Four-wheel Independent Steering Research Articles

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

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

Stability coordinated control strategy of autonomous unmanned 4WID-4WIS vehicle based on APD-LQR and AFSMC

To improve the four-wheel independent driving and four-wheel independent steering (4WID-4WIS) autonomous unmanned vehicles’ active safety performance at their maximum operating capacity, the control of path following, four-wheel steering (4WS) and lateral stability should be coordinated. This paper introduces a hierarchical coordinated control framework, in which several independent controllers collaborate to enhance the overall performance of the vehicle system. Initially, in the upper controller, an adaptive preview distance linear quadratic regulator (APD-LQR) path following algorithm is proposed, considering the dynamic characteristics of the tire. It aimed at making the following error converge and obtaining the optimal front wheel steering angle. Meanwhile, an adaptive fuzzy sliding mode control (AFSMC) algorithm is implemented for obtaining additional yaw moment. It aimed at coordinating and improving effectively path following performance and lateral stability. Using the phase plane approach, the sliding surface is dynamically modified. Subsequently, in the lower controller, a rear wheel angle controller based on vehicle speed is proposed. It aimed at further enhancing vehicle’s maneuverability and stability by fully utilizing its steering redundancy features. Moreover, a torque distribution controller establishes a comprehensive cost function and then the NSGA-III algorithm is employed to optimize the torque distribution coefficients. Finally, simulation and Hardware-in-Loop (HIL) test results demonstrates that the proposed coordination algorithm can fully utilize the redundancy features of the 4WID-4WIS vehicle, and significantly enhances the vehicle’s path following accuracy and lateral stability, especially under extreme driving conditions.

Unified Fault-Tolerant Control and Adaptive Velocity Planning for 4WID-4WIS Vehicles under Multi-Fault Scenarios

Four-wheel independent drive and four-wheel independent steering (4WID-4WIS) vehicles provide increased redundancy in fault-tolerant control (FTC) schemes, enhancing heterogeneous fault-tolerant capabilities. This paper addresses the challenge of maintaining vehicle safety and maneuverability in the presence of actuator faults in autonomous vehicles, focusing on 4WID-4WIS systems. A novel unified hierarchical active FTC strategy is proposed to handle various actuator failures. The strategy includes an upper-layer motion controller that determines resultant force requirements based on trajectory tracking errors and a middle-layer allocation system that redistributes tire forces to fault-free actuators using fault information. This study, for the first time, considers multi-fault scenarios involving longitudinal and lateral coupling, calculating FTC boundaries for each fault type. Additionally, a fault tolerance index is introduced for 256 fault scenarios, using singular value decomposition to linearly represent the vehicle attainable force domain. Based on this, an adaptive velocity planning strategy is developed to balance safety and maneuverability under fault conditions. Matlab 2021a/Simulink and Carsim 2019 co-simulation results validate the proposed strategies, demonstrating significant improvements in fault-tolerant performance, particularly in complex and emergency scenarios.

Model predictive control based integrated fault tolerant controller for autonomous vehicles with four-wheel independent steering, driving and braking systems

This paper presents a fault-tolerant control algorithm for path and velocity tracking of an autonomous vehicle based on four-wheel independent steering, driving, and braking systems. The proposed algorithm is designed to distribute the control effort to normal actuators in the occurrence of faults. The proposed algorithm consists of three modules. The reference velocity and yaw rate to track the reference path are determined by the reference state decision module. The model predictive control (MPC)-based fault-tolerant controller determines the optimal inputs to compensate for the effect of the faulty actuator and maintain the tracking performance. A faulty-factor estimator is proposed based on a recursive covariance estimator to evaluate the performance degradation due to the failure of each actuator. The estimated faulty factors are applied to the objective function of the MPC such that the control input is distributed based on the level of the fault. The performance of the proposed algorithm was verified via performing simulations using MATLAB/Simulink and CarSim. The simulations proved that the proposed controller maintained the control performance of the normal case even in multiple actuator failures.

