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.     

Fuzzy Sliding-Mode Control of Active Suspensions

In this paper, a robust fuzzy sliding-mode controller for active suspensions of a nonlinear half-car model is introduced. First, a nonchattering sliding-mode control is presented. Then, this control method is combined with a single-input–single-output fuzzy logic controller to improve its performance. The negative value of the ratio between the derivative of error and error is the input and the slope constant of the sliding surface of the nonchattering sliding-mode controller is the output of the fuzzy logic controller. Afterwards, a four-degree-of-freedom nonlinear half-car model, which allows wheel hops and includes a suspension system with nonlinear spring and piecewise linear damper with dry friction, is presented. The designed controllers are applied to this model in order to evaluate their performances. It has been shown that the designed controller does not cause any problem in suspension working limits. The robustness of the proposed controller is also investigated for different vehicle parameters. The results indicate the success of the proposed fuzzy sliding-mode controller.      

A novel driver assist stability system for all-wheel-drive electric vehicles

A novel driver-assist stability system for all-wheel-drive electric vehicles is introduced. The system helps drivers maintain control in the event of a driving emergency, including heavy braking or obstacle avoidance. The system comprises a fuzzy logic system that independently controls wheel torque to prevent vehicle spin. Another fuzzy wheel slip controller is used to enhance vehicle stability and safety. A neural network is trained to generate the required reference for yaw rate. Vehicle true speed is estimated by a sensor data fusion method. The intrinsic robustness of fuzzy controllers allows the system to operate in different road conditions successfully. Moreover, the ease of implementing fuzzy controllers gives a potential for vehicle stability enhancement.     

Dynamic control of electronic differential in the field weakening region

A simple and dynamic electronic differential control method for an outer rotor motor driven electric vehicle based on fuzzy gain scheduling of PI gains method is proposed for constant torque and power region operation using brushless direct current (BLDC) machine. The proposed method is quite insensitive to torque fluctuations and transient speed oscillations due to surface mounted permanent magnet (SMPM) BLDC machines constraints in the field weakening region. To improve the dynamics and stability of the electronic differential system and eliminate the skidding of the wheels and reduce the heating of electric machine in the wide speed range operation, a robust control method is developed. Moreover, PI controller gains are updated continuously by fuzzy gain scheduling approach which has phase advance angle, steering angle and measured speed as controller input parameters in order to eliminate the errors caused from the variable road conditions and torque oscillations in the field weakening region. The proposed method is implemented with 2 × 1.5 kW BLDC motor drive controlled by a TMS320F28335 digital signal processor (DSP). The experimental results show that the proposed method exhibits greater stability under various load, road and vehicle speed conditions.     

航空发动机模糊滑模变结构控制研究

针对航空发动机是一个具有时变不确定性的非线性系统,结合模糊控制和滑模变结构控制的特点,提出了一种基于模糊滑模变结构控制的航空发动机控制方法.采用线性滑模面和指数趋近律设计变结构控制器,应用模糊逻辑系统自适应调节切换增益,避免了传统滑模变结构控制系统在到达阶段对不确定性的敏感性,消除了滑模控制中的抖振.通过数字仿真,结果表明所设计的模糊滑模变结构控制器对系统的参数摄动和外部干扰具有不变性,使被控系统在整个控制阶段都具有较强的鲁棒性.     

An optimal fuzzy PID controller

This paper introduces an optimal fuzzy proportional-integral-derivative (PID) controller. The fuzzy PID controller is a discrete-time version of the conventional PID controller, which preserves the same linear structure of the proportional, integral, and derivative parts but has constant coefficient yet self-tuned control gains. Fuzzy logic is employed only for the design; the resulting controller does not need to execute any fuzzy rule base, and is actually a conventional PID controller with analytical formulae. The main improvement is in endowing the classical controller with a certain adaptive control capability. The constant PID control gains are optimized by using the multiobjective genetic algorithm (MOGA), thereby yielding an optimal fuzzy PID controller. Computer simulations are shown to demonstrate its improvement over the fuzzy PID controller without MOGA optimization     

Indirect sliding mode control based on gray-box identification method for pneumatic artificial muscle

