Fuzzy Sliding Mode Control for the Vehicle Height and Leveling Adjustment System of an Electronic Air Suspension

The accurate control for the vehicle height and leveling adjustment system of an electronic air suspension(EAS) still is a challenging problem that has not been effectively solved in prior researches. This paper proposes a new adaptive controller to control the vehicle height and to adjust the roll and pitch angles of the vehicle body(leveling control) during the vehicle height adjustment procedures by an EAS system. A nonlinear mechanism model of the full?car vehicle height adjustment system is established to reflect the system dynamic behaviors and to derive the system optimal control law. To deal with the nonlinear characters in the vehicle height and leveling adjustment processes, the nonlinear system model is globally linearized through the state feedback method. On this basis, a fuzzy sliding mode controller(FSMC) is designed to improve the control accuracy of the vehicle height adjustment and to reduce the peak values of the roll and pitch angles of the vehicle body. To verify the effectiveness of the proposed control method more accurately, the full?car EAS system model programmed using AMESim is also given. Then, the co?simulation study of the FSMC performance can be conducted. Finally, actual vehicle tests are performed with a city bus, and the test results illustrate that the vehicle height adjustment performance is effectively guaranteed by the FSMC, and the peak values of the roll and pitch angles of the vehicle body during the vehicle height adjustment procedures are also reduced significantly. This research proposes an effective control methodology for the vehicle height and leveling adjustment system of an EAS, which provides a favorable control performance for the system.

Controlling chaos using Takagi-Sugeno fuzzy model and adaptive adjustment

In this paper, an approach to the control of continuous-time chaotic systems is proposed using the Takagi-Sugeno （TS） fuzzy model and adaptive adjustment. Sufficient conditions are derived to guarantee chaos control from Lyapunov stability theory. The proposed approach offers a systematic design procedure for stabilizing a large class of chaotic systems in the literature about chaos research. The simulation results on Rossler＇s system verify the effectiveness of the proposed methods.

Novel ARC-Fuzzy Coordinated Automatic Tracking Control of Four-Wheeled Mobile Robot

Four-wheeled,individual-driven,nonholonomic structured mobile robots are widely used in industries for automated work,inspection and explora-tion purposes.The trajectory tracking control of the four-wheel individual-driven mobile robot is one of the most blooming research topics due to its nonholonomic structure.The wheel velocities are separately adjusted to follow the trajectory in the old-fashioned kinematic control of skid-steered mobile robots.However,there is no consideration for robot dynamics when using a kinematic controller that solely addresses the robot chassis’s motion.As a result,the mobile robot has lim-ited performance,such as chattering during curved movement.In this research work,a three-tiered adaptive robust control with fuzzy parameter estimation,including dynamic modeling,direct torque control and wheel slip control is pro-posed.Fuzzy logic-based parameter estimation is a valuable tool for adjusting adaptive robust controller(ARC)parameters and tracking the trajectories with less tracking error as well as high tracking accuracy.This research considers the O type and 8 type trajectories for performance analysis of the proposed novel control technique.Our suggested approach outperforms the existing control methods such as Fuzzy,proportional–integral–derivative(PID)and adaptive robust controller with discrete projection(ARC–DP).The experimental results show that the scheduled performance index decreases by 2.77%and 4.76%.All the experimen-tal simulations obviously proved that the proposed ARC-Fuzzy performed well in smooth groud surfaces compared to other approaches.

Optimal fuzzy PID controller with adjustable factors based on flexible polyhedron search algorithm

A new kind of optimal fuzzy PID controller is proposed, which contains two parts. One is an on line fuzzy inference system, and the other is a conventional PID controller. In the fuzzy inference system, three adjustable factors x p, x i , and x d are introduced. Their functions are to further modify and optimize the result of the fuzzy inference so as to make the controller have the optimal control effect on a given object. The optimal values of these adjustable factors are determined based on the ITAE criterion and the Nelder and Mead′s flexible polyhedron search algorithm. This optimal fuzzy PID controller has been used to control the executive motor of the intelligent artificial leg designed by the authors. The result of computer simulation indicates that this controller is very effective and can be widely used to control different kinds of objects and processes.

Thickness and Shape Synthetical Adjustment for DC Mill Based on Dynamic Nerve-Fuzzy Control

Due to the complexity of thickness and shape synthetical adjustment system and the difficulties to build a mathematical model,a thickness and shape synthetical adjustment scheme on DC mill based on dynamic nerve-fuzzy control was put forward,and a self-organizing fuzzy control model was established.The structure of the network can be optimized dynamically.In the course of studying,the network can automatically adjust its structure based on the specific questions and make its structure the optimal.The input and output of the network are fuzzy sets,and the trained network can complete the composite relation,the fuzzy inference.For decreasing the off-line training time of BP network,the fuzzy sets are encoded.The simulation results indicate that the self-organizing fuzzy control based on dynamic neural network is better than traditional decoupling PID control.

