Furthermore, the vehicle's torque was fine-tuned by employing an ant colony optimization (ACO) technique within the acceptable range defined by the lateral and longitudinal control requirements. To prevent the ACO algorithm from being stuck in local optima, a pseudo-random rule was implemented based on the original state transfer probability. This rule helps accelerate the convergence of the algorithm. Additionally, an elite approach and a dynamic change strategy for pheromone concentration were devised. Ultimately, the performance of the co-controller that was built is evaluated by simulation experiments conducted under both accelerated and decelerated driving situations. The test findings indicate that the technique effectively improves the lateral stability, tracking control, and energy economy of electric cars, with promising potential for practical use. [ABSTRACT FROM AUTHOR]

This article addresses the problem of speed and steering control of unmanned sea surface vehicles operating under unknown ocean environments affected by complex nonlinearities and uncertain hydrodynamic coefficients. An inertial delay control (IDC)-based sliding mode control (SMC) is proposed. The proposed controller is robust against the system's nonlinearities, parametric uncertainties, external disturbances like a strong wind, complex disturbances due to wind-generated and ocean currents, etc. The proposed controller uses IDC to estimate these effects mentioned above, which makes the proposed SMC independent of the bound of uncertainties and disturbances, and provides chatter-free control. The effectiveness of the proposed controller is confirmed by considering the highly nonlinear model of Cypership-II using various performance indices. [ABSTRACT FROM AUTHOR]

The object of the research is the automation processes in maritime navigation to ensure the safety of ship movement by predicting their trajectories in complex aquatic areas, such as narrow passages, straits, and ports. The research applied six key stages to create a comprehensive method for clustering and predicting ship trajectories based on ECDIS data. In the first stage, ship movement trajectories were constructed according to risk categories, using the LCSS and DTW algorithms to compare planned and actual trajectories. This allowed for the accurate identification of course deviations and the determination of potentially dangerous sections of the trajectory. The second stage implemented clustering using the DBSCAN and GMM algorithms. DBSCAN was used to identify the density of points in space, and GMM provided modeling of cluster probabilities, allowing for better risk zone determination. The third stage applied the Douglas-Peucker compression algorithm to reduce the number of points in the trajectories, which preserved key characteristics and optimized data processing. In the fourth stage, ship movement stability was assessed using the Fourier transform, which allowed the detection of high-frequency oscillations that may indicate movement instability caused by changes in course or speed. The fifth stage included fuzzy clustering of trajectories using the Gaussian Mixture Model (GMM), which allowed modeling the probabilities of dangerous trajectories, considering the uncertainty of navigational parameters. At the final stage, a multilayer neural network (MLP) was used to predict future points of ship trajectories. The model accurately predicted the ship’s coordinates, enabling timely trajectory adjustments. Experimental results showed that the developed method increased the accuracy of ship trajectory prediction to 72–81 % and also significantly reduced the final error, ensuring effective risk management during complex navigation. [ABSTRACT FROM AUTHOR]

This paper aims to improve the steering performance of the Ackermann personal mobility scooter based on a new meta-heuristic optimization algorithm named Differential Harris Hawks Optimization (DHHO) and the modeling of the steering encoder. The steering response in the Ackermann mechanism is crucial for automated driving systems (ADS), especially in localization and path-planning phases. Various methods presented in the literature are used to control the steering, and meta-heuristic optimization algorithms have achieved prominent results. Harris Hawks optimization (HHO) algorithm is a recent algorithm that outperforms state-of-the-art algorithms in various optimization applications. However, it has yet to be applied to the steering control application. The research in this paper was conducted in three stages. First, practical experiments were performed on the steering encoder sensor that measures the steering angle of the Landlex mobility scooter, and supervised learning was applied to model the results obtained for the steering control. Second, the DHHO algorithm is proposed by introducing mutation between hawks in the exploration phase instead of the Hawks perch technique, improving population diversity and reducing premature convergence. The simulation results on CEC2021 benchmark functions showed that the DHHO algorithm outperforms the HHO, PSO, BAS, and CMAES algorithms. The mean error of the DHHO is improved with a confidence level of 99.8047% and 91.6016% in the 10-dimension and 20-dimension problems, respectively, compared with the original HHO. Third, DHHO is implemented for interactive real-time PID tuning to control the steering of the Ackermann scooter. The practical transient response results showed that the settling time is improved by 89.31% compared to the original response with no overshoot and steady-state error, proving the superior performance of the DHHO algorithm compared to the traditional control methods. [ABSTRACT FROM AUTHOR]