Research on ground mobile robot trajectory tracking control based on MPC and ANFIS

This study focuses on the control strategy for a ground mobile robot (GMR) with independent three-axis six-wheel drive and four-wheel independent steering, performing double lane change trajectory tracking in complex scenarios. Initially, a dynamic model of the six-wheel independent drive and steering GMR was constructed. Utilizing Model Predictive Control (MPC) technology, the challenge of trajectory tracking at low speeds was effectively addressed. For high-speed conditions, by thoroughly analyzing the impact of the predictive time-domain, this study innovatively introduced an Adaptive Neuro-Fuzzy Inference System (ANFIS) to dynamically adjust the prediction horizon of the MPC. A novel trajectory tracking algorithm integrating MPC and ANFIS was developed, with the network structure being trained using backpropagation (BP) method and the least squares method. Compared to traditional MPC, this hybrid strategy significantly improves trajectory tracking accuracy and stability at high speeds, with computational efficiency increased by 48.65%. Additionally, the algorithm demonstrated excellent adaptability and control effectiveness in various rigorous tests, including different speed levels, complex steering paths, load changes, sudden obstacles, and variable terrain. A 70 km/h trajectory tracking experiment on a physical vehicle yielded a root mean square (RMS) error of 0.1904 m, verifying its superior tracking performance and practical reliability. This provides a pioneering solution for high-performance trajectory control of ground mobile robots.

Analysis of consumer preferences for new electric vehicle technologies: Can future vehicle steering system steer consumer's purchase intention?

As the mainstream of vehicle industry is moving from internal combustion engine vehicles to electric vehicles (EVs), a multi-perspective analysis of consumer preference for the new EV technology is required. Therefore, this study proposes a new framework for consumer preference analysis—a two-stage choice experiment—and conducts an empirical analysis of the four-wheel independent steering system (4-WISS). In the first stage, we analyzed consumer preference for the availability of 4-WISS in EVs. In the second stage, we investigated consumer preference for the specific technology of 4-WISS in EVs. Stated preference data were collected through a choice experiment survey, and a mixed logit model was used to quantitatively analyze consumer preference and willingness-to-pay. Results show that consumers are willing to pay KRW 19,045,098.6 (USD 13,513.9) to use 4-WISS in high-end EVs. Furthermore, consumers who are female, older, and have more knowledge of autonomous vehicles prefer 4-WISS more. We found that consumers prefer the parallel movement among 4-WISS modes, the touch display for selecting 4-WISS, the steering wheel for operating 4-WISS mode, and dashboard for displaying information. We finally discuss the new business model in the vehicle industry and suggest automakers to establish an effective strategy for the initial market for EVs with 4-WISS.

Path-following control of 4WIS/4WID autonomous vehicles considering vehicle stability based on phase plane

In order to ensure the following accuracy and improve the operational stability of four-wheel independent driving and four-wheel independent steering autonomous vehicles, this paper proposes a path-following control strategy based on the β−[Formula: see text] phase plane. First, based on the kinematic relationship between the vehicle and the reference path, the linear matrix inequality theory is used to design the H∞ controller to obtain the wheel steering angle. Then, the vehicle steering system is subjected to nonlinear analysis according to phase plane theory, and a partition region controller is designed. In the unstable region, the instability degree of the vehicle is predicted by quadratic polynomial extrapolation and the particle swarm optimization PID controller is designed to determine the required yaw moment to restore the vehicle to the stable region. In the stable region, a fuzzy sliding mode controller is adopted to determine the required yaw moment so that the actual state variable of the vehicle follows the ideal state variable. Finally, the optimal tire force distributor is designed such that the required forces are allocated to all four wheels. The simulation results show that the proposed method can obtain excellent path-following performance and stability performance under different driving conditions.

Tire–Road Friction Estimation for Four-Wheel Independent Steering and Driving EVs Using Improved CKF and FNN

Tire–Road Friction Estimation for Four-Wheel Independent Steering and Driving EVs Using Improved CKF and FNN

Coordinated Control for the Trajectory Tracking of Four-Wheel Independent Drive–Four-Wheel Independent Steering Electric Vehicles Based on the Extension Dynamic Stability Domain

In order to achieve multi-objective chassis coordination control for 4WID-4WIS (four-wheel independent drive–four-wheel independent steering) electric vehicles, this paper proposes a coordinated control strategy based on the extension dynamic stability domain. The strategy aims to improve trajectory tracking performance, handling stability, and economy. Firstly, expert PID and model predictive control (MPC) are used to achieve longitudinal speed tracking and lateral path tracking, respectively. Then, a sliding mode controller is designed to calculate the expected yaw moment based on the desired vehicle states. The extension theory is applied to construct the extension dynamic stability domain, taking into account the linear response characteristics of the vehicle. Different coordinated allocation strategies are devised within various extension domains, providing control targets for direct yaw moment control (DYC) and active rear steering (ARS). Additionally, a compound torque distribution strategy is formulated to optimize driving efficiency and tire adhesion rate, considering the vehicle’s economy and stability requirements. The optimal wheel torque is calculated based on this strategy. Simulation tests using the CarSim/Simulink co-simulation platform are conducted under slalom test and double-lane change to validate the control strategy. The test results demonstrate that the proposed control strategy not only achieves good trajectory tracking performance but also enhances handling stability and economy during driving.

