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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 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]

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]

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.

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.

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.

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]

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.

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.

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The steering system in autonomous vehicles is an essential issue that must be addressed. Appropriate control will result in a smooth and risk-free steering system. Compared to other types of controls, type-2 fuzzy logic control has the advantage of dealing with uncertain inputs, which are common in autonomous vehicles. This paper proposes a novel method for the steering control of autonomous vehicles based on type-2 fuzzy logic control combined with PI control. The primary control, type-2 fuzzy logic control, has three inputs—distance, navigation, and speed. The fuzzy system's output is the steering angle value. This was used as input for the secondary control, PI control. This control is in charge of adjusting the motor's position as a manifestation of the steering angle. The study results applied to the EPS system of autonomous vehicles revealed that type-2 fuzzy logic control and PI control produced better and smoother control than type-1 fuzzy logic control and PI. The slightest disturbance in the type-1 fuzzy logic control showed a significant change in steering, while this did not occur in the type-2 fuzzy logic control. The results indicate that type-2 fuzzy logic control and PI control could be used for autonomous vehicles by maintaining the comfort and safety of the users. [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.

The lateral position control of the vehicle is analyzed in the presence of time delay. To compensate the negative effects of dead time, the predictor control approach called finite spectrum assignment is applied. This controller includes a linear model of the plant and uses the solution of this model over the delay interval to predict the current system states. The focus of the article is whether to include tire dynamics in the predictive model of the controller. Although the more detailed model should improve control performance, the additional parameters (e.g., tire stiffnesses and yaw moment of inertia) are difficult to determine accurately. The effects of parameter mismatches are analyzed in detail, and recommendations are given to ensure safe control of the vehicle. It is shown that the inclusion of tire dynamics in the predictive model vastly improves control performance even in the presence of large parameter errors, but in certain cases, the inaccuracies may lead to instability. [ABSTRACT FROM AUTHOR]

Path tracking control of intelligent vehicles has to deal with the difficulties of model uncertainties and nonlinearities. As a class of adaptive optimal control methods, reinforcement learning (RL) has received increasing attention in solving difficult control problems. However, feature representation and online learning ability are two major problems to be solved for learning control of uncertain dynamic systems. In this article, we propose a multi-kernel online RL approach for path tracking control of intelligent vehicles. In the proposed approach, a multiple kernel feature learning framework is designed for online learning control based on dual heuristic programming (DHP) and the new online learning control algorithm is called multi-kernel DHP (MKDHP). In MKDHP, instead of the expert knowledge for selecting and fine-tuning of a suitable kernel function, only a set of basic kernel functions is required to be predefined and the multi-kernel features can be learned for value function approximation in the critic. The simulation studies on path tracking control for intelligent vehicles have been conducted under $S$ -curve and urban road conditions. The results demonstrated that compared with other typical path tracking controllers for intelligent vehicles, such as the linear quadratic regulator (LQR), the pure pursuit controller and the ribbon-based controller, the proposed multi-kernel learning controller can achieve better performance in terms of tracking precision and smoothness. [ABSTRACT FROM AUTHOR]

While full automation of road vehicles remains a future goal, shared-control and semiautonomous driving—involving transitions of control between the human and the machine—are more feasible objectives in the near term. These alternative driving modes will benefit from new research toward novel steering control devices, more suitably where machine intelligence only partially controls the vehicle. In this article, it is proposed that when the human shares the control of a vehicle with an autonomous or semiautonomous system, a force control, or nondisplacement steering wheel (i.e., a steering wheel which does not rotate but detects the applied torque by the human driver) can be advantageous under certain schemes: tight rein or loose rein modes according to the $H$ -metaphor. We support this proposition with the first experiments to the best of our knowledge, in which human participants drove in a simulated road scene with a force control steering wheel (FCSW). The experiments exhibited that humans can adapt promptly to force control steering and are able to control the vehicle smoothly. Different transfer functions are tested, which translate the applied torque at the FCSW to the steering angle at the wheels of the vehicle; it is shown that fractional order transfer functions increment steering stability and control accuracy when using a force control device. The transition of control experiments is also performed with both: a conventional and an FCSW. This prototypical steering system can be realized via steer-by-wire controls, which are already incorporated in commercially available vehicles. [ABSTRACT FROM AUTHOR]