We present an indirect robust nonlinear controller for position-tracking control of a pneumatic artificial muscle (PAMs) testing system. The system modeling is reviewed, for which the existence of uncertain, unknown, and nonlinear terms in the internal dynamics is presented. From the obtained results, an online identification method is proposed for estimation of the internal functions with learning rules designed via a Lyapunov derivative function. A robust nonlinear controller is then designed based on the approximated functions to satisfy the control objective under the sliding mode technique. Appropriate selection of the smooth robust gain and the sliding surface ensures convergence of the tracking error to a desired level of performance. Stability of the closed-loop system is proven through another Lyapunov function. The proposed approach is verified and compared with a conventional proportional–integral–differential (PID) controller, adaptive recurrent neural network (ARNN) controller, and robust nonlinear controller in a real-time system with three different kinds of trajectories and loading. From the comparative experimental results, the effectiveness of the proposed method is confirmed with respect to transient response, steady-state behavior, and loading effect.     

模糊控制技术在作物精播机中的应用

本研究以进一步提高农作物播种精度,减小误差为主要研究目标,抛开传统的轮驱动排种的纯机械播种方式,介绍一种以单片机为控制核心,以步进电机驱动排种的小麦精播控制技术,主、从单片机间采用蓝牙无线传递方式,播种参数及状况由人机界面设定和实时显示、报警。该技术基于模糊控制理论,采用PID控制方式,提出对步进电机施以模糊控制的设计思想,并对二维模糊控制器采用SIMULINK进行仿真验证。结果证明,采用模糊PID控制步进电机,的确达到良好效果,能有效提高播种精确度和稳定性。     

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.     

基于单轮车辆悬架的Fuzzy-PID控制器设计和仿真

研究车辆主动空气悬架的控制问题,在车辆主动空气悬的常规PID控制器的基础上,运用模糊推理对常规PID控制器进行参数在线修订,设计了基于单轮车辆主动空气悬架的Fuzzy-PID控制器,并对Fuzzy-PID控制的单轮车辆主动空气悬架进行Matlab建模和仿真试验。仿真结果表明,与车辆被动空气悬架、常规PID控制的车辆主动空气悬架相比,Fuzzy-PID控制的车辆主动空气悬架可大大降低车身加速度和悬架动行程,提高车辆乘坐舒适性和操纵稳定性,具有良好的鲁棒性,从而验证了Fuzzy-PID控制器的有效性和实用性。     

Modular integrated longitudinal and lateral vehicle stability control for electric vehicles

In this paper, the problem of integrated longitudinal and lateral vehicle stability control is addressed using a modular optimal control structure. The optimization process of the high level model predictive control (MPC) controller determines required longitudinal force and yaw moment adjustments to minimize the error between vehicle longitudinal and lateral vehicle stability dynamic states with respect to the target courses. The low level controller is designed to optimally regulate torque at each wheel based on the control inputs of the high level controller, and distribute required torque between the wheels via actuation system. The actuation system that is utilized to implement the proposed control structure functions based on all-wheel drive technology that can provide active control of both traction and yaw moment control with differential torque. The multi-layered structure of the control system allows modularity in design. The performance of the control structure is investigated by conducting experimental tests. The experimental tests have been performed on an electric Chevrolet Equinox vehicle equipped with four independent motors. The results show that the integration of the vehicle longitudinal and lateral dynamics preserves vehicle stability in a planar motion and improves the vehicle dynamic response, especially in challenging driving maneuvers.     

Enhanced fuzzy sliding mode controller for active suspension systems

We proposed a fuzzy sliding mode controller (FSMC) to control an active suspension system and evaluated its control performance. The FSMC employed the error of the sprung mass position and the error change to establish a sliding surface, and then introduced the sliding surface and the change of the sliding surface as input variables of a traditional fuzzy controller (TFC) in controlling the suspension system. However, no substantial improvement in the ride comfort could be obtained with the FSMC relative to the TFC because the dynamic effect of the sprung mass acceleration from the bouncing tire during tire rotation was not eliminated. We have developed an enhanced fuzzy sliding mode controller (EFSMC) that maintained not only the original FSMC property but also introduced an assisted FSMC to address and compensate for this problem, and to enhance the road-holding capability of the vehicle. The assisted FSMC differs from the original FSMC only in using the sprung mass acceleration instead of the sprung mass position as a variable of the controller design. The EFSMC exhibits better control performance than either the TFC or the FSMC, in suppressing the acceleration of the vehicle body to improve the ride quality, and in reducing the tire deflection to increase the road-holding ability of a car, as confirmed by experimental results.     

