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The variable steering transmission ratio was a crucial factor affecting the active safety and handling stability of vehicles. In order to enhance the steering characteristics of steering-by-wire vehicles on low-adhesion coefficient road surfaces, a variable gain transmission ratio that adapts to changes in road adhesion coefficient and vehicle speed was designed. A 2 DOF model was established for the vehicle, to analyze the factors influencing the yaw rate gain, and to obtain the data relationship between the influencing factors and the gain through simulation. The Min-Max normalization method was utilized to preprocess the data between the influencing factors and the yaw rate gain, constructing a neural network dataset. Design a Snake Optimizer Backpropagation Neural Network (SO-BP) was desighed and train to use the preprocessed dataset to dynamically acquire the variable yaw rate gain. A strategy was employed to combines the variable yaw rate gain with the lateral acceleration gain in proportion to design the variable gain transmission ratio for electronic control steering. Simulink-CarSim was used to build a steer-by-wire steering whole vehicle model. Compare and analyze the designed variable gain transmission ratio was analized and compared with a traditional fixed gain transmission ratio under conditions of both high-adhesion coefficient road surfaces with a double lane change scenario and low-adhesion coefficient road surfaces with a step input scenario. Results indicated that with high-adhesion coefficient road conditions, the trajectory error of the two transmission ratio vehicles remained within 3%, while the variable gain transmission ratio vehicle reduced the peak steering wheel angle by 9.1%. With low-adhesion coefficient road conditions, the variable gain transmission ratio vehicle showed a 22.3% reduction in steady-state yaw rate at low to moderate speeds and a 24.6% reduction in peak yaw rate. At moderate to high speeds, the steady-state yaw rate decreased by 6.6%, and the peak yaw rate decreased by 10.8%. The variable gain transmission ratio not only enhanced steering sensitivity on high-adhesion coefficient road surfaces, but also improved safety and maneuverability when driving on low-adhesion coefficient road surfaces. [ABSTRACT FROM AUTHOR]

The automotive industry, with particular reference to the off-road sector, is facing several challenges, including the integration of Advanced Driver Assistance Systems (ADASs), the introduction of autonomous driving capabilities, and system-specific requirements that are different from the traditional car market. Current vehicular electrical–electronic (E/E) architectures are unable to support the amount of data for new vehicle functionalities, requiring the transition to zonal architectures, new communication standards, and the adoption of Drive-by-Wire technologies. In this work, we propose an automated methodology for next-generation off-road vehicle E/E architectural design. Starting from the regulatory requirements, we use a MILP-based optimizer to find candidate solutions, a discrete event simulator to validate their feasibility, and an ascent-based gradient method to reformulate the constraints for the optimizer in order to converge to the final architectural solution. We evaluate the results in terms of latency, jitter, and network load, as well as provide a Pareto analysis that includes power consumption, cost, and system weight. [ABSTRACT FROM AUTHOR]

Steer-by-wire is a key technology to realize full self-driving for intelligent vehicles, where this is a challenge for steering angle following accurately. Therefore, a hybrid control thinking is proposed to design a model-tuned predictive sliding control approach, which is utilized to realize angle following of the electric motor steer-by-wire system. Here, sliding mode control is used as a core algorithm to be fit for the dynamics characteristics of the steer-by-wire system. Model tuning control is proposed to update model parameters by a receding horizon method, which means that some posteriori knowledge of the control system is utilized to make the model more accurate. Model predictive control is used to optimize the sliding manifold parameters of sliding mode control, which means that some priori knowledge of the control system is used to make the control law much fitter. Then we discuss a series of studies on the steer-by-wire system model and the control algorithm that, collectively, develop an approach of how the hybrid control algorithm precisely makes the front wheel angles follow desired steering commands. The designed approach has been deployed into a steering control unit, and tested in a steering test vehicle to realize the angle following of the electric motor steer-by-wire system. Based on the experimental results and statistical analysis, it can be concluded that the hybrid control thinking is a good idea of how to fuse several algorithms into a control approach, and the model-tuned predictive sliding control approach is a good candidate for the steering angle following of full self-driving vehicles. [ABSTRACT FROM AUTHOR]