We present a time-delayed control (TDC) approach that applies it to the electric power steering (EPS) system for the first time. The TDC approach uses a one-sample delayed information of the system to cancel out uncertain and unknown dynamics, including disturbances. Therefore, it is possible to achieve the dominant pole using the pole-assignment so that it can be easily performed in the desired convergence rate. Moreover, given that tuning parameters of the TDC approach are very few in number, this control approach is very convenient for the practicing engineers who do not have control engineering knowledge. We proved the system criteria for the TDC approach applied to the EPS system and hence can always guarantee the system stability. The effectiveness of the TDC approach is verified through simulations, which is compared to that of the existing control approach.

Due to the complex environment in the field, using machine vision technology to enable the robot to travel autonomously was a challenging task. This study investigates a method based on mathematical morphology and Hough transformation for drip tape following by a two-wheeled robot trailer. First, an image processing technique was utilized to extract the drip tape in the image, including the selection of the region of interest (ROI), Red-Green-Blue (RGB) to Hue-Saturation-Value (HSV) color space conversion, color channel selection, Otsu’s binarization, and morphological operations. The line segments were obtained from the extracted drip tapes image by a Hough line transform operation. Next, the deviation angle between the line segment and the vertical line in the center of the image was estimated through the two-dimensional law of cosines. The steering control system could adjust the rotation speed of the left and right wheels of the robot to reduce the deviation angle, so that the robot could stably travel along the drip tape, including turning. The guiding performance was evaluated on the test path formed by a drip tape in the field. The experimental results show that the proposed method could achieve an average line detection rate of 97.3% and an average lateral error of 2.6 ± 1.1 cm, which was superior to other drip-tape-following methods combined with edge detection, such as Canny and Laplacian.

The steering system in autonomous vehicles is an essential issue that must be addressed. Appropriate control will result in a smooth and risk-free steering system. Compared to other types of controls, type-2 fuzzy logic control has the advantage of dealing with uncertain inputs, which are common in autonomous vehicles. This paper proposes a novel method for the steering control of autonomous vehicles based on type-2 fuzzy logic control combined with PI control. The primary control, type-2 fuzzy logic control, has three inputs—distance, navigation, and speed. The fuzzy system’s output is the steering angle value. This was used as input for the secondary control, PI control. This control is in charge of adjusting the motor’s position as a manifestation of the steering angle. The study results applied to the EPS system of autonomous vehicles revealed that type-2 fuzzy logic control and PI control produced better and smoother control than type-1 fuzzy logic control and PI. The slightest disturbance in the type-1 fuzzy logic control showed a significant change in steering, while this did not occur in the type-2 fuzzy logic control. The results indicate that type-2 fuzzy logic control and PI control could be used for autonomous vehicles by maintaining the comfort and safety of the users.

Drivers who lack sufficient experience would be unable to achieve handling stability due to the variation and dynamics of the combined vehicles (CVs). Drivers face hurdles in the stabilization attempt once these vehicles are rendered unstable. In this investigation, the use of the behavior of real vehicles to track the desired properties of the developed combined vehicles can help maintain good handling stability despite the present varying dynamics. This paper provides an appropriate design method for CVs to gain suitable handling property for such vehicles. The developed adaptive steering controller (ASC) allows the tracking of the desired vehicle by the real vehicle, despite the variation of parameter and lack of information of the real vehicle. Simulation results are obtained to validate that the handling stability was improved by using one design parameter, which minimizes frequency oscillation caused in the wheel steering angles. The introduction of a driver model that can simulate the real vehicle demonstrated that the adoption of the ASC is useful in the driver-tractor-semitrailer system. [ABSTRACT FROM AUTHOR]