Vehicle Yaw Control via Second-Order Sliding-Mode Technique

The problem of vehicle yaw control is addressed in this paper using an active differential and yaw rate feedback. A reference generator, designed to improve vehicle handling, provides the desired yaw rate value to be achieved by the closed loop controller. The latter is designed using the second-order sliding mode (SOSM) methodology to guarantee robust stability in front of disturbances and model uncertainties, which are typical of the automotive context. A feedforward control contribution is also employed to enhance the transient system response. The control derivative is constructed as a discontinuous signal, attaining an SOSM on a suitably selected sliding manifold. Thus, the actual control input results in being continuous, as it is needed in the considered context. Simulations performed using a realistic nonlinear model of the considered vehicle show the effectiveness of the proposed approach.      

A supervisory fuzzy neural network controller for slider-crank mechanism

A supervisory fuzzy neural network (FNN) controller is proposed to control a nonlinear slider-crank mechanism in this study. The control system is composed of a permanent magnet (PM) synchronous servo motor drive coupled with a slider-crank mechanism and a supervisory FNN position controller. The supervisory FNN controller comprises a sliding mode FNN controller and a supervisory controller. The sliding mode FNN controller combines the advantages of the sliding mode control with robust characteristics and the FNN with on-line learning ability. The supervisory controller is designed to stabilize the system states around a defined bound region. The theoretical and stability analyses of the supervisory FNN controller are discussed in detail. Simulation and experimental results are provided to show that the proposed control system is robust with regard to plant parameter variations and external load disturbance.     

基于转角的商用车电动助力转向回正控制研究

以改善商用车电动助力转向系统的回正特性为目标,开发了一种基于转向盘转角的回正目标电流控制器,该控制器以转角传感器LH3获取的转角信号为输入,经PID控制方式获取回正目标电流,通过控制助力电机动作,使转向盘准确快速回位。并利用Simulink仿真软件搭建了该回正控制器仿真模型,对回正特性进行分析。仿真结果表明:转向盘的回正稳定时间大大缩短,该回正控制器明显提高了EPS系统的回正性能。     

压电陶瓷精密控制系统的自适应模糊控制器研究

针对压电陶瓷等非线性系统,建立压电陶瓷微精密位移系统模型,并将模糊控制器替换传统的比例、积分和微分(PID)控制器,实现对PID参数的在线自整定。对模糊控制器的控制变量及论域等级等进行了研究及制定,并对其进行MATLAB仿真。实验结果表明,模糊逻辑控制器比传统PID控制器的响应速度快,超调量小,且控制策略简单易行。     

基于BP神经网络的温度模糊PID控制器设计

根据BP神经网络对温度控制的要求设计出一种模糊PID控制器,采用误差和误差变化率作为模糊PID控制器的输入,PID参数作为模糊PID控制器的输出,使用一组模糊规则实现对PID参数的在线优化调节。采用Simulink图形化工具平台对模糊PID控制器和传统的PID控制器进行建模和仿真,结果表明和传统PID控制器相比,模糊PID控制器性能优良,使系统响应速度加快,超调减小。     

Adaptive Fuzzy Control for Stochastic Nonlinear Systems via Sliding Mode Method

In this paper, an adaptive fuzzy controller is designed for a nonlinear stochastic system to track the given reference via the sliding mode method. The nonlinear stochastic system is modeled by a deterministic nonlinear system with white noise obtained from the derivative of a Wiener process, which eventually generates an Itô differential equation. Compared with existing results, the main advantage is that information of the nonlinear functions is not required. Under the designed controller with the proposed update laws, the tracking error trajectories converge to an arbitrary small region around zero in the mean square norm. Simulations to show the efficiency of the proposed controller are provided.     

参数模糊自整定PID在飞机全电刹车中的应用

提出了一种参数模糊自整定PID的控制策略在飞机全电刹车系统中的应用。滑移率的偏差和偏差变化率作为控制器的输入,PID控制器3个参数的自整定值作为控制器的输出,实现了PID参数的在线自整定。分析了参数模糊自整定PID的原理。飞机全电刹车系统的仿真曲线表明:以滑移率为控制对象,参数模糊自整定PID控制与常规PID相比,控制系统的响应速度快、超调量减小、过渡过程时间大大缩短、振荡次数少,具有较强的鲁棒性和稳定性。