This paper mainly studies an autonomous path-planning and real-time path-tracking optimization method for snake robot. Snake robots can perform search and rescue, exploration, and other tasks in a variety of complex environments. Robots with visual sensors such as LiDAR can avoid obstacles in the environment through autonomous navigation to reach the target point. However, in an unstructured environment, the navigation of snake robot is easily affected by the external environment, causing the robot to deviate from the planned path. In order to solve the problem that snake robots are easily affected by environmental factors in unstructured environments, resulting in poor path-following ability, this paper uses the Los algorithm combined with steering control to plan the robot in real time and control the robot's steering parameters in real time, ensuring that the robot can stably follow the planned path. [ABSTRACT FROM AUTHOR]

To address the conflict of inconsistent driving intentions between the driver and driving assistance system in cooperative steering, a human–machine cooperative steering control framework based on non-cooperative Nash game is proposed. The competitive relationship between the driver and driving assistance system with different control objectives is modeled as a non-cooperative Nash game. The cooperative steering under human–machine Nash game condition is transformed into a model predictive control optimization problem to achieve coordination through Nash equilibrium. A flexible driving authority allocation strategy based on time-to-collision is then proposed for vehicle lateral control, with dynamic driving authority weights fed into the human–machine cooperative steering model in real time. The effectiveness of the non-cooperative Nash game human–machine cooperative steering strategy and flexible driving authority allocation strategy is validated through simulations and hardware-in-the-loop experiments. Results show the proposed strategy can improve vehicle safety while retaining certain driver freedom when human–machine control targets conflict, by flexibly transferring authority from the driver to the assistance system as collision risk increases. [ABSTRACT FROM AUTHOR]

In this study, the optimization problem for controlling the pivot steering function of tracked vehicles is addressed. Firstly, kinematic modeling of the pivot steering process of tracked vehicles is conducted. Secondly, the control system of tracked vehicles is decoupled, and PID control algorithms for vehicle speed and yaw rate are separately designed. Furthermore, the parameters of the PID controllers are optimized using the Grey Wolf Optimizer algorithm. Finally, by constructing a joint simulation model using Matlab/Simulink + RecurDyn (V9R4), the simulation results indicate that the above control algorithm can effectively improve the tracking speed of tracked vehicles on vehicle speed and yaw rate under the pivot steering condition, quickly respond to the driver's driving intention, and ensure the stability of the pivot steering process, providing an effective basis for further research on the pivot steering function of tracked vehicles. [ABSTRACT FROM AUTHOR]

This article proposes a new intelligent trajectory tracking control law for the precise maneuvering of an autonomous vehicle in the presence of parametric uncertainties and external disturbances. The controller design includes a fuzzy sliding mode algorithm for smooth motion control subjected to steering saturation and curvature constraints. Along with the Salp Swarm Optimization technique, explored for optimal selection of surface coefficient in fractional order Proportional-Derivative type P D α sliding manifold. The sliding variable on the surface approaches zero in a finite time. Further, the trajectory tracking control rule offers the stability of closed-loop tracking on the predetermined path and ensures finite time convergence to the sliding surface. In addition, to estimate the hitting gain in online mode, a supervisory fuzzy logic controller system is used. Therefore, it is not necessary to determine upper bounds on uncertainty in the dynamic parameters of autonomous vehicles. Lyapunov theory verifies the global asymptotic stability of the entire closed-loop control strategy. The major control issue is the input constraints arising primarily due to the capability of the steering actuating module, which causes significant deviation or vehicle instability. Consequently, it is desirable to design a robust adaptive stable controller, such as Adaptive Backstepping Control (ABC), even though it requires vehicle model information. Therefore, the proposed model-free intelligent sliding mode technique offers better tracking performance and vehicle stability in adverse conditions. Finally, the efficacy of the proposed control technique was confirmed through a comparative analysis based on numerical simulation using MATLAB/SIMULINK and experimental validation using Quanser's self-driving car module. A quantitative study was conducted to elucidate the superior tracking performance of intelligent control over the traditional SMC and adaptive backstepping control methods. [ABSTRACT FROM AUTHOR]

The study of lateral steering control for Automated Driving Systems identifies new control solutions more often than new control problems. This is likely due to the maturity of the field. To prevent repeating efforts toward solving already-solved problems, what is needed is a cohesive way of evaluating all developed controllers under a wide variety of environmental conditions. This work serves as a step in this direction. Four controllers are tested on five maneuvers representing highways and collision avoidance trajectories. Each controller and maneuver combination is repeated on five sets of environmental conditions or Operational Design Domains (ODDs). The design of these ODDs ensures the translation of these experimental results to real-world applications. The commercial software, CarSim 2020, is extended with Simulink models of the environment, sensor dynamics, and state estimation performances to perform highly repeatable and realistic evaluations of each controller. The results of this work demonstrate that most of the combinations of maneuvers and ODDs have existing cheap controllers that achieve satisfactorily safe performance. Therefore, this field's research efforts should be directed toward finding new control problems in lateral path tracking rather than proposing new controllers for ODDs that are already solved. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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.

