A fuzzy logic sliding mode controlled electronic differential for a direct wheel drive EV

In this study, a direct wheel drive electric vehicle based on an electronic differential system with a fuzzy logic sliding mode controller (FLSMC) is studied. The conventional sliding surface is modified using a fuzzy rule base to obtain fuzzy dynamic sliding surfaces by changing its slopes using the global error and its derivative in a fuzzy logic inference system. The controller is compared with proportional–integral–derivative (PID) and sliding mode controllers (SMCs), which are usually preferred to be used in industry. The proposed controller provides robustness and flexibility to direct wheel drive electric vehicles. The fuzzy logic sliding mode controller, electronic differential system and the overall electrical vehicle mechanism are modelled and digitally simulated by using the Matlab software. Simulation results show that the system with FLSMC has better efficiency and performance compared to those of PID and SMCs.     

Design of an Optimal Fuzzy Controller for Antilock Braking Systems

Antilock braking systems (ABSs) have been developed to improve vehicle control during sudden braking, especially on slippery road surfaces. The objective of such control is to increase wheel traction force in the desired direction while maintaining adequate vehicle stability and steerability and reducing the vehicle stopping distance. In this paper, an optimized fuzzy controller is proposed for ABSs. The objective function is defined to maintain the wheel slip to a desired level so that maximum wheel traction force and maximum vehicle deceleration are obtained. All the components of a fuzzy system are optimized using genetic algorithms. The error-based global optimization approach is used for fast convergence near the optimum point. Simulation results show fast convergence and good performance of the controller for different road conditions     

Adaptive vehicle traction force control for intelligent vehicle highway systems (IVHSs)

This paper is concerned with robust longitudinal control of vehicles in intelligent vehicle highway systems by adaptive vehicle traction force control. Two different traction force controllers, adaptive fuzzy logic control and adaptive sliding-mode control, are proposed and applied to the fastest stable acceleration/deceleration and robust vehicle platooning problems. The motivation for investigating adaptive techniques arises from the unknown time-varying nature of the tire/road surface interaction that governs vehicle traction. Synchronous application of the engine or brake torques is also proposed for more stable vehicle maneuvers. The lack of controllability during braking (only one net input torque for the two control objectives, i.e., front and rear wheel slips) is partly overcome by applying auxiliary engine torque. Simulations of the two control methods are conducted using a complex nonlinear vehicle model which fully describes the dynamic behavior of the vehicle. Both controllers result in good performance under time-varying operating conditions.     

High Speed Aircraft Robust Fuzzy Controller Design Based on H∞ Performance

The dynamics of the high-speed vehicle (HSV) is partially or completely unknown because of various reasons, such as modeling errors, in-flight failure, and external disturbances. In this paper, a global stability robust fuzzy controller is designed to control the flight F-16 with uncertain perturbation. For the desired H output-feedback controllers, a necessary and sufficient condition of quadratic stability is derived with the well-established results of the Lyapunov stability theory and nonnegative matrix. The controllers not only guarantee the global asymptotically stability of the resultant closed-loop system with external disturbance and parameter perturbation, but also have a desired H performance in a large flight envelop (LFE).     

Multi-objective control synthesis: an application to 4WS passenger vehicles

The vehicle dynamics and control play an important role in an automated highway system for passenger cars. This study addresses the problem of designing active controllers for four-wheel-steering (4WS) vehicles. We first obtain a set of linear maneuvering equations representing the four-wheel steering motions and independent wheel torques for lateral/directional plus roll dynamics. We then formulate simultaneous H₂ and H_∞ (sub)-optimal controls with a desired pole assignment via linear matrix inequalities (LMIs). The steering angles are actively controlled by steering wheel commands through the actuator mechanisms for the lateral/directional and roll motions. Further the wheel power and braking are directly controlled by independent torques. Numerical simulations are performed on a complex vehicle model in order to evaluate the vehicle performance (noise and disturbance attenuation), stability, and robustness under a given class of uncertainty. Finally, the presented autopilot controller provides greater maneuverability and improved directional stability for passenger vehicles.     