Fuzzy Shape Control Based on El man Dynamic Recursion Network Prediction Model

In the strip rolling process, shape control system possesses the characteristics of nonlinearity, strong coupling, time delay and time variation. Based on self adapting Elman dynamic recursion network prediction model, the fuzzy control method was used to control the shape on four-high cold mill. The simulation results showed that the system can be applied to real time on line control of the shape.

The Optimized Disign of the Fuzzy Controller(Ⅰ)——The predigested disquisition of rules of fuzzy control

To improve the ability and precisions of the fuzzy control,this thesis points out the adjusted fuzzy control method,realizes the precision of the fuzzy quantity, and reduces the number of the fuzzy control rules,so that it can predigest the process of disigns and realize the methods without influencing the idiocratic control,which are on the base of the domain flexing.

Correction factor based double model fuzzy logic control strategy of arc voltage in pulsed MIG welding

According to the feature of arc voltage control in welding steel using pulsed MIG welding, a correction factor based double model fuzzy logic controller (FLC) was developed to realize the arc voltage control by means of arc voltage feedback. When the error of peak arc voltage was great, a coarse adjusting fuzzy logic control rules with correction factor was designed, in the controller, the peak arc voltage was controlled by the wire feeding speed by means of arc voltage feedback. When the error of peak arc voltage was small, a fine adjusting fuzzy logic control rules with correction factor was designed, in this controller, the peak arc voltage was controlled by the background time by means of arc voltage feedback. The FLC was realized in a Look-Up Table (LUT) method. Experiments had been carried out aiming at implementing the control strategy to control the arc length change in welding process. Experimental results show that the controller proposed enables the consistency of arc length and the stability of arc voltage and welding process to be achieved in pulsed MIG welding process.

Adaptive fuzzy design of a rope attitude adjustment system

The attitude adjustment of rope system faces the challenging due to the difficulty in obtaining accurate three-dimensional(3D)mathematical model and solving by traditional methods.A set of adjustment systems is designed and used to investigate the automatic control for level or preset attitude adjustment of unknown weights and eccentric loads.The system principle and characteristics are analyzed.The 3D model is decomposed into two two-dimensional(2D)subsystems,and an adaptive fuzzy controller based on BP neural network and least squares(LSE)is designed.The simulation experiment uses MATLAB to train the level-adjustment data for testing algorithm,and a small load is used to verify the effectiveness of the system.The experimental results show that precise attitude adjustment can be achieved within the system load range,and the response speed is fast.This adjustment method provides a fast and effective method for precise adjustment of the load attitude.

预估Fuzzy-PID在中央空调控制系统中的应用

电加热常作为中央空调的末端设备置于被调节房间风道入口处 ,微调加热以提高温度控制精度 ,为此提出一种电加热温度控制方法 ,采用预估Fuzzy PID复合控制算法 ,通过对加热功率的调节进行闭环控制 ,实现控制点温度的稳定控制 .新的控制算法结合了smith预估控制、Fuzzy和PID控制器的优点 .在MATLAB环境中仿真预估Fuzzy PID控制中央空调末端电加热 ,结果表明该控制方法能提高温度稳定性并降低能耗 ,控制精度达到± 0 .1℃ .

基于自调整因子 Fuzzy 规则的专家控制器

针对传统控制理论的不足，介绍一种基于自调整因子Ｆｕｚｚｙ规则的专家控制器。通过仿真和在应力冻结箱控制中实时运行，取得了满意的效果。

一种Fuzzy-PID复合控制器在过热汽温控制中的仿真研究

在电厂中,必须严格控制过热器出口蒸汽温度,蒸汽温度过高或过低,都将给安全生产带来不利影响。针对过热汽温被控对象的特点,充分利用模糊控制的动态特性好和PID调节能消除静态偏差的特性,设计了一种带自调整因子的模糊控制器,研究了Fuzzy-PID复合串级控制在电厂过热汽温控制系统中的应用。仿真结果表明,Fuzzy-PID复合串级控制系统具有更好的抗干扰能力和适应性。

丘陵山地翻耕机具姿态调整系统设计与仿真

针对现有丘陵山地翻耕作业机具姿态调整系统在复杂作业工况下调整和控制精度不高,导致机具作业稳定性和作业安全性较差的问题,基于机具横向姿态调整系统,利用模糊PID控制算法设计出一套适用于丘陵山地翻耕作业的机具姿态调整系统。以常规PID控制算法为对照,在姿态调整系统动力学建模的基础上,通过MATLAB对其进行仿真分析,结果表明,常规PID控制算法的机具横向姿态调整时间为4.5 s,横向姿态角的超调量为0.89°;模糊PID控制算法的机具横向姿态调整时间为1.9 s,横向姿态角基本无超调。仿真试验结果验证了该机具姿态调整系统的可行性、正确性和科学性,调整系统精度和稳定性能满足丘陵山地横坡作业复杂的工况要求。