Taking a motor planning perspective, this study investigates whether haptic force cues displayed on the steering wheel are more effective than visual cues in signaling the direction of an upcoming lane change. Licensed drivers drove in a fixed-base driving simulator equipped with an active steering system for realistic force feedback. They were instructed to make lane changes upon registering a directional cue. Cues were delivered according to the movement precuing technique employing a pair of precues and imperative cues which could be either visual, haptic, or crossmodal (a visual precue with a haptic imperative cue, and vice versa). The main dependent variable was response time. Additional analyses were conducted on steering wheel angle profiles and the rate of initial steering errors. Conditions with a haptic imperative cue produced considerably faster responses than conditions with a visual imperative cue, irrespective of the precue modality. Valid and invalid precues produced the typical gains and costs, with one exception. There appeared to be little cost in response time or initial steering errors associated with invalid cueing when both cues were haptic. The results are consistent with the hypothesis that imperative haptic cues facilitate action selection while visual stimuli require additional time-consuming cognitive processing. [ABSTRACT FROM AUTHOR]

Recent research indicates that installing shoulders on rural roads for safety purposes causes drivers to steer further inside on right bends and thus exceed lane boundaries. The present simulator study examined whether continuous rather than broken edge-line delineation would help drivers to keep their vehicles within the lane. The results indicated that continuous delineation significantly impacts the drivers' gaze and steering trajectories. Drivers looked more towards the lane centre and shifted their steering trajectories accordingly. This was accompanied by a significant decrease in lane-departure frequency when driving on a 3.50-m lane but not on a 2.75-m lane. Overall, the findings provide evidence that continuous delineation influences steering control by altering the visual processes underlying trajectory planning. It is concluded that continuous edge-line delineation between lanes and shoulders may induce safer driver behaviour on right bends, which has potential implications for preventing run-off-road crashes and cyclist safety. Practitioner summary: This study examined how continuous and broken edge lines influence driving behaviour around bends with shoulders. With continuous delineation, drivers gazed and steered in the bend further from the edge line and thus had fewer lane departures. Continuous marking can therefore help prevent run-off-road crashes and improve cyclists' safety. [ABSTRACT FROM AUTHOR]

This paper presents a new sensorless control method for four-wheel steering vehicles. Compared to the existing sensor-based control, this approach improved dynamic stability, manoeuvrability and robustness in case of malfunction of the front steering angle sensor. It also provided a software redundancy and backup solution, as well as improved fault tolerance. The strategy of the sensorless control is based on the sliding mode method to estimate the replacement of the front steering input from the errors between the vehicle's measured and desired values of the vehicle's sideslip angle and yaw rate. The simulation results demonstrate that the observer effectively estimated the front wheel steering angle at both low- and high-speed scenarios in the cornering and lane change manoeuvres. Furthermore, the sensorless control approach can achieve equivalent control performances to the sensor-based controller including a small and stable yaw rate response and zero sideslip angle. The results of the study offer a potential solution for improving manoeuvrability, stability and sensor fault tolerance of four-wheel steering vehicles. [ABSTRACT FROM AUTHOR]

We test an approach to modelling the car driver behaviour during simulated lane changing tasks, aiming to obtain a sufficiently precise model in the simplest possible form, namely, with a small number of parameters. Various applications of such models are available in the literature. Based on a recent review of the research to date, the cybernetic single-loop transfer function models employing McRuer’s theory are applied. The purpose of the presented method is to evaluate the optimal structure of the transfer function via cross-validation as a technique known from machine learning. The experiments utilize a driving simulator with in-house developed software; this configuration facilitates acquiring the data at the desired sampling frequency and in a manner that ensures the repeatability of the test process scenarios. Using the cross-validation results, we evaluate the second-order model with a derivative state and a reaction delay component as an optimal structure for approximating the measured data, which originated from a set of measurements on 92 active drivers. Even though more complex driving tasks could require high-order models, driver’s control action during our specific experiment is described through only four parameters. The parameters are jointly determined by the current driver’s mental state and the testing conditions defined in our scenario. Since the parameters are related to his/her dynamical behaviour, they allow easier mutual comparison of the drivers than complex models with many parameters. The results are verified via establishing a relationship to the multi-loop model presented in the recent literature. The larger dataset enables evaluating the confidence intervals of the drivers’ parameters which is inconvenient with 4 to 10 drivers commonly presented in the relevant sources.