Automobile Brake-by-Wire Control System Design and Analysis

The automobile brake-by-wire (BBW) system, which is also called the electromechanical brake system, has become a promising vehicle braking control scheme that enables many new driver interfaces and enhanced performances without a mechanical or hydraulic backup. In this paper, we survey BBW control systems with focuses on fault tolerance design and vehicle braking control schemes. At first, the system architecture of BBW systems is described. Fault tolerance design is then discussed to meet the high requirements of reliability and safety of BBW systems. A widely used braking model and several braking control schemes are investigated. Although previous work focused on antilock and antislip braking controls on a single wheel basis, we present a whole-vehicle control scheme to enhance vehicle stability and safety. Simulations based on the whole-vehicle braking model validate a proposed fuzzy logic control scheme in the lateral and yaw stability controls of vehicles.     

Design of a VDC System for All-Wheel Independent Drive Vehicles

It is shown that the vehicle dynamic control (VDC) system can improve the vehicle handling and active safety of driver and passengers considerably. The control of vehicle yaw moment through differential braking, based on the vehicle dynamic state feedbacks, is a traditional way of VDC. In this study, a new VDC system for a four motorized-wheels electric vehicle has been developed, for which the traction of each wheel can be controlled individually. Using this feature, the new VDC system provides the desired tractive force of vehicle and the desired external yaw moment through the integrated control of wheel motors. The structure of the control system is a multilayer type, which has been developed by using independent controllers, designed in accordance with the appropriate theories.     

Fuzzy combination of linear state-feedback and switching controllers

A fuzzy controller, which is a fuzzy combination of linear state-feedback and switching controllers, is proposed for nonlinear systems subject to parameter uncertainties. By proper design of the proposed fuzzy controller, the chattering effect near the origin can be eliminated. The global system stability is also guaranteed.     

Lateral stabilization of a four wheel independent drive electric vehicle on slippery roads

In this paper, a new controller is proposed for lateral stabilization of four wheel independent drive electric vehicles without mechanical differential. The proposed controller has three levels including high, medium and low control levels. Desired vehicle dynamics such as reference longitudinal speed and reference yaw rate are determined by higher level of controller. Moreover, using a neural network observer and a fuzzy logic controller, a novel reference longitudinal speed generator system is presented. This system guarantees the vehicle’s stable motion on the slippery roads. In this paper, a new sliding mode controller is proposed and its stability is proved by Lyapunov stability theorem. This sliding mode control structure is faster, more accurate, more robust, and with smaller chattering than classic sliding mode controller. Based on the proposed sliding mode controller, the medium control level is designed to determine the desired traction force and yaw moment. Therefore, suitable wheel forces are calculated. Finally, the effectiveness of the introduced controller is investigated through conducted simulations in CARSIM and MATLAB software environments.     

FPGA Implementation of Embedded Fuzzy Controllers for Robotic Applications

Fuzzy-logic-based inference techniques provide efficient solutions for control problems in classical and emerging applications. However, the lack of specific design tools and systematic approaches for hardware implementation of complex fuzzy controllers limits the applicability of these techniques in modern microelectronics products. This paper discusses a design strategy that eases the implementation of embedded fuzzy controllers as systems on programmable chips. The development of the controllers is carried out by means of a reconfigurable platform based on field-programmable gate arrays. This platform combines specific hardware to implement fuzzy inference modules with a general-purpose processor, thus allowing the realization of hybrid hardware/software solutions. As happens to the components of the processing system, the specific fuzzy elements are conceived as configurable intellectual property modules in order to accelerate the controller design cycle. The design methodology and tool chain presented in this paper have been applied to the realization of a control system for solving the navigation tasks of an autonomous vehicle.     