Human drivers have rich and diverse driving characteristics on curved roads. Finding the characteristic quantities of the experienced drivers during curve driving and applying them to the steering control of autonomous vehicles is the research goal of this article. We first recruited 10 taxi drivers, 5 bus drivers, and 5 driving instructors as the representatives of experienced drivers and conducted a real car field experiment on six curves with different lengths and curvatures. After processing the collected driving data in the Frenet frame and considering the free play of a real car's steering system, it was interesting to observe that the shape enclosed by steering wheel angles and the coordinate axis was a trapezoid. Then, we defined four feature points, four feature distances, and one feature steering wheel angle, and the trapezoidal steering wheel angle (TSWA) model was developed by backpropagation neural network with the inputs were vehicle speeds at four feature points, and road curvature and the outputs were feature distances and feature steering wheel angle. The comparisons between TSWA model and experienced drivers, model predictive control, and preview-based driver model showed that the proposed TSWA model can best reflect the steering features of experienced drivers. What is more, the concise expression and human-like characteristic of TSWA model make it easy to realize human-like steering control for autonomous vehicles. Lastly, an autonomous vehicle composed of a nonlinear vehicle model and electric power steering (EPS) system was established in Simulink, the steering wheel angles generated by TSWA model were tracked by EPS motor directly, and the results showed that the EPS system can track the steering angles with high accuracy at different vehicle speeds. [ABSTRACT FROM AUTHOR]

In this paper, a strategy based on model predictive control consisting of path planning and path tracking is designed for obstacle avoidance steering control problem of the unmanned ground vehicle. The path planning controller can reconfigure a new obstacle avoidance reference path, where the constraint of the front-wheel-steering angle is transformed to formulate lateral acceleration constraint. The path tracking controller is designed to realize the accurate and fast following of the reconfigured path, and the control variable of tracking controller is steering angle. In this work, obstacles are divided into two categories: static and dynamic. When the decision-making system of the unmanned ground vehicle determines the existence of static obstacles, the obstacle avoidance path will be generated online by an optimal path reconfiguration based on direct collocation method. In the case of dynamic obstacles, receding horizon control is used for real-time path optimization. To decrease online computation burden and realize fast path tracking, the tracking controller is developed using the continuous-time model predictive control algorithm, where the extended state observer is combined to estimate the lumped disturbances for strengthening the robustness of the controller. Finally, simulations show the effectiveness of the proposed approach in comparison with nonlinear model predictive control, and the CarSim simulation is presented to further prove the feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

When negotiating bends car drivers perform gaze polling: their gaze shifts between guiding fixations (GFs; gaze directed 1–2 s ahead) and look-ahead fixations (LAFs; longer time headway). How might this behavior change in autonomous vehicles where the need for constant active visual guidance is removed? In this driving simulator study, we analyzed this gaze behavior both when the driver was in charge of steering or when steering was delegated to automation, separately for bend approach (straight line) and the entry of the bend (turn), and at various speeds. The analysis of gaze distributions relative to bend sections and driving conditions indicate that visual anticipation (through LAFs) is most prominent before entering the bend. Passive driving increased the proportion of LAFs with a concomitant decrease of GFs, and increased the gaze polling frequency. Gaze polling frequency also increased at higher speeds, in particular during the bend approach when steering was not performed. LAFs encompassed a wide range of eccentricities. To account for this heterogeneity two sub-categories serving distinct information requirements are proposed: mid-eccentricity LAFs could be more useful for anticipatory planning of steering actions, and far-eccentricity LAFs for monitoring potential hazards. The results support the idea that gaze and steering coordination may be strongly impacted in autonomous vehicles.