Steering systems with an electromechanical amplifier (EMA) are a modern design solution compared to hydraulic and electro-hydraulic steering systems. Hydraulic steering amplifiers are used in the steering drives of modern trolleybuses and electric buses. If an electric motor powered from the power grid is used to drive the hydraulic pump in trolleybuses, then in electric buses, the source of electrical power is rechargeable batteries. Energy consumption to ensure the operation of the hydraulic power steering reduces the mileage of the electric bus between charging the batteries. Therefore, conducting research and substantiating the possibility of using EMA in electric buses is relevant and has important practical significance. Considering the design features of the electromechanical steering amplifier and the design of the steering axle of the Electron 19101 electric bus, a dynamic model of the drive for turning the controlled wheels of the electric bus was built on the spot. Based on the dynamic model of the drive for turning the controlled wheels of an electric bus with an electromechanical steering amplifier, a mathematical model of the drive and a stimulation model were developed in the MathLab Simulink environment for the study of oscillatory processes in the drive links when the wheels turn on a horizontal plane. The nature of the change of elastic torques in the links of the steering control drive of an electric bus with an electromechanical steering amplifier, the frequency of rotation of the rotor of the electric motor, the current strength in the windings of the rotor and stator of the electric motor, the angle of rotation of the steered wheels as a function of time was studied. It was found that the change in the moment of resistance to the rotation of the steered wheels increases smoothly, and the load on the drive links of the electromechanical power steering depends on the total gear ratio of the drive and its distribution between the gearbox and the steering rack. A decrease in the total transmission ratio of the drive leads to an increase in the speed of rotation of the driven wheels and an increase in elastic moments in the drive links. Transient processes in the electric part of the drive correspond to the characteristics of such electric motors in terms of the nature of the change and do not exceed the permissible values in terms of magnitude. It was established that the power characteristics of the electromechanical steering amplifier with the selected parameters and the electric motor can ensure the control of the wheels of the electric bus following the established requirements.

Abstract To resolve the response delay and overshoot problems of intelligent vehicles facing emergency lane-changing due to proportional-integral-differential (PID) parameter variation, an active steering control method based on Convolutional Neural Network and PID (CNNPID) algorithm is constructed. First, a steering control model based on normal distribution probability function, steady constant radius steering, and instantaneous lane-change-based active for straight and curved roads is established. Second, based on the active steering control model, a three-dimensional constraint-based fifth-order polynomial equation lane-change path is designed to address the stability problem with supersaturation and sideslip due to emergency lane changing. In addition, a hierarchical CNNPID Controller is constructed which includes two layers to avoid collisions facing emergency lane changing, namely, the lane change path tracking PID control layer and the CNN control performance optimization layer. The scaled conjugate gradient backpropagation-based forward propagation control law is designed to optimize the PID control performance based on input parameters, and the elastic backpropagation-based module is adopted for weight correction. Finally, comparison studies and simulation/real vehicle test results are presented to demonstrate the effectiveness, significance, and advantages of the proposed controller.

