Control strategy for four-wheel steering vehicle based on collaborative simulation

This paper presents a fuzzy logic control strategy on four-wheel steering(4WS) vehicle based on a multi-body vehicle dynamic model. The multi-body vehicle dynamic model based on ADAMS can accurately predict the dynamics performance of the vehicle. Fuzzy logic is applied to track the yaw velocity of the two degrees of freedom ideal model through the co-simulation of ADAMS and Matlab Fuzzy control unit with the optimized membership function. The fuzzy control parameters are optimized and analyzed by a combined optimization algorithm (Genetic Algorithm (GA) and Nonlinear Programming Quadratic Line search (NLPQL) method) combined with response surface model (RSM). Single lane change test is chosen to validate the fuzzy control logic strategy. Simulation result shows that four-wheel steering vehicle with the fuzzy control logic strategy can improve vehicle handling stability greatly comparing with traditional front wheel steering.

In order to study the handling stability of four-wheel steering vehicle, the vehicle is simplified into a linear two-degree-of-freedom model. The fuzzy control technology is used to study the proportional feedforward + fuzzy feedback control strategy of four-wheel steering vehicle, and the fuzzy control toolbox in Matlab/Simulink is used to establish the four-wheel steering control system. The simulation results show that the fuzzy control can effectively improve the response of rear wheel steering, improve the maneuverability of the vehicle at low speed, reduce the turning radius, and improve the handling stability of the vehicle at high speed.

Autonomous navigation in narrow indoor environments such as indoor factory, warehouse and laboratory environments, and so on requires higher flexibility and navigation accuracy of the vehicle. This article presents an autonomous navigation method for four-wheel steering vehicle which combines extended Kalman filtering (EKF) and rapidly-exploring random tree (RRT) to improve the precision and flexibility of autonomous navigation of the vehicle in narrow indoor environments. The four-wheel steering model was established by the key parameters such as shape size and minimum angle of rotation of the experimental vehicle. Considering the problem that the uncertainty of pose estimation increases with time during autonomous navigation, an error model is schemed by adding noise to the output terminal of the analog odometer sensor. In order to suppress the accumulation of the uncertainty and keep it stable for a long time, the prediction and update steps of Kalman filter are introduced to filter the error. Then, the simultaneous positioning and mapping are established. Based on accurate positioning, a set of driving paths to reach the target is generated by RRT sampling algorithm. The simulation results show that positioning uncertainty remains stable over time, which verifies the effectiveness of the method. The overall positioning percentage error is 0.21%. Compared with traditional dead reckoning algorithm, the positioning accuracy is improved by 73.1% and the vehicle flexibility is increased by 68.6%. The four-wheel steering vehicle can find an ideal trajectory in narrow indoor environments, which assures the efficiency of the autonomous navigation and the traveling quality of the navigation route. Finally, the experimental results are consistent with the simulation results, which further verifies the effectiveness of the proposed algorithm.

This paper presents a new steering control structure for vehicles equipped with four-wheel steering system. A linear model of the lateral dynamics is used in this paper. This control structure is based on a simplified linear model of the lateral dynamics of such vehicles and aims to decouple the control of sideslip from the control of yaw rate. The control design is based on a linear multivariable plant and the front and rear steering angles, According to the Individual Channel Design paradigm. The proposed control structure has been applied to design sideslip and yaw rate controllers using a more accurate model of the lateral dynamics of four-wheel steering vehicles. Simulations are used to illustrate the performance and robustness of the designed controllers.

For the issue that it is difficult to build an accurate mathematical model for a strongly coupled and nonlinear four-wheel steering vehicle, a model-free adaptive control scheme is proposed in this thesis. The method of compact form dynamic linearization is used to transform the nonlinear vehicle model into a discrete dynamic linearization data model. The controller is designed by using the sideslip angle and yaw rate of vehicles, and the immune algorithm is adopted to optimize the relevant parameters. To simulate the control system, CarSim and MATLAB/Simulink were introduced into my research. The results showed that the model-free adaptive control strategy can improve the vehicle's handling stability more significantly, compared with the model-based proportional control four-wheel steering strategy, which verified the feasibility and effectiveness of the designed model-free adaptive control scheme.