Active Fault-Tolerant Control of a Four-Wheel Independent Steering System Based on the Multi-Agent Approach

In this paper, we present an active fault-tolerant, vehicle motion control, based on the multi-agent approach for a four-wheel independent steering system. This improves handling stability and active safety of the vehicle in the case of actuator faults. Firstly, an eight-degree-of-freedom four-input vehicle model is established by using the vector transformation method, which takes the four-wheel angle as the input and the yaw rate and the sideslip angle, obtained by the lateral force of a single wheel, as the outputs. Then, we established the topological structure of the four-wheel independent steering subsystems based on graph theory. Aiming at the known fault conditions, an adaptive sliding mode active fault-tolerant control based on the multi-agent four-wheel independent steering system is proposed, in which the unmodeled part of the system is estimated using adaptive methods. For unknown fault conditions, an active fault-tolerant control of the four-wheel independent steering system based on state observer is proposed. An extended state observer with sliding mode surface function as a state variable is designed to estimate actuator faults. Finally, it was demonstrated that the designed state observer and distributed active fault-tolerant control method reduced the impact of actuator failures on vehicle steering and improved the stability of the steering process based on the Lyapunov theory. The hardware in the loop experimental results validate the effectiveness of the proposed novel method.

Instantaneous Center of Rotation Tracking Control of Four-Wheel Independent Steering Vehicles Under Large-Curvature Turning Conditions

Instantaneous Center of Rotation Tracking Control of Four-Wheel Independent Steering Vehicles Under Large-Curvature Turning Conditions

A Novel Real-Time Robust Controller of a Four-Wheel Independent Steering System for EV Using Neural Networks and Fuzzy Logic

In this study a four-wheel independent steering (4WIS) system for an electric vehicle (EV) steered by stepper motors is presented as a revolutionary real-time control technique employing neural networks in combination with fuzzy logic, where the use of the neural network greatly simplifies the computational process of fuzzy logic. The control of the four wheels is based on a variation of a Hopfield Neural Network (VHNN) method, in which the input is the error of each steering motor and the output is processed by a hyperbolic tangent function (HTF) feeding the fuzzy logic controller (FLC), which ultimately drives the stepper motor. The whole system consists of the four aforementioned blocks which work in sync and are inseparable from each other with the common goal of driving all the steering stepper motors at the same time. The novelty of this system is that each wheel monitors the condition of the others, so even in the case of the failure of one wheel, the vehicle does not veer off course. The results of the simulation show that the suggested control system is very resilient and workable at all angles and speeds.

Reference Generator for a Platoon of Position-Controlled Vehicles on a Curved Path

<div>Vehicular automation in the form of a connected and automated vehicle platoon is demanding as it aims to increase traffic flow and driver safety. Controlling a vehicle platoon on a curved path is challenging, and most solutions in the existing literature demonstrate platooning on a straight path or curved paths at constant speeds. This article proposes an algorithmic solution with leader-following (LF) communication topology and constant distance (CD) spacing for platooning homogeneous position-controlled vehicles (PCVs) on a curved path, with each vehicle capable of cornering at variable speeds. The lead vehicle communicates its reference position and orientation to all the follower vehicles. A follower vehicle stores this information as a virtual trail of the lead vehicle for a specific period. An algorithm uses this trail to find the follower vehicle’s reference path by solving an optimization problem. This algorithm is feasible and maintains a constant inter-vehicle distance. The PCVs can be holonomic or nonholonomic. For simulations, this article considers a holonomic four-wheel independent steering four-wheel independent drive (4WIS4WID) PCV for platooning. This vehicle has superior maneuverability and traction and can extend the applications of vehicle platoons from highways to paths with smaller radii of curvature. Simulation of a five-vehicle platoon suggests a satisfactory performance of the proposed approach. This article also presents an alternate curved platooning approach where the lead vehicle communicates its reference longitudinal and lateral velocities and yaw rate to a follower vehicle. The follower vehicle directly follows these communicated signals for platooning. This approach does not store the communicated signals and also cuts the cost of the position controller for the follower vehicles. Simulation results show that this alternative approach is applicable to constant-speed motion.</div>