基于LMI的时滞模糊大系统的分散镇定

讨论了具有时滞关联的模糊大系统的分散镇定问题，基于李亚普诺夫稳定性理论及大系统分散控制理论，给出了保证该时滞模糊大系统闭环渐近稳定的LMI形式的充分条件。满足这些充分条件的分散化并行分布补偿(DPDC)控制器参数可通过MATLAB的LMI工具箱有效地求取。因此本文提供了一个综合时滞关联模糊大系统DPDC模糊控制器的有效方法。     

Modeling, Analysis, and Neural Network Control of an EV Electrical Differential

This paper presents system modeling, analysis, and simulation of an electric vehicle (EV) with two independent rear wheel drives. The traction control system is designed to guarantee the EV dynamics and stability when there are no differential gears. Using two in-wheel electric motors makes it possible to have torque and speed control in each wheel. This control level improves EV stability and safety. The proposed traction control system uses the vehicle speed, which is different from wheel speed characterized by a slip in the driving mode, as an input. In this case, a generalized neural network algorithm is proposed to estimate the vehicle speed. The analysis and simulations lead to the conclusion that the proposed system is feasible. Simulation results on a test vehicle propelled by two 37-kW induction motors showed that the proposed control approach operates satisfactorily.     

An adaptive hierarchical control approach of vehicle handling stability improvement based on Steer-by-Wire Systems

This paper proposes a novel adaptive hierarchical control approach for Steer-by-Wire (SbW) vehicles to improve the handling stability. The high-level stability control scheme contains a variable steering ratio (VSR) strategy based on the adaptive-network-based fuzzy inference system (ANFIS) and an active front steering (AFS) controller designed with the integral sliding mode method by tracking the expected yaw rate, in which the desired front wheel angle is generated to enhance the cornering stability performance. Besides, an adaptive tracking controller (ATC) for the SbW system is designed by using the adaptive sliding mode control method to achieve desired steering performance in the lower level. The proposed adaptive control strategy is validated with different driving circles from ISO standards in simulation tests and hardware-in-the-loop (HiL) experiments. The results demonstrate that the designed control approach improve the vehicle handling stability significantly, even in some extreme driving conditions.     

Backing control problem of a mobile robot with multiple trailers:fuzzy modeling and LMI-based design

The paper presents backing control of computer simulated mobile robots with multiple trailers by fuzzy modeling and control. We deal with two kinds of mobile robots: a mobile robot with five trailers and a mobile robot with ten trailers. To design fuzzy controllers, nonlinear models of the mobile robots with multiple trailers are represented by Takagi-Sugeno fuzzy models (TS fuzzy model). Before making TS fuzzy models, we simplify the nonlinear dynamics of the mobile robots. Under an assumption, TS fuzzy models are made from the simplified nonlinear models. The so-called parallel distributed compensation (PDC) is employed to design fuzzy controllers from the TS fuzzy models. Next, we derive a stability condition based on the Lyapunov approach. The stability condition of the designed fuzzy control system is cast in terms of linear matrix inequalities (LMI's) since it is reduced to a problem of finding a common Lyapunov function for a set of Lyapunov inequalities. Convex optimization techniques based on LMI's are utilized to solve the problem of finding stable feedback gains and a common Lyapunov function for the designed fuzzy control system. The simulation results show the effects of the fuzzy modeling, the controller design via the PDC, and the stability analysis based on LMIs     

一类基于自适应反馈控制的车辆编队研究

文中研究了基于生物刺激神经动力学方法的车辆编队控制问题。以编队车辆的运动学模型为基础,在跟随领航者体系下建立车辆编队系统动态模型。根据李雅普诺夫稳定性理论,设计出新的自适应反馈跟踪控制器,将车辆跟随问题转化为系统误差的控制问题,并运用仿真实验验证控制器的有效性。与现有的反馈跟踪控制器进行对比后可以看到,文中所设计的控制器在稳定性和收敛速度上有了进一步的提高。     

Lyapunov-function-based design of fuzzy logic controllers and itsapplication on combining controllers

