Vehicle Steering Control Research Articles

Variable Impedance-based Human-machine Interaction Method Using Reinforcement Learning for Shared Steering Control of Intelligent Vehicle

Variable Impedance-based Human-machine Interaction Method Using Reinforcement Learning for Shared Steering Control of Intelligent Vehicle

Adaptive Convex Optimization Algorithm for Curved Road Vehicle Steering Control

Adaptive Convex Optimization Algorithm for Curved Road Vehicle Steering Control

Automated Guided Vehicle Control System for Automated Parking Purposes

For the safe transport of the load using automated controlled vehicles, specifically pallets with passenger cars, it is necessary to design a control processes at multiple levels. However, the custom control of such transporters is also very complex because, in addition to the standard control loops of the drives, it is necessary to precisely compare the pallets to the lifting points, in the case of battery power to predict the range, diagnose the components states and other related tasks. This article deals with the real scale model built for parking house purposes and individual parts of the control system and its functional tests.

LiDAR-driven spiking neural network for collision avoidance in autonomous driving

Facilitated by advances in real-time sensing, low and high-level control, and machine learning, autonomous vehicles draw ever-increasing attention from many branches of knowledge. Neuromorphic (brain-inspired) implementation of robotic control has been shown to outperform conventional control paradigms in terms of energy efficiency, robustness to perturbations, and adaptation to varying conditions. Here we propose LiDAR-driven neuromorphic control of both vehicle's speed and steering. We evaluated and compared neuromorphic PID control and online learning for autonomous vehicle control in static and dynamic environments, finally suggesting proportional learning as a preferred control scheme. We employed biologically plausible basal-ganglia and thalamus neural models for steering and collision-avoidance, finally extending them to support a null controller and a target-reaching optimization, significantly increasing performance.

Neural-Fuzzy-Based Adaptive Sliding Mode Automatic Steering Control of Vision-based Unmanned Electric Vehicles

This paper presents a novel neural-fuzzy-based adaptive sliding mode automatic steering control strategy to improve the driving performance of vision-based unmanned electric vehicles with time-varying and uncertain parameters. Primarily, the kinematic and dynamic models which accurately express the steering behaviors of vehicles are constructed, and in which the relationship between the look-ahead time and vehicle velocity is revealed. Then, in order to overcome the external disturbances, parametric uncertainties and time-varying features of vehicles, a neural-fuzzy-based adaptive sliding mode automatic steering controller is proposed to supervise the lateral dynamic behavior of unmanned electric vehicles, which includes an equivalent control law and an adaptive variable structure control law. In this novel automatic steering control system of vehicles, a neural network system is utilized for approximating the switching control gain of variable structure control law, and a fuzzy inference system is presented to adjust the thickness of boundary layer in real-time. The stability of closed-loop neural-fuzzy-based adaptive sliding mode automatic steering control system is proven using the Lyapunov theory. Finally, the results illustrate that the presented control scheme has the excellent properties in term of error convergence and robustness.

Research on control algorithm of a automatic driving robot based on improved model predictive control

The automatic driving robot can replace the driver in the traditional test and ADAS test; Model Predictive Control (MPC) is used in the horizontal and vertical control process of high-speed driving. According to the uncertainties and complex constraints of automatic driving horizontal and vertical control, the adaptive improvement of MPC is proposed, which can better adapt to the characteristics of self driving vehicles, sensors and tyres. This paper proposes a multi-layer and multi time automatic driving control strategy based on improved MPC. Visual studio is used to write the upper computer software of the automatic driving robot, which realizes good human-computer interaction. The automatic driving robot is verified in real vehicle test. The experimental results show that the transverse and longitudinal control strategy of the improved model predictive control is stable, and the automatic driving robot can realize the high precision and high dynamic control of vehicle steering, pedal and shift.