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In this article, a disturbance estimation–based robust generalized predictive control approach is proposed for angle demand control of a steer-by-wire system. It consists of a generalized predictive controller and a nonlinear disturbance observer. First, the nonlinear disturbance observer is used to estimate the effects of the lumped disturbances, including model uncertainties, unmodeled dynamics, and external disturbances. Second, the estimate of the nonlinear disturbance observer is integrated into the generalized predictive controller design for front-wheel angle output prediction. Then, an explicit analytical form of the disturbance estimation–based robust generalized predictive controller is derived. In addition, rigorous stability analysis proves that the closed-loop system under the proposed control is stable. Finally, the developed control approach has been downloaded into a steering control unit and tested in real-world conditions using a vehicle test bench to fully realize steering by wire in engineering practice. Extensive simulations and experiments have been performed, and results show that the proposed control approach outperforms the comparative controllers regarding disturbance rejection, robust tracking performance under model uncertainties, and disturbances. [ABSTRACT FROM AUTHOR]

The steer-by-wire system severs the mechanical link between the steering wheel and the steering gear. This configuration enhances the angular transmission characteristics. Entering the nonlinear region of the tires could result in a reduction in the vehicle's steering gain. In order to improve the comfort of vehicle steering operation, we have developed a variable transmission ratio controller for the steer-by-wire (SBW) system. This controller utilizes information on the vehicle speed and steering wheel angle to generate a variable transmission ratio coefficient, thereby adjusting the steering ratio. We introduce a multi-objective comprehensive evaluation index that takes into account vehicle lateral deviation, driver steering burden, vehicle stability, and safety. To harmonize the transmission ratio weights of constant steering gain, we employ the coefficient of variation method. Ultimately, a fuzzy neural network is employed to craft a nonlinear controller. We conducted steady-state circular motion tests, double lane-change tests, and step input tests to validate the performance of the variable transmission ratio control. The results suggest that, in comparison to conventional fixed transmission ratio systems, the variable transmission ratio control within the steer-by-wire system significantly alleviates the driver's operational burden while enhancing the vehicle's handling stability and safety. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper proposes a personalized steering feel model for the steer-by-wire vehicles to reconstruct the haptic feedback during steering motion. Through the investigation of the steering mechanism, the aligning torque obtained via the rack force estimated by the extended disturbance observer and the equivalent friction torque modelled using a hyperbolic tangent function constitute the steering feel model. Also, different mathematical functions are designed to achieve the characteristics of the power assist and the steering feel varying with vehicle speeds. The variable parameters are introduced to design the personalized steering feel and the objective evaluation is employed to evaluate the performance of the proposed model. The hardware-in-the-loop experiment results demonstrate that the designed extended disturbance observer has good rack force estimation performance. Compared with conventional electric power steering systems, the proposed model achieves the same haptic feedback and has the assist effect and the characteristics of steering feel that changes with speed. Furthermore, the personalized steering feel is realized by adjusting the variable parameters of the steering feel model according to different drivers' preferences. [ABSTRACT FROM AUTHOR]

The event-triggered tracking control is studied for the steer-by-wire (SbW) system with actuator fault, inaccurate dynamic model, and external disturbance. First, an adaptive sliding mode control based on event-triggering mechanism is proposed. The lumped nonlinearity, including friction torque and self-aligning torque of SbW systems, is approximated by an adaptive robust radial basis function neural network (RBFNN). Robust terms of sliding mode control attenuate the negative effects of the actuator fault, modeling error, external disturbance, and event-triggering error on control performance. More importantly, the sliding-mode high-frequency switching control appears in the high-order derivative of sliding variable without increasing the input–output relative degree, thereby eliminating chattering. Furthermore, the designed method can achieve the practical finite-time stability. The proposed event-triggering mechanism can strictly exclude Zeno behavior and economize the communication resources. Finally, simulations and experiments show the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This study concerns the comparative investigation of two advanced lateral stability automotive controllers with respect to a commercial solution. The research aims to improve the stability performances achieved by a combined tracking of yaw rate and side-slip angle through the application of optimal efforts. The proposed solutions are based on Linear Quadratic Regulation and Sliding Mode Control, respectively. Both rely on the same approach for the control objective definition but differ from the action perspective. This solution involves the adoption of a differential braking actuation technique to deliver a desired yaw moment to the car body to track controlled states. Indeed, a sliding controller can also traction torques of hub-motor configurations as well as steering corrections, achieving vehicle stability and a driving response in accordance with the pilot's intentions. Calibration and validation of the controllers are performed through a Hardware-in-the-Loop simulation rig, along with a real-time static simulator, performing different close-loop maneuvers to assess achievements in terms of lateral stability. Results show that both solutions ensure higher handling performances if compared to Non-controlled or Commercial-controlled vehicle scenarios. [ABSTRACT FROM AUTHOR]