A steering control system is one of the key steps in the tractor's automatic navigation. Many nonlinear factors (such as hydraulic dead zone, saturation zone, and mechanical backlash) can dominate the performance of steering control, even the accuracy and stability of tractor navigation. In this study, the Lovol Europa M704-2H tractor was taken as the test platform with the electric steering wheel instead of the onboard steering wheel as the actuator, where the angle sensor on the steering wheel axle of the tractor to measure the size of the wheel turning angle. The steering control system included the steering angle calibration and the steering control. The steering angle calibration aimed to obtain the limit angle and median AD value size of the tractor, and then the steering wheel angle size at any angle by linear fitting. The limit steering angle calibration was required for the tractor steering wheel close to the limit through a circle, the satellite positioning of the trajectory point, and the least squares, in order to obtain the minimum turning radius of the tractor. The two-wheel model of the tractor was then utilized to calculate the maximum steering angle. The median steering angle calibration was required for the tractor to drive a section of an approximate straight-line trajectory. The least squares method (LSM) was also used to identify the correction of the zero position for the accurate median AD value. The TZ-QX2A6/5T front wheel angle meter was selected to compare the front wheel angle with the sensor measurement. The results show that the maximum error of calibrated angle measurement was 1.3°, and the average error was 0.11°, fully meeting the demand of steering control. At the same time, the mechanical backlash of the steering system was firstly quantified, in terms of the angular size of steering wheel reversal across the free travel. The accurate size of mechanical backlash was obtained through experimental identification. Then, the fuzzy PD steering control with backlash compensation was designed at the same time. When the electric steering wheel was out of the free travel, the fuzzy PD control was adopted; when the electric steering wheel was in free travel, the steering control was compensated to add the backlash P control into the output of the fuzzy PD control. Simulation results in Simulink show that better performance was achieved in the tracking of the steering angle signals of square wave and sinusoidal wave, fully meeting the requirements. The fuzzy PD steering control with/without the backlash compensation was also used to compare the tracking performance of square wave and sinusoidal steering angle signals. The experimental results show that the response time of the square wave angle signal tracking without backlash compensation was 1.3 s, where the maximum and average steady-state errors were 0.672°, and 0.244°, respectively. The average delay time of the sine wave angle signal tracking without backlash compensation was 0.5 s, where the maximum and average errors were 2.59° and 1.32°, respectively. Compared to the algorithm without backlash compensation, the time of steering angle error within ±0.2° was improved by 71%, the maximum steady state error was reduced by 0.022°, and the average steady state absolute error was reduced by 0.112°. Once the backlash compensation was performed, the response time of the square wave angle signal tracking was 1.1 s, where the maximum and average steadystate errors were 0.65°, and 0.132°, respectively. The average delay time of the sine wave angle signal tracking was 0.5 s, the maximum and average errors were 1.91° and 1.09°, respectively. Compared to the algorithm without backlash compensation, the maximum error was reduced by 0.68°, and the average error was reduced by 0.23°. The experimental results show that the backlash compensation outstandingly improved the stability of steering control for the less control error. The linear navigation test on the leveled field showed that the maximum and absolute average errors of angle tracking were 2.82° and 0.61°, respectively, when the system was in a steady state. Therefore, better performance was achieved with the high accuracy of the steering control system. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this study, the optimization problem for controlling the pivot steering function of tracked vehicles is addressed. Firstly, kinematic modeling of the pivot steering process of tracked vehicles is conducted. Secondly, the control system of tracked vehicles is decoupled, and PID control algorithms for vehicle speed and yaw rate are separately designed. Furthermore, the parameters of the PID controllers are optimized using the Grey Wolf Optimizer algorithm. Finally, by constructing a joint simulation model using Matlab/Simulink + RecurDyn (V9R4), the simulation results indicate that the above control algorithm can effectively improve the tracking speed of tracked vehicles on vehicle speed and yaw rate under the pivot steering condition, quickly respond to the driver’s driving intention, and ensure the stability of the pivot steering process, providing an effective basis for further research on the pivot steering function of tracked vehicles.

Recent cellular systems are moving towards heterogeneous cellular networks (HCNs) that consist of a mixture of miniature cells and legacy macro-cells to meet the requirements of wireless data traffic, owing to the immense amount of multi-purpose mobile applications. The inclusion of small cells is a cost-effective solution for enhancing the size and coverage of the existing macro-cellular network. This article assumes a heterogeneous cellular network consisting of two tiers of base stations (BSs): large-scale (macro) and small-scale (pico) BSs. The users are evenly distributed, and each tier of BSs and users creates a uniform Poisson point process (PPP). Practical third-generation partnership project (3GPP) models for path loss are considered, and three camping/association criteria are utilized to relate user equipment (UEs) to large or small-scale BSs, including coupled and decoupled camping criteria to study coverage. The impact of several system design parameters on coverage is investigated using the aforementioned heterogeneous cellular network, association criteria, and 3GPP path loss models. Our simulation results provide insights into the effect of infrastructure sharing between macro and pico-cells and user density on coverage. We also explore the impact of fractional power control (FPC) and signaling limits on coverage under all considered association strategies. Finally, we investigate the effect of open-loop UE transmission power, pico-density, and biasing on coverage. Specifically, we thoroughly explore the effect of empty BSs on coverage under all system design parameters.