Adaptive type-2 fuzzy sliding mode control of steer-by-wire systems with event-triggered communication

The steering-by-wire (SbW) system is one of the main subsystems of automatic vehicles, realizing the steering control of autonomous vehicles. This paper proposes an event-triggered adaptive sliding mode control for the SbW system subject to the uncertain nonlinearity, time-varying disturbance, and limited communication resources. Firstly, an event-triggered nested adaptive sliding mode control is proposed for SbW systems. The uncertain nonlinearity is approximated by the interval type-2 fuzzy logic system (IT2 FLS). The time-varying disturbance, modeling error, and event-triggering error can be offset by robust terms of sliding mode control. The key advantage is that the high-frequency switching of sliding mode control only appears on the time derivate of control input without increasing the input-output relative degree of closed-loop SbW systems, such that the chattering phenomenon can be eliminated. Finally, theoretical analysis shows that the practical finite-time stability of the closed-loop SbW system can be achieved, and communication resources in the controller-to-actuator channels can be saved while avoiding the Zeno-behavior. Numerical simulations and experiments are given to evaluate the effectiveness of the proposed method.

Establishment and tracking control of trapezoidal steering wheel angle model for autonomous vehicles

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.

Perception, Planning and Control for Self-Driving System Based on On-board Sensors

The autonomous vehicle can recognize and understand environment, self-control, and achieve the driving level of the human driver. To create this kind of system, the following work was carried out: (a) a real time lane detection system is proposed, based on vision system functions, using webcam camera; (b) In order to detect curve lanes, deep learning is applied for lane detection, based on fully Convolutional Neural Network (CNN); (c) The cubic spline interpolation method is used for path generation, based on Global Positioning System (GPS) data, where distance between two adjacent path generation points is same. Compared with the connection method of the cubic polynomial fitting algorithm, the curve fitted path by the cubic spline interpolation method is smoother and more satisfied with the vehicle motion pattern; (d) Based on frenet coordinate frame, optimize trajectory planning method is used to plan safe, easy and comfort trajectory. Compared to the Cartesian coordinate frame, frenet coordinate frame simplifies the solution of road curve fitting problems, especially in the case of complex road environment; (e) Fuzzy sliding mode control method is proposed for vehicle steering control. Finally, the simulation and experiment results are used to prove the effectiveness of the proposed methods.

Fail-Operation Control of In-Wheel Motor Drive Electric Vehicle Based on Wheel Isolation and Yaw Moment Compensation

To improve the trackability of in-wheel motor drive (IWMD) and wheel-individual steer electric vehicles (EVs) when steering actuators fail, the fail-operation control strategy was proposed to correct vehicles in a steering failure situation and avoid losing control of vehicle steering. A linear quadratic regulator (LQR) decides the additional yaw moment of the vehicle according to vehicle state errors. The tire force estimation module estimates the compensating resistance moment generated by the failed wheel according to the tire slip angle and the vertical tire force. By isolating the failed wheel, the optimal torque distribution (OTD) controller allocates the additional yaw moment and the compensating resistance moment to normal wheels to realize the fail-operation control of the IWMD vehicle. The control effect was verified through co-simulation of MATLAB/Simulink and Trucksim. Compared with the uncontrolled and direct torque allocation methods, the proposed OTD method reduces the lateral trajectory error of the vehicle by 86% and 60.5%, respectively, when failure occurs, and achieves better velocity maintaining ability, which proves the effectiveness of the proposed fail-operation control strategy.

Neural Network Based Modelling and Steering Control of an Intelligent Vehicle Under Dynamic Sensitive Conditions

A neural network based modelling and steering control method of an intelligent vehicle under dynamic sensitive conditions is proposed in this paper. Four radius function neural networks are used to compensate the modelling error of a traditional dynamic vehicle steering model. The designed control law is consisted of four terms: a neural network term, a proportion term, an integral term and a robust term. Stability is analysed through Lyapunov function and the training law is generated at the same time. Test results show that the designed steering controller works quite well and is of great value in path tracking of an intelligent vehicle when the vehicle dynamics getting significant.