This paper investigates the trajectory tracking control issue of four-wheel independently actuated electric vehicles (FWIA EVs) with steer-by-wire devices concerning parameter uncertainties, external disturbances, input saturation, and vehicle sideslip angle not easily obtained. A robust  H ∞  dynamic output feedback control strategy is proposed for the integrated control of the steering motor current and direct yaw moment without using sideslip angle information to ensure the reference trajectory tracking and the improvement of handling performance and yaw stability. In the proposed integration control framework, the steer-by-wire device dynamic is involved in the polyhedral linear parameter-varying (LPV) trajectory tracking error model considering the time-varying longitudinal velocity, and the norm-bounded parameter uncertainties such as road adhesion coefficient, tire cornering stiffness, vehicle mass, and vehicle moment of inertia. With the help of the LPV model of all the states of the steer-by-wire FWIA EV, a dynamic output feedback trajectory tracking controller is designed using the robust  H ∞  technique. The controller gain matrices are obtained by solving the linear matrix inequalities. Finally, the high-fidelity full-vehicle model based on the CarSim-MATLAB/Simulink joint simulation platform verifies the robustness and advantages of the designed control strategy in the accelerated lane-change scenario. [ABSTRACT FROM AUTHOR]

In recent years, the Steer-by-Wire (SBW) technology has been gaining popularity and replacing classical steering systems. It plays the most crucial role in autonomous cars where the vehicle must perform maneuvers on its own without driver’s intervention. One of the key components of this system is the steering wheel angle sensor (SAS). Its reliability and performance may affect driver’s life and health. The purpose of this paper is to show a test system to comprehensively evaluate the performance of the steering wheel angle sensor in the SBW system during real-world maneuvers and show how SAS parameters such as accuracy of angle, angular speed etc. affect car trajectory resulting in hit cones. For this purpose, a test system was built, with the use of virtual test drives based on CarMaker software, CANoe and VTSystem hardware. In order to evaluate its performance, the errors introduced by the system were determined. Additionally, using the realised test system, three commercial steering wheel angle sensors were tested and compared during a virtual test drive. Their errors were determined, as well as their performance in the SBW technology and the consistency of the obtained results with the parameters declared by the manufacturer were verified as well.

This paper focuses on the steering motion control of an in-wheel motor-drive robot. The influence of the pulse-width modulation (PWM) duty cycle on steering motion and the steering control method have not yet been proved. Thus, this study aimed to design a steering controller for the off-center steer-by-wire system of a robot. The influence of the PWM duty cycle on the steering motion under different conditions is firstly tested on a test bench. Based on the optimal duty cycles of different cases found in the test, a two-stage fuzzy controller of the duty cycle is designed for the steering system. The first stage of the controller is used to dynamically adjust the PWM duty cycle of the electromagnetic friction lock (EFL). The second stage is designed to realize the self-tuning of the fuzzy controller's quantization factor and the scale factor. Through two-stage control, the motion of the in-wheel motor and the EFL can be coordinated to realize stable and rapid steering. Considering the robots' primary application in field roads at present, road tests were ultimately conducted to verify the proposed method. The test results show that the angle response rate of the steering arm is elevated with the increase in the steering angle signal. The proposed controller can sensitively track the target angles with smaller overshoot, yaw rate and lateral acceleration, and better steering accuracy than the PID (proportional–integral–differential) controller under different working conditions. [ABSTRACT FROM AUTHOR]