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

An optimal control approach is developed for Four-Wheel Active Steering Vehicles. First, parameterized vehicle model is built. Second, active steering model is implemented and coupled to the vehicle model. Finally, an optimal control loop, delivering as final output optimal vehicle performances of Four-Wheel Active Steering Vehicles is developed.

Because of the complexity of vehicle steering motion, the classical linear two degrees of freedom lateral dynamics model of vehicle has been unable to meet well the research needs of the active steering control system. In order to make better use of the chassis control technology to improve the safety when vehicle turning and the vehicle-handling stability at high speeds, it is necessary to establish a more complex new dynamic model which not only fully reflect the steering characteristics of the vehicle, but also meet the needs of the control system design. In this paper, the active four-wheel steering vehicle is regarded as the research object; the modeling method of a eight degrees of freedom nonlinear dynamics model of this active four-wheel steering vehicle is discussed based on the comprehensive analysis of kinetic and kinematic; the differential equations for each degree of freedom of motion are derived step by step; and the modeling and solving methods are also illustrated by means of the Simulink software. In addition, the model accuracy is also verified effectively by contrast with ADAMS vehicle multi-body dynamics model. Verification results show that the established model has a relatively high accuracy and can be applied to the research and analysis for different active chassis control systems of the vehicle.

Current research predominantly focuses on enhancing vehicle driving stability when tires operate in the linear region. Thus, the effect of cornering stiffness on vehicle driving stability under nonlinear region remains an unresolved challenge. To address this issue, this paper proposes a novel nonlinear model predictive control (NMPC)-based four-wheel steering trajectory tracking controller that incorporates time-varying cornering stiffness. First, the longitudinal and lateral axle forces are estimated using a strong tracking unscented Kalman filter (STUKF); these estimated forces are then applied to a cornering stiffness estimator based on the unscented Kalman filter (UKF). Simulation tests under various scenarios are conducted using CarSim and Simulink, with comparisons made against the linear quadratic regulator (LQR) and conventional model predictive control (MPC) strategies. Finally, hardware-in-the-loop (HIL) experimental results verify that the proposed NMPC controller exhibits satisfactory trajectory tracking performance, stability, and real-time performance.

This paper proposes a dexel‐based virtual prototyping system, which builds a virtual prototype with dexels or rectangular strips of solid. The approach resembles the physical fabrication process of most powder‐based rapid prototyping (RP) systems. It simulates an RP process to create a virtual prototype. Colour virtual prototypes may also be fabricated relatively easily. Thus, the designer can perform design validation and accuracy analysis easily in a virtual environment as if using a physical prototype. In addition to numeric quantification of the RP process, the system provides vivid visualisations of the prototype for studying its characteristics. Furthermore, the prototype may be superimposed on the product model, and the areas with dimensional errors beyond design limits may be clearly highlighted for subsequent improvement. The designer may thus analyse and compare the surface texture point‐by‐point of the prototype with the product design.

In order to harvest sugarcane, the whole stalk sugarcane harvester was designed by South China Agricultural University. Virtual prototyping and key parts of the sugarcane harvester were simulated in PRO/E and ADAMS. The virtual experiment was conducted to study the following factors, separated and lifted-up device, cutting device, primary conveying device, secondary conveying device, and gathering device, analyzed the flow in various parts of sugarcane harvester and field experiment was validated. Virtual results show that the virtual prototype can smoothly separating and lifting, cutting, conveying and gathering, reference value was provided for physical prototype in manufacture.