Velocity-based robust fault tolerant automatic steering control of autonomous ground vehicles via adaptive event triggered network communication

This paper proposes a velocity-based robust fault tolerant automatic steering control approach for autonomous ground vehicles via an adaptive event triggered network communication mechanism. Firstly, as the vehicle longitudinal velocity is not fixed but time-varying, a polytope with finite vertices is adopted to construct a linear parameter varying vehicle system model. Secondly, since network-induced delay and actuator fault inevitably occur in the control system, a novel Lyapunov function is formulated to guarantee the asymptotical stability of the closed-loop system. Moreover, an advanced event triggered communication scheme is proposed for the co-design of robust automatic steering controller, which is quite effective to reduce network communication burden and enhance resource utilization of the shared band-limited in-vehicle communication network. Finally, numerical simulations on two cases are carried out to evaluate and verify the effectiveness and robustness of the proposed controller. Compared with existing studies, the proposed controller contributes greatly to the economization of communication resource and improvement of path following performance and lateral stability.

Research on Steering Control of Multi-Axle Steering Heavy Commercial Vehicle Based on Reducing Tire Wear

Research on Steering Control of Multi-Axle Steering Heavy Commercial Vehicle Based on Reducing Tire Wear

Collision-avoidance steering control for autonomous vehicles using neural network-based adaptive integral terminal sliding mode

Due to the complex driving conditions confronted by an autonomous vehicle, it is significant for the vehicle to possess a robust control system to achieve effective collision-avoidance performance. This paper proposes a neural network-based adaptive integral terminal sliding mode (NNAITSM) control scheme for the collision-avoidance steering control of an autonomous vehicle. In order to describe the vehicle’s lateral dynamics and path tracking characteristics, a two-degrees-of-freedom (2DOF) dynamic model and a kinematic model are adopted. Then, an NNAITSM controller is designed, where a radial basis function neural network (RBFNN) scheme is utilized to online approximate the optimal upper bound of lumped system uncertainties such that prior knowledge about the uncertainties is not required. The stability of the control system is proved via Lyapunov, and the selection guideline of control parameters is provided. Last, Matlab-Carsim co-simulations are executed to test the performance of the designed controller under different road conditions and vehicle velocities. Simulation results show that compared with conventional sliding mode (CSM) and nonsingular terminal sliding mode (NTSM) control, the proposed NNAITSM control scheme owns evident superiority in not only higher tracking precision but also stronger robustness against various road surfaces and vehicle velocities.

Sliding Mode Steering Control of Spherical Underwater Robot

From the point of view of motion control, considering the effects of inertia mass, additional mass, secondary resistance, gravity and buoyancy on the robot, a relatively complete dynamic model of the spherical underwater vehicle is established. On this basis, the steering controller of the spherical underwater vehicle is designed based on the principle of sliding mode exponential reaching law. The control performance is compared with that of ordinary PID control algorithm. From the simulation results, it can be seen that the steering control of the spherical underwater vehicle with the designed sliding mode control method can make the robot have faster steering ability and stronger anti-jamming ability.

The Control Schemes of Vehicle Steer by Wire System by using Fuzzy Logic and PID Controller

Recently, the Vehicle Steer-By-Wire (VSBW) system has improved significantly in terms of vehicle dynamics, comfort, safety, maneuverability and stability. VSBW eliminates the mechanical linkage between steering wheel and the front wheels which caused the practical problems for vehicle steering control. This study presents investigations into the development of PID and fuzzy logic control schemes for steering wheel returnability, directional control and wheel synchronization of a VSBW system. For PID control scheme, PD controller is used to control the returnability of steering wheel while PID controller is used to control the angle of front wheel. The Ziegler Nichols method is used for tuning the PID parameters. The fuzzy logic controller is used to control the returnability of steering wheel. The implementation environment is developed within LabView Software for comparison of performance for both control schemes. Implementation results of the response of the VSBW system with the PID and fuzzy logic controller are presented in time domains. The performances of control schemes are examined in terms of steering wheel returnability, directional control and wheel synchronization.

