Simulation Analysis Of Vehicle Four-Wheel Steering Control

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 improve vehicle stability at high speed, the ideal steering model of vehicle was set up, an active four-wheel steering controller was designed based on optimal following control principle, and the graphical structure of the control system was put up using Matlab/Simulink. A virtual prototype of the four-wheel steering vehicle was constructed using ADAMS, the co-simulation technology of virtual prototype and control system was studied, and a visual co-simulation platform of mechanical-control system was built. For typical double-lane-change driving condition, the virtual experiment was carried out to verify control effect to vehicle. Simulation results show that this four-wheel steering optimum controller designed achieve control object for reducing sideslip and keeping constant steering sensitivity when the vehicle is cornering, and improve vehicle safety at high speed. The co-simulation technology provides a more intuitive and accurate research means for vehicle dynamic control.

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.

In this paper, we study the application of fuzzy set theory, genetic algorithms and rough set theory techniques to the control of the rectifier. A fuzzy adaptive control scheme with the aid of rough set theory via genetic algorithms (GAs) finding the center parameters in place of the classical control is proposed. On the one hand, genetic algorithm gets optimal parameters of an accurate domain model, which tunes the scaling factors of fuzzy adaptive control but is hardly established in a non-liner system. On the other hand, fuzzy adaptive control deals with the dynamics and complexity of responses from rectifier of a HVDC system in the operation points, by adjusting its control parameters with the aid of rough tuner adaptively. Our study includes a brief introduction to fuzzy control and rough set algorithms, both theory and application. We also evaluate the performance of rough fuzzy control by simulation in the paper. The focus of our experiments is on the constant current control in rectifier side of HVDC system. The result shows there are many improvements offered by the fuzzy control scheme based on rough set theory in comparison with the only fuzzy control scheme in the rectifier side of HVDC system.

Adaptive fuzzy fault-tolerant control problem for a class of uncertain switched stochastic nonlinear systems with time-varying asymmetric output constraints is addressed in this study. Under the action of well-designed asymmetric nonlinear mapping, fuzzy control technology, and backstepping recursive design scheme; the actuator faults of both loss of effectiveness and lock-in-place are considered to develop the adaptive fuzzy controller. The boundedness of all signals as well as the convergence of the output tracking error of the closed-loop plant to an arbitrary small neighborhood about zero are guaranteed by the developed fuzzy adaptive control strategy, and the time-varying output constraints are not violated. A simulation example is worked out to demonstrate the validity of the proposed control scheme.

In order to improve the tracking accuracy of unmanned vehicle paths, while taking into account steering frequency and vehicle stability, and to be able to identify nonlinear road realities, a comprehensive collaborative governance strategy for unmanned vehicle driving is proposed, taking into account real-time influencing factors on the road as far as possible. On the basis of lateral trajectory following control, a driver operation discrimination module is introduced, a vehicle dynamics model and a tyre model are established, and an upper trajectory following lateral controller is designed using strong robust structural control theory based on the assumption of steady-state circular motion, optimal pre-sighting theory. The fuzzy controller with gain adjustment and the underlying actuator controller are used to reduce the sliding mode controller jitter in the automatic steering mode and to reduce the error of the theoretical analysis. For the ESP system, a more robust non-linearised fuzzy PID control strategy is used to improve it.

In view of the problems related to vehicle-handling stability and the real-time correction of the heading direction, nonlinear analysis of a vehicle steering system was carried out based on phase plane theory. Subsequently, direct yaw-moment control (DYC) of the vehicle was performed. A four-wheel, seven-degree-of-freedom nonlinear dynamic model that included the nonlinear characteristics of the tire was established. The stable and unstable regions of the vehicle phase plane were divided, and the stable boundary model was established by analyzing the side slip angle–yaw rate ([Formula: see text]) and side slip angle–side slip angle rate [Formula: see text] phase planes as functions of the vehicle state variables. In the unstable region of the phase plane, taking the instability degree as the control target, a fuzzy neural network control strategy was utilized to determine the additional yawing moment of the vehicle required for stability restoration, which pulled the vehicle back from an unstable state to the stable region. In the stable region of the phase plane, a fuzzy control strategy was utilized to determine the additional yawing moment so that the actual state variables followed the ideal state variables. In this way, the vehicle responded rapidly and accurately to the steering motion of the driver. A simulation platform was established in MATLAB/Simulink and three working condition was tested, that is, step, sine with dwell, and sine amplification signals. The results showed that the vehicle handling stability and the instantaneous heading-direction adjustment ability were both improved due to the control strategy.