Trajectory Tracking Coordinated Control of 4WID-4WIS Electric Vehicle Considering Energy Consumption Economy Based on Pose Sensors

In order to improve the stability and economy of 4WID-4WIS (four-wheel independent drive—four-wheel independent steering) electric vehicles in trajectory tracking, this paper proposes a trajectory tracking coordinated control strategy considering energy consumption economy. First, a hierarchical chassis coordinated control architecture is designed, which includes target planning layer, and coordinated control layer. Then, the trajectory tracking control is decoupled based on the decentralized control structure. Expert PID and Model Predictive Control (MPC) are employed to realize longitudinal velocity tracking and lateral path tracking, respectively, which calculate generalized forces and moments. In addition, with the objective of optimal overall efficiency, the optimal torque distribution for each wheel is achieved using the Mutant Particle Swarm Optimization (MPSO) algorithm. Additionally, the modified Ackermann theory is used to distribute wheel angles. Finally, the control strategy is simulated and verified using Simulink. Comparing the control results of the average distribution strategy and the wheel load distribution strategy, it can be concluded that the proposed coordinated control not only provides good trajectory tracking but also greatly improves the overall efficiency of the motor operating points, which enhances the energy economy and realizes the multi-objective coordinated control of the chassis.

Control Interface for Next Generation Vehicles: What Is the Best Way to Drive Four-Wheel Independent Steering Vehicles?

Four-wheel independent steering (4WIS) vehicles refer to vehicles designed to be able to move in multi-orientation by eliminating the mechanical linkage among the wheels. This study aims to evaluate and compare the usability of previously proposed 4WIS vehicle controllers. A usability test with 33 drivers was conducted to compare the five control interfaces (steering wheel buttons, touchscreen buttons, center console buttons, joystick, and paddle shifters). In order to identify what factors contributes to the usability of 4WIS control interfaces, we utilized a mixed-method approach that collects and analyzes both quantitative and qualitative data within the study. The quantitative results indicate that button-type interfaces shorten task completion time and reduce workload than joystick or paddle shifters. Qualitative analysis implies that touchscreen buttons reduce the perceived safety of drivers due to the lack of tactile feedback. Design recommendations for 4WIS control interfaces were provided from the usability testing results.

Integrated Path Tracking and Lateral Stability Control with Four-Wheel Independent Steering for Autonomous Electric Vehicles on Low Friction Roads

This paper presents a method to design an integrated path tracking and lateral stability controller for an autonomous electric vehicle with four-wheel independent steering (4WIS) on low friction roads. Recent advances in autonomous driving have led to extensive studies on path tracking control. However, path tracking is difficult on low friction roads. In this paper, path tracking control was converted to the yaw rate tracking one to cope with problems caused by low friction roads. To generate a reference yaw rate for path tracking, we present several methods using a driver model and a target path. For yaw rate tracking, we designed a controller with a two-layer control hierarchy, i.e., upper and lower layers. The control yaw moment was calculated using a direct yaw moment controller in the upper layer. In the low layer, a control allocation method was adopted to allocate the control yaw moment into steering angles of 4WIS. To verify the performance of the proposed controller, we conducted a simulation on vehicle simulation software. From the simulation results, it is shown that the proposed controller is effective for path tracking and lateral stability on low friction roads. To analyze path tracking and lateral stability performance of the proposed controller on low friction roads, the effects of the steady-state yaw rate gain are investigated from the simulation results.

Path Tracking Control With Four-Wheel Independent Steering, Driving and Braking Systems for Autonomous Electric Vehicles