Active steering control for autonomous vehicles based on a driver-in-the-loop platform: A case study of collision avoidance

Leveraging the advancements in sensor and mapping technologies, the collision-free autonomous vehicle becomes possible in the future. In this article, a case study of collision avoidance by active steering control is presented and verified by a driver-in-the-loop platform. The proposed control system integrates a risk assessment algorithm and a hierarchical model predictive control approach to ensure a safe driving. First, a fuzzy logic is used to estimate the potential conflict. Besides, a nonlinear model predictive control is introduced in the upper layer of the model predictive controller to generate a collision-free trajectory. Furthermore, the lower layer determines the optimal steering angle based on the linear time-variant model predictive control to follow the replanning path. The performance of the controller has been evaluated in the real-time driver-in-the-loop test. The results show that the autonomous vehicle is able to avoid the collision with the surrounding vehicle that is operated by a real driver, and the performance of collision avoidance is improved by means of the risk assessment.

Improved Steering and Adaptive Cruise Control for Autonomous Vehicles Using Model Predictive Control

The research on autonomous vehicles was started nearly a century ago but the major parts of significant advance have been done over the past few decades. In this paper, designing and simulating of a Model Predictive Controller (MPC) and comparing it to a classical feedback controller, which was designed and implemented on a car for Lane Keeping Assist (LKA) and Adaptive Cruise Control (ACC) systems, are presented. The goal of the control system is to follow linear trajectories and stay in the lanes by correcting the lateral deviation to reach the destination point. By regulating the longitudinal and lateral accelerations of the vehicle, it is possible to provide hands-free driving experience only on highways as it satisfies the level 2 autonomy according to the SAE automation levels. In the Adaptive Cruise Control (ACC) system, the ego car follows the velocity set by driver until it maintains a safe distance from the lead car. If the space between the lead car and the ego car is less than the safe distance, the ego car reduces the velocity and does not follow the driver set velocity until it has reached a safe distance with the lead car. The frontal or rear-end collisions and traffic congestions can be reduced by maintaining the safe distance using the spacing control by Adaptive Cruise Control (ACC) system. Model Predictive Control with the linear time invariant system with input, output and state variables uses the feed-forward and the disturbance models are used for ACC and Lane keeping Assist systems.

Emergency steering control of autonomous vehicle for collision avoidance and stabilisation

ABSTRACTCollision avoidance and stabilisation are two of the most crucial concerns when an autonomous vehicle finds itself in emergency situations, which usually occur in a short time horizon and require large actuator inputs, together with highly nonlinear tyre cornering response. In order to avoid collision while stabilising autonomous vehicle under dynamic driving situations at handling limits, this paper proposes a novel emergency steering control strategy based on hierarchical control architecture consisting of decision-making layer and motion control layer. In decision-making layer, a dynamic threat assessment model continuously evaluates the risk associated with collision and destabilisation, and a path planner based on kinematics and dynamics of vehicle system determines a collision-free path when it suddenly encounters emergency scenarios. In motion control layer, a lateral motion controller considering nonlinearity of tyre cornering response and unknown external disturbance is designed using tyre lateral force estimation-based backstepping sliding-mode control to track a collision-free path, and to ensure the robustness and stability of the closed-loop system. Both simulation and experiment results show that the proposed control scheme can effectively perform an emergency collision avoidance manoeuvre while maintaining the stability of autonomous vehicle in different running conditions.

Differential Steering Control of Four-Wheel Independent-Drive Electric Vehicles

This paper investigates the skid steering of four-wheel independent-drive (4WID) electric vehicles (EV) and a differential steering of a 4WID EV with a steer-by-wire (SBW) system in case of steering failure. The dynamic models of skid steering vehicle (SSV) and differential steering vehicle (DSV) are established and the traditional front-wheel steering vehicle with neutral steering characteristics is selected as the reference model. On this basis, sideslip angle observer and two different sliding mode variable structure controllers for SSV and DSV are designed respectively. Co-simulation results of CarSim and Simulink show that the designed controller for DSV not only controls the yaw rate and sideslip angle of DSV to track those of the reference model exactly, but also ensures the robustness of the controlled system compared with the designed controller for SSV. And the differential driving torque needed to realize the differential steering is much smaller than that for skid steering, which indicates the possibility of the differential steering in case of steering failure.