This paper presents the design of fuzzy logic controllers (FLCs) for nonlinear systems with guaranteed closed-loop stability and its application on combining controllers. The design is based on heuristic fuzzy rules. Although each rule in the FLC refers to a stable closed-loop subsystem, the overall system stability cannot be guaranteed when all these rules are applied together. In this paper, it is proved that if each subsystem is stable in the sense of Lyapunov (ISL) under a common Lyapunov function, the overall system is also stable ISL. Since no fuzzy plant model is involved, the number of subsystems generated is relatively small, and the common Lyapunov function can be found more easily. To probe further, an application of this design approach to an inverted pendulum system that combines a sliding-mode controller (SMC) and a state feedback controller (SFC) is reported. Each rule in this FLC has an SMC or an SFC in the consequent part. The role of the FLC is to schedule the final control under different antecedents. The stability of the whole system is guaranteed by the proposed design approach. More importantly, the controller thus designed can keep the advantages and remove the disadvantages of the two conventional controllers     

Adaptive Vehicle Lateral-Plane Motion Control Using Optimal Tire Friction Forces With Saturation Limits Consideration

This paper presents an adaptive nonlinear control scheme aimed at the improvement of the handling properties of vehicles. The control inputs for steering intervention are the steering angle and wheel torque for each wheel, i.e., two control inputs for each wheel. The control laws are obtained from a nonlinear 7-degree-of-freedom (DOF) vehicle model. A main loop and eight cascade loops are the basic components of the integrated control system. In the main loop, tire friction forces are manipulated with the aim of canceling the nonlinearities in a way that the error dynamics of the feedback linearized system has sufficient degrees of exponential stability; meanwhile, the saturation limits of tires and the bandwidth of the actuators in the inner loops are taken into account. A modified inverse tire model is constructed to transform the desired tire friction forces to the desired wheel slip and sideslip angle. In the next step, these desired values, which are considered as setpoints, are tackled through the use of the inner loops with guaranteed tracking performance. The vehicle mass and mass moment of inertia, as unknown parameters, are estimated through parameter adaptation laws. The stability and error convergence of the integrated control system in the presence of the uncertain parameters, which is a very essential feature for the active safety means, is guaranteed by utilizing a Lyapunov function. Computer simulations, using a nonlinear 14-DOF vehicle model, are provided to demonstrate the desired tracking performance of the proposed control approach.     

A new strategy for traction control in turning via engine modeling

The driving stability is affected by driven wheel slip, which can be controlled by the driven wheel torque. In a vehicle powered by an internal combustion engine, the torque can be controlled by an engine management system. The sliding mode algorithm is the mechanism behind the design of the traction control system (TCS). The longitudinal slip is controlled by the position of the throttle valve. The vehicle model used has seven degrees of freedom and a two-state engine model, i.e., the mass of air in the intake manifold and the engine speed. Time-delay transport is considered in the engine model used. A nonlinear tire model for combined slip is used for tire force computation. Due to the nonlinear dynamic of the tire, vehicle, and engine, the control method of sliding mode is used for its robustness. A controller is designed based on the dynamic surface control, for which two first-order surfaces are defined. The effectiveness of the controller is demonstrated with simulation results for different maneuvers. Results show that for different road conditions, the acceleration performance, directional stability, and steerability of a vehicle equipped with TCS is improved. The reason is that the slip is controlled by keeping it in a desired range     

Hybrid control system design using a fuzzy logic interface

A hybrid control system is proposed for regulating an unknown nonlinear plant. The interface between the continuous-state plant and the discrete-event supervisor is designed using a fuzzy logic approach. The fuzzy logic interface partitions the continuous-state space into a finite number of regions. In each region, the original unknown nonlinear plant is approximated by a fuzzy logic-based linear model, then state-feedback controllers are designed for each linear model. A high-level supervisor coordinates (mode switching) the set of closed-loop systems in a stable and safe manner. The stability of the system is studied using nonsmooth Lyapunov functions. For illustration and verification purposes, this technique has been applied to the well-known inverted pendulum balancing problem.