Nomoto's linear model is always utilized in model-based steering control design for model experiments to analyze the steering dynamics and optimize the controllers using numerical tools. The effective identification of parameters for the free running model is important for reducing the time cost of model experiments. In this paper, a parameter identification method based on closed-loop responses using unity feedback is discussed. Unity feedback can transform the original dynamics of the model into the familiar 2nd-order mechanical mass-damper-spring system, which is convenient for analyzing the system. Although the identification of parameters based on closed-loop responses is a common technique in control engineering, few approaches using the free running model have been applied in ocean engineering. Given this motivations, parameter identification methods based on unity feedback responses are evaluated through numerical simulations and model experiments. From the results of this investigation, it is clear that noise in measurements and the initial yaw rate of the running model have detrimental impacts on the identified values. [ABSTRACT FROM AUTHOR]

To resolve the response delay and overshoot problems of intelligent vehicles facing emergency lane-changing due to proportional-integral-differential (PID) parameter variation, an active steering control method based on Convolutional Neural Network and PID (CNNPID) algorithm is constructed. First, a steering control model based on normal distribution probability function, steady constant radius steering, and instantaneous lane-change-based active for straight and curved roads is established. Second, based on the active steering control model, a three-dimensional constraint-based fifth-order polynomial equation lane-change path is designed to address the stability problem with supersaturation and sideslip due to emergency lane changing. In addition, a hierarchical CNNPID Controller is constructed which includes two layers to avoid collisions facing emergency lane changing, namely, the lane change path tracking PID control layer and the CNN control performance optimization layer. The scaled conjugate gradient backpropagation-based forward propagation control law is designed to optimize the PID control performance based on input parameters, and the elastic backpropagation-based module is adopted for weight correction. Finally, comparison studies and simulation/real vehicle test results are presented to demonstrate the effectiveness, significance, and advantages of the proposed controller. [ABSTRACT FROM AUTHOR]

Road accidents are one of the major cause of human deaths around the globe. In order to accelerate autonomous driving, a problem of designing suitable lateral and longitudinal drive control scheme, with an obstacle avoidance mechanism has been considered. For this purpose, a two-wheel equivalent model of a vehicle has been adopted and steering angle velocity is treated as a control input. A cascaded architecture for Obstacle Avoidance and Drive Control has been presented. Impact point algorithm has been developed for Obstacle Avoidance System that receives data from various onboard sensors, and computes potential impact probability based on position and velocity of own vehicle and potential static and moving obstacles. The reference heading and speed signals are continuously updated by the Obstacle Avoidance System. Feedback linearization based lateral and longitudinal drive control laws have been proposed to control the required heading angle and longitudinal speed, respectively. Performance of the proposed control scheme for various operational scenarios has been evaluated through simulations. [ABSTRACT FROM AUTHOR]

Human-machine interfaces (HMIs) can be used to decode a user'smotor intention to control an external device. People that suffer from motor disabilities, such as spinal cord injury, can benefit from the uses of these interfaces. While many solutions can be found in this direction, there is still room for improvement both from a decoding, hardware, and subject-motor learning perspective. Here we show, in a series of experiments with non-disabled participants, a novel decoding and training paradigm allowing naïve participants to use their auricular muscles (AM) to control two degrees of freedom with a virtual cursor. AMs are particularly interesting because they are vestigial muscles and are often preserved after neurological diseases. Our method relies on the use of surface electromyographic records and the use of contraction levels of both AMs to modulate the velocity and direction of a cursor in a two-dimensional paradigm. We used a locking mechanism to fix the current position of each axis separately to enable the user to stop the cursor at a certain location. A five-session training procedure (20-30min per session) with a 2D center-out task was performed by five volunteers. All participants increased their success rate (Initial: 52.78 ± 5.56%; Final: 72.22 ± 6.67%; median ± median absolute deviation) and their trajectory performances throughout the training. We implemented a dual task with visual distractors to assess the mental challenge of controlling while executing another task; our results suggest that the participants could perform the task in cognitively demanding conditions (success rate of 66.67 ± 5.56%). Finally, using the Nasa Task Load Index questionnaire, we found that participants reported lower mental demand and effort in the last two sessions. To summarize, all subjects could learn to control the movement of a cursor with two degrees of freedom using their AM, with a low impact on the cognitive load. Our study is a first step in developing AM-based decoders for HMIs for people with motor disabilities, such as spinal cord injury. [ABSTRACT FROM AUTHOR]

In this paper, the locomotion and steering control of a simulated Mini Cheetah quadruped robot was investigated in the presence of terrain characterised by low friction. Low-level locomotion and steering control were implemented via a central pattern generator approach, whereas high-level steering control manoeuvres were implemented by comparing a neural network and a linear model predictive controller in a dynamic simulation environment. A data-driven approach was adopted to identify the robot model using both a linear transfer function and a shallow artificial neural network. The results demonstrate that, whereas the linear approach showed good performance in high-friction terrain, in the presence of slippery conditions, the application of a neural network predictive controller improved trajectory accuracy and preserved robot safety with different steering manoeuvres. A comparative analysis was carried out using several performance indices. [ABSTRACT FROM AUTHOR]

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.