When negotiating bends car drivers perform gaze polling: their gaze shifts between guiding fixations (GFs; gaze directed 1–2 s ahead) and look-ahead fixations (LAFs; longer time headway). How might this behavior change in autonomous vehicles where the need for constant active visual guidance is removed? In this driving simulator study, we analyzed this gaze behavior both when the driver was in charge of steering or when steering was delegated to automation, separately for bend approach (straight line) and the entry of the bend (turn), and at various speeds. The analysis of gaze distributions relative to bend sections and driving conditions indicate that visual anticipation (through LAFs) is most prominent before entering the bend. Passive driving increased the proportion of LAFs with a concomitant decrease of GFs, and increased the gaze polling frequency. Gaze polling frequency also increased at higher speeds, in particular during the bend approach when steering was not performed. LAFs encompassed a wide range of eccentricities. To account for this heterogeneity two sub-categories serving distinct information requirements are proposed: mid-eccentricity LAFs could be more useful for anticipatory planning of steering actions, and far-eccentricity LAFs for monitoring potential hazards. The results support the idea that gaze and steering coordination may be strongly impacted in autonomous vehicles. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive steering robust nonlinear control method based on driver behavior is presented for an unmanned driving robotic vehicle (UDRV), in order to achieve accurate and stable steering control of UDRV. Considering modeling uncertainties and unknown nonlinear external disturbances, a UDRV nonlinear dynamics model is established. A driver behavior model is established considering a coordinate transformation model, a driver virtual path planning model, and a driver desired yaw rate model. On the basis of this, an adaptive steering robust nonlinear controller for UDRV is presented, consisting of an adaptive robust backstepping controller and a nonlinear disturbance observer (NDO). The NDO is designed to compensate for modeling uncertainties and unknown nonlinear external disturbances. The adaptive steering robust nonlinear controller for UDRV is designed through backstepping method and NDO compensation. The stability of control system is proved. Finally, double lane change experiments are conducted. Comparison analysis results among the proposed control method, other existed control method, and human driver demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Steer-by-wire is the electrical steering systems on vehicles that are expected with the development of an optimal control system can improve the dynamic performance of the vehicle. This paper aims to optimize the control systems, namely Fuzzy Logic Control (FLC) and the Proportional, Integral and Derivative (PID) control on the vehicle steering system using Imperialist Competitive Algorithm (ICA). The control systems are built in a cascade, FLC to suppress errors in the lateral motion and the PID control to minimize the error in the yaw motion of the vehicle. FLC is built has two inputs (error and delta error) and single output. Each input and output consists of three Membership Function (MF) in the form of a triangular for language term "zero" and two trapezoidal for language term "negative" and "positive". In order to work optimally, each MF optimized using ICA to get the position and width of the most appropriate. Likewise, in the PID control, the constant at each Proportional, Integral and Derivative control also optimized using ICA, so there are six parameters of the control system are simultaneously optimized by ICA. Simulations performed on vehicle models with 10 Degree Of Freedom (DOF), the plant input using the variables of steering that expressed in the desired trajectory, and the plant outputs are lateral and yaw motion. The simulation results showed that the FLC-PID control system optimized by using ICA can maintain the movement of vehicle according to the desired trajectory with lower error and higher speed limits than optimized with Particle Swarm Optimization (PSO).

Abstract: Improvements in vehicle safety require understanding of the neural systems that support the complex, dynamic task of real‐world driving. We used functional near infrared spectroscopy (fNIRS) and pupilometry to quantify cortical and physiological responses during a realistic, simulated driving task in which vehicle dynamics were manipulated. Our results elucidate compensatory changes in driver behavior in response to changes in vehicle handling. We also describe associated neural and physiological responses under different levels of mental workload. The increased cortical activation we observed during the late phase of the experiment may indicate motor learning in prefrontal–parietal networks. Finally, relationships among cortical activation, steering control, and individual personality traits suggest that individual brain states and traits may be useful in predicting a driver's response to changes in vehicle dynamics. Results such as these will be useful for informing the design of automated safety systems that facilitate safe and supportive driver–car communication. [ABSTRACT FROM AUTHOR]