The steer-by-wire system severs the mechanical link between the steering wheel and the steering gear. This configuration enhances the angular transmission characteristics. Entering the nonlinear region of the tires could result in a reduction in the vehicle’s steering gain. In order to improve the comfort of vehicle steering operation, we have developed a variable transmission ratio controller for the steer-by-wire (SBW) system. This controller utilizes information on the vehicle speed and steering wheel angle to generate a variable transmission ratio coefficient, thereby adjusting the steering ratio. We introduce a multi-objective comprehensive evaluation index that takes into account vehicle lateral deviation, driver steering burden, vehicle stability, and safety. To harmonize the transmission ratio weights of constant steering gain, we employ the coefficient of variation method. Ultimately, a fuzzy neural network is employed to craft a nonlinear controller. We conducted steady-state circular motion tests, double lane-change tests, and step input tests to validate the performance of the variable transmission ratio control. The results suggest that, in comparison to conventional fixed transmission ratio systems, the variable transmission ratio control within the steer-by-wire system significantly alleviates the driver’s operational burden while enhancing the vehicle’s handling stability and safety.

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.

In order to meet the needs of drivers with different driving styles and improve driving comfort and safety, a personalized design strategy of vehicle steer-by-wire (SBW) characteristics considering driving style is proposed in this paper. Firstly, based on the driver hardware-in-the-loop test bench, the driver's driving characteristic data is collected under double lane change condition. Then, the K-means clustering algorithm is adopted to classify the driver's driving style into cautious type, general type and radical type. Based on this, combined with the clustering results and grid search method, the support vector machine algorithm is used to construct the driving style identification model. Finally, considering the driving style, vehicle stability and steering wheel alignment characteristics, the driver personalized variable steering ratio strategy is designed based on the ideal transmission characteristics, and the personalized SBW performance is analyzed under step condition and sinusoidal condition. The results indicate that the proposed personalized design strategy of vehicle SBW characteristics considering driving style can not only accurately identify driving style, but also meet the needs of drivers with different driving styles for vehicle response. [ABSTRACT FROM AUTHOR]

Improving the performance of articulated vehicles is not only by completing the tractor systems, but also by intellectualizing the trailer links (TL), is a complex task. In this regard, the search for and justification of the implementation of controlled TL systems is an important process (Intelligent Trailer). The research has been carried for the purpose of development of the systemic approach from the idea schematization to the practical recommendations. The paper proposes a general approach for creating a comprehensive calculation model that establishes a link between the dynamics of movement of articulated vehicle and the active controlling systems of the semitrailer. The key moments is the developed mathematical model based on matrix technique that facilitates the creation of the universal simulation model in the Simulink environment. The created method with recommended programming product afforded to increase the exactness of modelling of curving way of articulated vehicle on 10 % under the diminishing of time on the calculation about 50 % in comparison with classical method. At the expense of experimental investigation there was confirmed appropriateness of proposed imitation model of movement dynamics of articulated vehicle, general relative mistake in comparison with theoretical investigations was not over than 5%. Additional combining to semitrailer chassis the active turning control system diminishes the size line of constant movement of articulated vehicle in the circle on 14,5% in comparison with semitrailer without this system, which is also positive from the point of safety of articulated vehicle maneuverability.

With the popularization of active safety systems, autonomous driving, and Steer-by-Wire (SBW) technology, steering wheel angle sensors (SAS) have begun to play a key role in these systems. It is responsible for measuring the angle and angular velocity of the steering wheel and sending these data via a bus to the cooperating devices. For this reason, automakers are making increasingly demanding requirements that are difficult for sensor suppliers to meet. These requirements describe the metrological properties of the sensor, but their influence on the quality of car control is not clear. It is impossible to directly determine whether systems that use these sensors work properly and efficiently. Therefore, the question that needs to be asked is whether and to what extent sensor parameters such as accuracy and data transmission period affect the Steer-by-Wire car’s trajectory and behavior during actual driving? For this purpose, a test bench was built based on the CarMaker and CANoe software using virtual test drives. It was used to perform a series of tests for different combinations of sensor accuracies and CAN frame periods, based on which errors were determined, and their influence on the number of knocked down cones during slalom performance.