X-by-wire technology is an advancement in automotive industry and is recognized by many countries in recent years. The in-wheel motor system is a type of drive-by-wire technology and it will be the main focused for the vehicle model in this paper. The steer-by-wire is a kind of by-wire technology in the automotive industry for the electric vehicle. [1] Steer-by-wire technology can be divided into two types which are two-wheel steering (2WS) and four-wheel steering (4WS). As we know, 2WS system is used in most of the vehicles.[2] However, the lower maneuverability will be shown in this type of vehicle during the vehicle spinning. The dynamic equation of motion was used for the simulation of vehicle movement.[3] The software of MATLAB Simulink was used to imitate that the effect of 4WD and 4WS EV during cornering.[4,5] The passive control was used in this simulation. As the result, the simulation indicated that 2WS EV is easy oversteered. After applied 4WS system, the vehicle oversteer problem was successfully solved by use parallel steering mode.

A layer-based virtual prototyping system for product development

Taking automobile design as a study case, this paper presents the approaches to implement a distributed image-based virtual prototyping (VP) system employing distributed virtual reality (DVR) technology combined with image-based rendering (IBR) technology. Firstly, the methods to implement an image-based closed spherical space are presented, which can be used to construct a photo-quality virtual prototype; secondly, the novel vision sensitivity-based rendering tactic and the relevant image cubic-spline interpolation methods, which visibly improve the efficiency of rendering, is represented. Finally, a distributed image-based virtual prototyping system is implemented, by which a product designer can easily and rapidly construct a photo-quality virtual prototype and distribute it on the Internet, whereby buyers can visually understand whether or not the product can meet their individual requirements. This system enables buyers and designers to visually evaluate and discuss the design remotely, which has consequently improved the design efficiency greatly and lowered the cost substantially.

One of the well-known methods for improving the lateral stability of a vehicle is to utilize an active steering controller. Common active steering systems use feedback signals from a yaw rate sensor and side-slip angle estimation. However, in some driving conditions, skilled drivers can prevent vehicle rollover using the steering angle of the vehicle. Hence, the effect of an active front-steering controller on the roll angle of the vehicle is studied in this paper. For this study, a three-degree-of-freedom linear model of a vehicle which has the lateral motion, the yaw motion and the roll motion as the degrees of freedom is derived. The effectiveness of the active steering controller on the roll stability of the vehicle is examined by simulations in a wide range of longitudinal velocities of the vehicle using a linear quadratic regulator controller. The results obtained indicate that the active steering controller increases the roll stability of the vehicle only at low vehicle speeds and also that consideration of the roll degree of freedom in the design of the active steering controller is not effective at high vehicle speeds. Therefore a two-degree-of-freedom bicycle model is sufficient for active steering controller design. Another part of this paper is dedicated to designing a practical active steering controller for a vehicle with time-varying and uncertain parameters. In fact, designing a controller which guarantees the stability of the vehicle with simultaneous uncertainties in the cornering stiffnesses of the tyres and in the time-varying velocity is the main goal of this paper. Another important advantage of the proposed controller is its simplicity and static structure. In fact, this controller design is carried out offline and can be implemented for use in a real vehicle. This simple and powerful controller is a robust linear quadratic regulator controller. For designing a robust linear quadratic regulator controller, a polytopic model of the two-degree-of-freedom vehicle is obtained, which considers the uncertainty in the parameters. In addition, a linear matrix inequality is used to design a linear quadratic regulator controller for an uncertain or parameter-varying system. Finally, the performance of the designed robust linear quadratic regulator controller was studied by simulating the vehicle responses in some manoeuvres. A sport utility vehicle model is used for simulation purposes. This non-linear vehicle model has eight degrees of freedom and its tyres are assumed to be both linear and non-linear. The performance of the robust linear quadratic regulator controller was studied for vehicles with both linear and non-linear Pacejka tyres. The proposed controller guarantees robust stability of the closed-loop system in the presence of 50% variation in the vehicle speed and 20% uncertainty in the stiffnesses of the tyres.