This paper presents a method to design a path tracking controller with four-wheel independent braking (4WIB), drive (4WID) and steering (4WIS) systems equipped in in-wheel motor-driven electric vehicles (IWM-EVs). Generally, it is difficult to calculate the steering angles of 4WIS and the braking/traction torques of 4WIB/4WID for path tracking control. Moreover, there have been limitations of an error dynamics-based path tracking controller, which requires assumptions on a target path. To cope with these problems, the path tracking problem on a target path is converted into the yaw rate tracking one with a reference yaw rate in this paper. Two methods are adopted for the purpose of calculating a reference yaw rate. The first is to use a pure pursuit method, which generates a steering angle for path tracking. From the steering angle, a reference yaw rate is calculated. The second is to derive a reference yaw rate from a target path and a current vehicle position. For yaw rate tracking, direct yaw moment control is adopted to generate a control yaw moment. A control allocation method is adopted to distribute a control yaw moment into tire forces, generated by 4WIS, 4WID and 4WIB. Several actuator combinations are represented by various sets of virtual weights in the control allocation method. A simulation with a vehicle simulation program, CarSim®, shows that the proposed path tracking controller is effective in enhancing the path tracking performance with 4WIS, 4WID and 4WIB. From the simulation, effects of actuator combinations on path tracking performance are analyzed.

Wheel Deflection Control of Agricultural Vehicles with Four-Wheel Independent Omnidirectional Steering

Due to the harsh working environment of wheeled agricultural vehicles in the field, it is difficult to ensure that all wheels make contact with the ground at the same time, which is easy to unequally distribute the yaw moments of each independent wheel. The commonly used vehicle lateral control methods are mostly controlled by coordinating the individual torque between different wheels. Obviously, this control method is not suitable for agricultural four-wheeled vehicles. The goal of this study was to provide a wheel steering angle control method that uses electric push rods as actuators that can cope with this problem. The design of a four-wheel steering controller generally adopts the linear PID control method, but the research object of this paper is difficult to establish an accurate and linear mathematical model due to the complex working environment. Therefore, fuzzy adjustment is added on the basis of PID control, which can meet the requirements of model difficulty and control accuracy at the same time. In order to verify the feasibility and rationality of the designed wheel steering mechanism, the model dynamics simulation based on ADAMS software and the response analysis of the electric linear actuator thrust were completed. Based on the kinematics model of the controlled object, the rotation angle of the actuator motor is used as the control target, the lateral deviation e and deviation variation ec are taken as input variables and the parameters KP, KI and KD are taken as output variables, thereby establishing a fuzzy PID controller. Then, this controller is constructed in the Matlab/ Simulink simulation environment to analyze the lateral deviation and response stability during the process of vehicle path tracking. From the verification results of the linear path walking test under the fuzzy PID control method, the maximum lateral deviation of vehicle chassis is 2.7 cm when the driving speed is set as 1 m/s, and the deviation adjustment stable time of the system is 0.15 s. It can be seen that the proposed steering control strategy has good response performance and effectively increases the steering stability.

Model Predictive Control-Based Integrated Path Tracking and Velocity Control for Autonomous Vehicle with Four-Wheel Independent Steering and Driving

This paper presents an MPC-based integrated control algorithm for an autonomous vehicle equipped with four-wheel independent steering and driving systems. The objective of this research is to improve the performance of the path and velocity tracking controllers by distributing the control effort to the multiple actuators. The proposed algorithm has two modules: reference state decision and MPC-based vehicle motion controller. Reference state decision module determines reference state profiles consisting of yaw rate and velocity in order to overcome the limitation of the error dynamics-based path tracking controller, which requires several assumptions on the reference path. The MPC-based vehicle motion controller is designed with a linear time-varying vehicle model in order to optimally allocate the control effort to each actuator. A linear time-varying MPC is adopted to reduce computational burden caused by using a non-linear one. The effectiveness of the proposed algorithm is validated via simulation on MATLAB/Simulink and CarSim. The simulation results show that the proposed algorithm improves the reference tracking performance by effectively distributing the control effort to the steering angle and driving force of each actuator.

Trajectory-following of a 4WID-4WIS vehicle via feedforward–backstepping sliding-mode control

Realisation of high-precision trajectory tracking is the key technology to achieve unmanned driving, which has an important impact on vehicle handling stability, safety and comfort. However, many confounding factors seriously restrict tracking performance and pose a great challenge to the design of the controller for tracking the desired trajectory. A comprehensive method that combines feedforward and backstepping sliding mode control is proposed for a four-wheel independent driving–four-wheel independent steering (4WID-4WIS) vehicle, which has the advantages of over-coupling, multiple degrees of freedom and flexible operation. The desired value is the target for the feedforward output of the controller, and the backstepping sliding mode control is used to overcome all kinds of disturbances. A mature and reliable Luenberger observer is designed to achieve good trajectory tracking performance for reducing some sensors. The proposed method is verified via MATLAB/Simulink simulation, which proved that the method has an excellent trajectory tracking performance.