In recent years, the Steer-by-Wire (SBW) technology has been gaining popularity and replacing classical steering systems. It plays the most crucial role in autonomous cars where the vehicle must perform maneuvers on its own without driver's intervention. One of the key components of this system is the steering wheel angle sensor (SAS). Its reliability and performance may affect driver's life and health. The purpose of this paper is to show a test system to comprehensively evaluate the performance of the steering wheel angle sensor in the SBW system during real-world maneuvers and show how SAS parameters such as accuracy of angle, angular speed etc. affect car trajectory resulting in hit cones. For this purpose, a test system was built, with the use of virtual test drives based on CarMaker software, CANoe and VTSystem hardware. In order to evaluate its performance, the errors introduced by the system were determined. Additionally, using the realised test system, three commercial steering wheel angle sensors were tested and compared during a virtual test drive. Their errors were determined, as well as their performance in the SBW technology and the consistency of the obtained results with the parameters declared by the manufacturer were verified as well. [ABSTRACT FROM AUTHOR]

Intelligent vehicles require the ability to switch freely between manual and automatic driving modes, which opens up more possibilities for steer-by-wire. Among them, the design of transmission ratio for both driving modes can improve the steering performance of vehicle. In the manual driving mode, this paper optimizes the yaw rate gain upon the particle swarm optimization (PSO) algorithm and designs a variable steering transmission ratio, which can reduce the driver's burden and improve the vehicle's handling stability. Further, the steering wheel angular rate information and fuzzy control are used to design the high-speed transmission ratio to ensure the safety of the vehicle in high-speed emergency situations. In the automatic driving mode, the transmission ratio values at various vehicle speeds are optimized offline based on the PSO algorithm, and a variable transmission ratio curve is designed to enhance the trajectory tracking ability and the vehicle stability. Finally, the hardware-in-the-loop test results show that the variable transmission ratio designed in the manual driving mode reduces the burden of the driver at low speed and improves the vehicle stability at high speed, especially in emergency situations. It also proves that the optimized design of variable ratio in the automatic driving mode promotes the path-following ability and the vehicle stability. [ABSTRACT FROM AUTHOR]

Rear-wheel steering has seen new popularity in recent years. Many explanations of rear-wheel steering systems use the concept of a 'virtual wheel base' to explain in-phase or out-of-phase steering. This paper introduces a novel reference model that realises this metaphor. A reference model is created where its wheelbase can be changed arbitrarily, making the resulting system intuitive and simpler to design and tune. This augments the properties of the reference model whose signals are used for a feedback control system. The real vehicle tracks these signals using active front and rear-wheel steering. This system can be implemented in a vehicle using steer-by-wire or an autonomous vehicle for control. A MIMO controller is derived and results show that the system operates as intended. Finally, a control law is proposed to change the virtual wheelbase using the road curvature. It is shown that it has advantages over conventional rear-wheel steering control. [ABSTRACT FROM AUTHOR]

This paper investigates the trajectory tracking control issue of four-wheel independently actuated electric vehicles (FWIA EVs) with steer-by-wire devices concerning parameter uncertainties, external disturbances, input saturation, and vehicle sideslip angle not easily obtained. A robust H∞ dynamic output feedback control strategy is proposed for the integrated control of the steering motor current and direct yaw moment without using sideslip angle information to ensure the reference trajectory tracking and the improvement of handling performance and yaw stability. In the proposed integration control framework, the steer-by-wire device dynamic is involved in the polyhedral linear parameter-varying (LPV) trajectory tracking error model considering the time-varying longitudinal velocity, and the norm-bounded parameter uncertainties such as road adhesion coefficient, tire cornering stiffness, vehicle mass, and vehicle moment of inertia. With the help of the LPV model of all the states of the steer-by-wire FWIA EV, a dynamic output feedback trajectory tracking controller is designed using the robust H∞ technique. The controller gain matrices are obtained by solving the linear matrix inequalities. Finally, the high-fidelity full-vehicle model based on the CarSim-MATLAB/Simulink joint simulation platform verifies the robustness and advantages of the designed control strategy in the accelerated lane-change scenario.