Fuzzy控制理论在甜菜制糖过程的应用

基于Ｆｕｚｚｙ控制理论提出了归一Ｆｕｚｚｙ量化及带修正函数的Ｆｕｚｚｙ控制规则自调整方法，并介绍了该法在甜菜制糖过程的成功应用。通过某糖厂的运行结果，证明了微机Ｆｕｚｚｙ控制在甜菜制糖过程应用的可行性与有效性。

酸碱中和过程pH值的Fuzzy-PID控制

针对酸碱中和过程的非线性和时滞特性,提出一种在线自调节参数的Fuzzy-PID控制方法;由pH值变化特性曲线可知,在离中和点较远的两侧pH值变化缓慢,而在中和点附近pH值变化十分剧烈,因此PID控制器参数在pH值中和的全过程需要适时变动;Fuzzy控制器根据pH值实时偏差在线调整PID控制器的参数,获得期望控制效果的主要方法有两个:其一,通过提高偏差较小时语言值的灵敏度,来适应中和点附近pH值变化率较大的特性;其二,通过分析偏差及偏差变化率的大小和符号来设置模糊规则;仿真结果表明,该方法既能适应pH值全局变化的特点,又能克服流量波动的影响。

输灰系统杆驱机构双缸闭环同步模糊PID控制

为了提高输灰系统杆驱机构双缸同步控制精度,设计了一种推进过程模糊比例-积分-微分(PID)控制方案,采用闭环同步控制。构建了模糊PID控制器,并通过Simulink完成仿真测试,验证了推进过程的同步控制性能。研究结果表明:速度信号发生了先加速,之后进入稳定速度,最后再减速。低速下,以模糊PID控制的双缸最大同步误差为0.085 mm,在稳定状态下的误差为0.002 2 mm;高速下,以模糊PID控制的双缸最大同步误差降低至0.13 mm,在稳定状态下的误差减小为0.03 mm。无论是高速还是低速,模糊PID控制相对于传统PID控制方式表现出更低的双缸同步误差,可以获得更高的系统控制精度。

变参数自调整的FUZZY-PI控制器设计

简单的模糊控制器与常规的PID调节器相比虽具有较好的动态性能,并对控制对象的参数和结构变化有较强的适应性,但是其稳态误差较大.作者所设计的变参数自调整模糊控制器在控制过程中可以根据误差及误差的变化自动调整误差、误差变化的量化因子、比例系数和积分系数,从而提高了控制水平.该系统已经在山西某药厂投入使用,达到了预期的目标,取得了良好的经济效益.

重型多轮车辆半主动悬挂复合控制策略

针对重型车辆半主动悬挂系统和其关键部件可调叶片式减振器,提出了一种含深度置信网络逆模型的分层复合控制策略。首先基于可调叶片式减振器在道路模拟实验台架上的试验数据建立了基于深度置信网络的逆模型,可以根据需求阻尼力反求出控制电流。然后根据14自由度半车模型和单轮悬挂模型,分别建立了约束最优控制器和模糊控制器,通过一个S函数对二者进行加权分配并得到最终的需求阻尼控制力输出。基于Python和Matlab/Simulink环境搭建了虚拟试验平台,验证了深度置信网络逆模型的可行性,并对复合控制策略进行了仿真验证,结果表明,所提出的复合控制策略能够有效提高车辆的舒适性和安全性。

基于模糊控制的闪蒸罐PID控制器设计与仿真

PID控制器依照经验进行整定的方式需要花费大量时间和精力,而工业被控对象大都存在随时间变化、非线性等特性,PID控制的效果并不理想。以闪蒸罐压力控制为研究对象设计FUZZY-PID控制器。仿真结果表明:在PID调节后有超调情况下,FUZZY-PID控制器调节时间缩短约341.80 s,超调量减小约4.62%;在PID调节后无超调情况下,FUZZY-PID控制器调节时间缩短约395.60 s。抗干扰实验结果表明:在加入白噪声后,FUZZY-PID控制器与PID控制器控制效果差距不甚明显,都比较稳定,在同一时刻加入脉冲扰动后,FUZZY-PID控制器所需调节时间较PID控制器缩短约70.70 s,调节时间更短,更加稳定。说明相较于传统PID控制器,FUZZY-PID控制器调节时间更短、超调量更小、更加稳定。

用FUZZY-PID控制器实现控制参数的自整定

为扩展传统的的PID控制,在分析FUZZYCONTROL(模糊控制)和PID控制原理的基础上,得出了FUZZY和PID控制相结合的一种复合控制的有效结构.介绍了FUZZY控制器设计中的几个关键问题.通过readfis命令把MATLAB中的FUZZYTOOLBOX与SIMULNK结合起来,实现了对几种典型对象的控制仿真,其结果显示,三种典型实例的阶跃响应均接近理想,从而方便并简化了对实际控制系统的设计与调试过程.