Robotic fish are susceptible to interference from non-stationary flow fields and thus may deviate from the target course during navigation. In this study, course angle feedback is used to solve the problem of course deviation in a robotic fish without flow field sensors. First, a relationship between joint angular motion and joint torque is obtained by establishing the joint dynamics model of a robotic fish. In addition, a relationship between the propulsion and steering torque and swing posture is obtained. Subsequently, to maintain the stability of the robotic fish, a central pattern generator controller is used to adjust the closed-loop control system. Furthermore, this study takes the length of time during which the robot fish converges from a set course angle deviation to zero as an optimization index, and uses the particle swarm optimization algorithm to obtain the best controller parameters that can achieve rapid steering. The simulation analysis is performed based on the established dynamics model of a robotic fish, and the results verify the effectiveness and rationality of the proposed design method.

Human-machine interfaces (HMIs) can be used to decode a user's motor intention to control an external device. People that suffer from motor disabilities, such as spinal cord injury, can benefit from the uses of these interfaces. While many solutions can be found in this direction, there is still room for improvement both from a decoding, hardware, and subject-motor learning perspective. Here we show, in a series of experiments with non-disabled participants, a novel decoding and training paradigm allowing naïve participants to use their auricular muscles (AM) to control two degrees of freedom with a virtual cursor. AMs are particularly interesting because they are vestigial muscles and are often preserved after neurological diseases. Our method relies on the use of surface electromyographic records and the use of contraction levels of both AMs to modulate the velocity and direction of a cursor in a two-dimensional paradigm. We used a locking mechanism to fix the current position of each axis separately to enable the user to stop the cursor at a certain location. A five-session training procedure (20–30 min per session) with a 2D center-out task was performed by five volunteers. All participants increased their success rate (Initial: 52.78 ± 5.56%; Final: 72.22 ± 6.67%; median ± median absolute deviation) and their trajectory performances throughout the training. We implemented a dual task with visual distractors to assess the mental challenge of controlling while executing another task; our results suggest that the participants could perform the task in cognitively demanding conditions (success rate of 66.67 ± 5.56%). Finally, using the Nasa Task Load Index questionnaire, we found that participants reported lower mental demand and effort in the last two sessions. To summarize, all subjects could learn to control the movement of a cursor with two degrees of freedom using their AM, with a low impact on the cognitive load. Our study is a first step in developing AM-based decoders for HMIs for people with motor disabilities, such as spinal cord injury.

In demonstration experiments of automated driving vehicles, lane departures and collisions with roadside structures due to poor vehicle positioning and self-localization have been reported. In this study, we propose a promising method to prevent such departures and collisions, and then validate the proposed method by applying it to an actual automated driving vehicle. The proposed method monitors the target steering angles computed by the automated driving control and limits them before commanded the actuator when there is a risk of colliding with obstacles. As the above-mentioned control is lower-level, it can prevent an automated driving vehicle from colliding with obstacles without complicating upper-level controls. Experiments on an actual automated driving vehicle showed that the steering control structure of the proposed method could prevent an automated driving vehicle from colliding with obstacles by limiting its target steering angle. In addition, the method does not impose excessive limits on the steering angle when the automated driving vehicle follows a normal path and no risk of collision exists. [ABSTRACT FROM AUTHOR]

This article presents the design and real-time implementation of an optimal controller for precise steering control of a remotely operated underwater vehicle (ROV). A PI controller is investigated to achieve the desired steering performance. The gain parameters of the controller are tuned using the genetic algorithm (GA). The experimental response corresponding to the step waveform for the GA is obtained. A root-locus-tuned PI controller alongside a simulated-annealing-based PI controller (SAPI) is used to benchmark the response characteristics such as overshoot, peak time, and settling time. The experimental findings indicate that GAPI provides considerably better performance than SAPI and the root-locus-tuned controller. [ABSTRACT FROM AUTHOR]