This study concerns the comparative investigation of two advanced lateral stability automotive controllers with respect to a commercial solution. The research aims to improve the stability performances achieved by a combined tracking of yaw rate and side-slip angle through the application of optimal efforts. The proposed solutions are based on Linear Quadratic Regulation and Sliding Mode Control, respectively. Both rely on the same approach for the control objective definition but differ from the action perspective. This solution involves the adoption of a differential braking actuation technique to deliver a desired yaw moment to the car body to track controlled states. Indeed, a sliding controller can also traction torques of hub-motor configurations as well as steering corrections, achieving vehicle stability and a driving response in accordance with the pilot’s intentions. Calibration and validation of the controllers are performed through a Hardware-in-the-Loop simulation rig, along with a real-time static simulator, performing different close-loop maneuvers to assess achievements in terms of lateral stability. Results show that both solutions ensure higher handling performances if compared to Non-controlled or Commercial-controlled vehicle scenarios.

Purpose – Steer-by-wire (SBW) system mainly relies on sensors, controllers and motors to replace the traditionally mechanical transmission mechanism to realize steering functions. However, the sensors in the SBW system are particularly vulnerable to external influences, which can cause systemic faults, leading to poor steering performance and even system instability. Therefore, this paper aims to adopt a fault-tolerant control method to solve the safety problem of the SBW system caused by sensors failure. Design/methodology/approach – This paper proposes an active fault-tolerant control framework to deal with sensors failure in the SBW system by hierarchically introducing fault observer, fault estimator, fault reconstructor. Firstly, the fault observer is used to obtain the observation output of the SBW system and then obtain the residual between the observation output and the SBW system output. And then judge whether the SBW system fails according to the residual. Secondly, dependent on the residual obtained by the fault observer, a fault estimator is designed using bounded real lemma and regional pole configuration to estimate the amplitude and time-varying characteristics of the faulty sensor. Eventually, a fault reconstructor is designed based on the estimation value of sensors fault obtained by the fault estimator and SBW system output to tolerate the faulty sensor. Findings – The numerical analysis shows that the fault observer can be rapidly activated to detect the fault while the sensors fault occurs. Moreover, the estimation accuracy of the fault estimator can reach to 98%, and the fault reconstructor can make the faulty SBW system to retain the steering characteristics, comparing to those of the fault-free SBW system. In addition, it was verified for the feasibility and effectiveness of the proposed control framework. Research limitations/implications – As the SBW fault diagnosis and fault-tolerant control in this paper only carry out numerical simulation research on sensors faults in matrix and laboratory/Simulink, the subsequent hardware in the loop test is needed for further verification. Originality/value – Aiming at the SBW system with parameter perturbation and sensors failure, this paper proposes an active fault-tolerant control framework, which integrates fault observer, fault estimator and fault reconstructor so that the steering performance of SBW system with sensors faults is basically consistent with that of the fault-free SBW system.

This paper focuses on the steering motion control of an in-wheel motor-drive robot. The influence of the pulse-width modulation (PWM) duty cycle on steering motion and the steering control method have not yet been proved. Thus, this study aimed to design a steering controller for the off-center steer-by-wire system of a robot. The influence of the PWM duty cycle on the steering motion under different conditions is firstly tested on a test bench. Based on the optimal duty cycles of different cases found in the test, a two-stage fuzzy controller of the duty cycle is designed for the steering system. The first stage of the controller is used to dynamically adjust the PWM duty cycle of the electromagnetic friction lock (EFL). The second stage is designed to realize the self-tuning of the fuzzy controller’s quantization factor and the scale factor. Through two-stage control, the motion of the in-wheel motor and the EFL can be coordinated to realize stable and rapid steering. Considering the robots’ primary application in field roads at present, road tests were ultimately conducted to verify the proposed method. The test results show that the angle response rate of the steering arm is elevated with the increase in the steering angle signal. The proposed controller can sensitively track the target angles with smaller overshoot, yaw rate and lateral acceleration, and better steering accuracy than the PID (proportional–integral–differential) controller under different working conditions.