Objective: The aim of this study was to investigate steering control in a low-cost driving simulator with and without a virtual vehicle cab.Background: In low-cost simulators, the lack of a vehicle cab denies driver access to vehicle width, which could affect steering control, insofar as locomotor adjustments are known to be based on action-scaled visual judgments of the environment.Method: Two experiments were conducted in which steering control with and without a virtual vehicle cab was investigated in a within-subject design, using cornering and straight-lane-keeping tasks.Results: Driving around curves without vehicle cab information made drivers deviate more from the lane center toward the inner edge in right (virtual cab = 4 ± 19 cm; no cab = 42 ± 28 cm; at the apex of the curve, p < .001) but not in left curves. More lateral deviation from the lane center toward the edge line was also found in driving without the virtual cab on straight roads (virtual cab = 21 ± 28 cm; no cab = 36 ± 27 cm; p < .001), whereas driving stability and presence ratings were not affected. In both experiments, the greater lateral deviation in the no-cab condition led to significantly more time driving off the lane.Conclusion: The findings strongly suggest that without cab information, participants underestimate the distance to the right edge of the car (in contrast to the left edge) and thus vehicle width. This produces considerable differences in the steering trajectory.Application: Providing a virtual vehicle cab must be encouraged for more effectively capturing drivers' steering control in low-cost simulators. [ABSTRACT FROM AUTHOR]

In this article, a new solution to the steering control problem of nonholonomic systems, which are transformable into chained form is investigated. A smooth super twisting sliding mode control technique is used to steer nonholonomic systems. Firstly, the nonholonomic system is transformed into a chained form system, which is further decomposed into two subsystems. Secondly, the second subsystem is steered to the origin by using smooth super twisting sliding mode control. Finally, the first subsystem is steered to zero using signum function. The proposed method is tested on three nonholonomic systems, which are transformable into chained form; a two-wheel car model, a model of front-wheel car, and a fire truck model. Numerical computer simulations show the efectiveness of the proposed method when applied to chained form nonholonomic systems. [ABSTRACT FROM AUTHOR]

Preceding vehicle following control system has been investigated widely. In many previous studies, the vehicular gap is assumed to be short, so the following vehicle can travel stably only by using heading angle toward preceding vehicle. However, the vehicle gap sometimes becomes longer at the complex surroundings such as intersection, so there is a problem that the following vehicle tends to travel along a shortcut path. Therefore, the preceding vehicle trajectory needs to be recognized correctly, but it is impossible because the system can use only current preceding vehicle position. We proposed the algorithm of the preceding vehicle trajectory generation. In the algorithm, the relative preceding vehicle position that is recognized at each period is hold. And each position is converted into the coordinate fixed to the present ego-vehicle. After that point sequence is generated by each position data. By using least squares method, the preceding vehicle point sequence is approximated by a curve line as the preceding vehicle trajectory. By using the trajectory as the target path for course following control, it becomes possible to follow the preceding vehicle regardless of the vehicular gap. Moreover, the validity of the proposed method is confirmed by the experiment conducted with an actual vehicle.

Steering control for an autonomous underwater glider (AUG) is very challenging due to its changing dynamic characteristics such as payload and shape. A good choice to solve this problem is online system identification via in-field trials to capture current dynamic characteristics for control law reconfiguration. Hence, an online polynomial estimator is designed to update the yaw dynamic model of the AUG, and an adaptive model predictive control (MPC) controller is used to calculate the optimal control command based on updated estimated parameters. The MPC controller uses a quadratic program (QP) to compute the optimal control command based on a user-defined cost function. The cost function has two terms, focusing on output reference tracking and move suppression of input, respectively. Move-suppression performance can, at some level, represent energy-saving performance of the MPC controller. Users can balance these two competitive control performances by tuning weights. We have compared the control performance using the second-order polynomial model to that using the fifth-order polynomial model, and found that the former cannot capture the main characteristics of yaw dynamics and may result in vibration during the flight. Both processor-in-loop (PIL) simulations and in-lake tests are presented to validate our steering control performance. [ABSTRACT FROM AUTHOR]