A scheme is promoted which builds a water-saving irrigation system based on wireless sensor network and fuzzy control technology. we design a wireless sensor network, which consists of sensor node cluster, coordinator node and irrigation controller nodes Here sensor node cluster is responsible for gathering information such as soil moisture and regularly send it to the coordinator node. Fuzzy controller embedded in the coordinator node takes soil moisture error and error change rate as its input and obtained water demand amount of crops under certain soil moisture through fuzzy inference and fuzzy judge and output it to irrigation controller node. Irrigation controller node controls the implementation of automatic watering. In this article, we describe the implementation scheme of sensor network node and fuzzy controller in detail and design communication protocol for sensor nodes. The experimental results show that the system can quickly and accurately calculate water demand amount of crops, which can provide a scientific basis for water-saving irrigation. In this way, we have made an exploratory study in applying wireless sensor networks and fuzzy control technology to fine agriculture project.

Aiming at the limitation of traditional PID algorithm in the field of control, the fuzzy control theory and traditional PID control algorithm are combined to realize parameter self-tuning function, a fuzzy self-tuning PID controller, and this algorithm is analyzed in-depth study. It provides a new solution of modern industrial control problem that combining with fuzzy control technology and PID control technology. The FPGA has high integration, short development cycle, the characteristics of strong reusability; fuzzy PID controller has a good practical value and optimistic application prospect based on FPGA.

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.

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.

Pulse-type near-fault earthquakes have obvious long-duration pulses, so they can cause large deformation in a base-isolated system in contrast to non-pulse-type near-fault and far-field earthquakes. This paper proposes a novel self-tuning fuzzy logic control strategy for seismic protection of a base-isolated system, which can operate the control force of the piezoelectric friction damper against different types of earthquakes. This control strategy employs a hierarchic control algorithm, in which a higher-level supervisory fuzzy controller is implemented to adjust the input normalization factors and output scaling factor, while a sub-level fuzzy controller effectively determines the command voltage of the piezoelectric friction damper according to current level of earthquakes. The efficiency of the proposed control strategy is also compared with uncontrolled and maximum passive cases. Numerical results reveal that the novel fuzzy logic control strategy can effectively reduce the isolation system deformations without the loss of potential advantages of base-isolated system.

Fuzzy control strategy is developed for the dual-clutch single-axis torque coupling parallel hybrid electric vehicle. In this paper the torque distribution fuzzy controller which has been designed for the hybrid vehicle which is optimized by genetic algorithms. The simulation model of the hybrid vehicle was built upon matlab / simulink and ADVISOR software. Then a fuzzy rules and correspondent membership functions had been established and the input language variable and output language variable use trapeziform and deltoid membership functions. After design of fuzzy logic torque controller, the genetic algorithm was introduced and used it to optimize the fuzzy logic torque controller. Under typical condition NEDC, the fuzzy control strategy is optimized both by genetic algorithms with the constraint condition of economy performance and by integrated constraint conditions of economy performance and emission performance. Optimization results show that when the controller is only optimize fuzzy control strategy for economy performance the fuel consumption decreased by 5.3% but the emission of CO and NOx both increased, but when the controller both optimize fuzzy control strategy for economy performance and emission performance the fuel consumption decreased by 4.3% with emission quality improved. So the fuzzy control strategy optimize by the genetic algorithm can improve the fuel consumption obvious.

This paper presents an application of fuzzy controllers for improving the captured wind energy and the performance of variable speed wind turbines using fuzzy controllers. The simulated fuzzy model has been satisfactorily implemented using a programmable logic controller as the designed fuzzy controller. Transition between power optimization and power limitation of the wind turbine is greatly improved especially in the rated wind speed working conditions. Fuzzy controller also permits to increase captured wind energy in low and high wind speed.

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.