• Dynamic model of the tracked vehicle considered this type of forces which is traction, hydrodynamic, centrifugal, lateral friction, longitudinal resistive, bulldozing, and compaction force. • Path tracking is vital in autonomous vehicles since it is designed to deliver appropriate steering, throttle, and brake input to control the vehicle's direction and speed along a predefined path. • Model Predictive Control (MPC) may handle system limits and future predictions. • The objective of a path tracking controller is to eliminate any variation in vehicle direction from the specified path. This paper provides a review of path tracking strategies used in autonomous vehicle control design. Several elements of modelling process and path tracking control are examined, including the vehicle model implemented, the path tracking control algorithms used, and the criteria for evaluating the controller's performance. Path tracking control is classified into several forms based on its methodology and linearity. Vehicle models are grouped into numerous types based on the linearity and the intended behaviour to be observed. This study explores each of these strategy in terms of the applicability and disadvantages/advantages. The main challenges in the field of path tracking control are defined and future research directions are offered based on the critical reviews. Based on the entire review, a model-based controller based on a linear vehicle model and assessed with hardware-in-the-loop (HIL) is suggested. This review is aimed to serve as a starting point for determining which controllers to use in path tracking control development for an autonomous tracked vehicle. [ABSTRACT FROM AUTHOR]

A dynamic model predictive control method for driving robots is proposed to realize accurate steering control of the test vehicle. First, the coupling dynamics model of the driving robot and the controlled vehicle is established, and the controllability of the coupling model is judged. Then, the Kalman filter is used to estimate the state of the coupled model, and a model predictive controller is designed according to the estimated state. The least square method is adapted to fit the nonlinear relationship between path curvature and prediction horizon, and a dynamic model predictive controller with variable prediction horizon is designed. Finally, the simulation and the test of the steering control of the driving robot at different conditions are conducted, and the results verify the effectiveness of the proposed method.

• Drivers shifted toward the inner edge on right but not on left bends with shoulders. • This occurred at entry, apex, and innermost position with no oncoming traffic. • This occurred at apex and innermost position with oncoming traffic. • Findings show a previously unreported impact of shoulders on steering behavior. • It is argued that drivers view shoulders as a new field of safe travel. Paved shoulders have long been used to create "forgiving" roads where drivers can maintain control of their vehicles even when as they drift out of the lane. While the safety benefits of shoulders have been well documented, their effects on driver behavior around curves have scarcely been examined. The purpose of this paper is to fill this gap by assessing whether the addition of shoulders affects driver behavior differently as a function of bend direction. Driver behavior in a driving simulator was analyzed on left and right curves of two-lane rural roads in the presence and absence of 0.75-m and 1.25-m shoulders. The results demonstrated significant changes in drivers' lateral control when shoulders were provided. In the absence of oncoming traffic, the shoulders caused participants to deviate more toward the inner lane edge at curve entry, at the apex and at the innermost position on right bends but not left ones. In the presence of oncoming traffic, this also occurred at the apex and the innermost position, leading participants to spend more time off the lane on right curves. Participants did not slow down in either traffic condition to compensate for steering farther inside, thereby increasing the risk of lane departure on right curves equipped with shoulders. These findings highlight the direction-specific influence of shoulders on a driver's steering control when driving around bends. They provide arguments supporting the idea that drivers view paved shoulders as a new field of safe travel on right curves. Recommendations are made to encourage drivers to keep their vehicle within the lane on right bends and to prevent potential interference with cyclists when a shoulder is present. [ABSTRACT FROM AUTHOR]

In general, vehicle longitudinal velocity varies and the values of vehicle parameters vary greatly. Therefore, when we ignore the facts and design a controller, the controlled vehicle system may have undesired control performance. In the worst case, the controlled vehicle system may become unstable. To address the problem, we propose an improved rollover prevention control scheme using front and rear-wheel steering. At first, we propose a new representation for vehicles. The representation is suitable for controller design in case when vehicle longitudinal velocity and the values of vehicle parameters vary. Next, based on the representation, we will develop an improved rollover prevention control scheme. Finally, numerical simulations are carried out to demonstrate the usefulness of the proposed controller. [ABSTRACT FROM AUTHOR]

In this paper, the locomotion and steering control of a simulated Mini Cheetah quadruped robot was investigated in the presence of terrain characterised by low friction. Low-level locomotion and steering control were implemented via a central pattern generator approach, whereas high-level steering control manoeuvres were implemented by comparing a neural network and a linear model predictive controller in a dynamic simulation environment. A data-driven approach was adopted to identify the robot model using both a linear transfer function and a shallow artificial neural network. The results demonstrate that, whereas the linear approach showed good performance in high-friction terrain, in the presence of slippery conditions, the application of a neural network predictive controller improved trajectory accuracy and preserved robot safety with different steering manoeuvres. A comparative analysis was carried out using several performance indices.