FUZZY GLOBAL SLIDING MODE CONTROL BASED ON GENETIC ALGORITHM AND ITS APPLICATION FOR FLIGHT SIMULATOR SERVO SYSTEM

To alleviate the chattering problem, a new type of fuzzy global sliding mode controller （FGSMC） is presented. In this controller, the switching gain is estimated by fuzzy logic system based on the reachable conditions of sliding mode controller（SMC）, and genetic algorithm （GA） is used to optimize scaling factor of the switching gain, thus the switch chattering of SMC can be alleviated. Moreover, global sliding mode is realized by designing an exponential dynamic sliding surface. Simulation and real-time application for flight simulator servo system with Lugre friction are given to indicate that the proposed controller can guarantee high robust performance all the time and can alleviate chattering phenomenon effectively.

Study of engaged performance regulation in controlled transfer clutch of 4WD car based on genetic sliding mode control

This paper investigates an approach to improve the engagement quality of controlled transfer clutch mode in 4 wheel drive(WD) car from three considerations of reducing friction,smoothening responsiveness and alleviating jerk.The method utilizes an improved sliding mode control with genetic algorithm instead of simplified mode to determine appropriate values of parameters in control close- loop.The simulation results show that the method is effective for improving the engagement quality of coupling satisfying different design needs for 4WD car,as well as robustness even if input torque is changed at a certain range.

Optimal variable structure control with sliding modes for unstable processes

In this work,a variable structure control(VSC)technique is proposed to achieve satisfactory robustness for unstable processes.Optimal values of unknown parameters of VSC are obtained using Whale optimization algorithm which was recently reported in literature.Stability analysis has been done to verify the suitability of the proposed structure for industrial processes.The proposed control strategy is applied to three different types of unstable processes including non-minimum phase and nonlinear systems.A comparative study ensures that the proposed scheme gives superior performance over the recently reported VSC system.Furthermore,the proposed method gives satisfactory results for a cart inverted pendulum system in the presence of external disturbance and noise.

Fuzzy-second order sliding mode control optimized by genetic algorithm applied in direct torque control of dual star induction motor

The direct torque control of the dual star induction motor(DTC-DSIM) using conventional PI controllers is characterized by unsatisfactory performance, such as high ripples of torque and flux, and sensitivity to parametric variations. Among the most evoked control strategies adopted in this field to overcome these drawbacks presented in classical drive, it is worth mentioning the use of the second order sliding mode control(SOSMC) based on the super twisting algorithm(STA) combined with the fuzzy logic control(FSOSMC). In order to realize the optimal control performance, the FSOSMC parameters are adjusted using an optimization algorithm based on the genetic algorithm(GA). The performances of the envisaged control scheme, called G-FSOSMC, are investigated against G-SOSMC, G-PI and BBO-FSOSMC algorithms. The proposed controller scheme is efficient in reducing the torque and flux ripples, and successfully suppresses chattering. The effects of parametric uncertainties do not affect system performance.

Adaptive Fuzzy Sliding Mode Controller for Grid Interface Ocean Wave Energy Conversion

This paper presents a closed-loop vector control structure based on adaptive Fuzzy Logic Sliding Mode Controller (FL-SMC) for a grid-connected Wave Energy Conversion System (WECS) driven Self-Excited Induction Generator (SEIG). The aim of the developed control method is to automatically tune and optimize the scaling factors and the membership functions of the Fuzzy Logic Controllers (FLC) using Multi-Objective Genetic Algorithms (MOGA) and Multi-Objective Particle Swarm Optimization (MOPSO). Two Pulse Width Modulated voltage source PWM converters with a carrier-based Sinusoidal PWM modulation for both Generator- and Grid-side converters have been connected back to back between the generator terminals and utility grid via common DC link. The indirect vector control scheme is implemented to maintain balance between generated power and power supplied to the grid and maintain the terminal voltage of the generator and the DC bus voltage constant for variable rotor speed and load. Simulation study has been carried out using the MATLAB/Simulink environment to verify the robustness of the power electronics converters and the effectiveness of proposed control method under steady state and transient conditions and also machine parameters mismatches. The proposed control scheme has improved the voltage regulation and the transient performance of the wave energy scheme over a wide range of operating conditions.

Design of Soft Computing Based Optimal PI Controller for Greenhouse System

Greenhouse system (GHS) is the worldwide fastest growing phenomenon in agricultural sector. Greenhouse models are essential for improving control efficiencies. The Relative Gain Analysis (RGA) reveals that the GHS control is complex due to 1) high nonlinear interactions between the biological subsystem and the physical subsystem and 2) strong coupling between the process variables such as temperature and humidity. In this paper, a decoupled linear cooling model has been developed using a feedback-feed forward linearization technique. Further, based on the model developed Internal Model Control (IMC) based Proportional Integrator (PI) controller parameters are optimized using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to achieve minimum Integral Square Error (ISE). The closed loop control is carried out using the above control schemes for set-point change and disturbance rejection. Finally, closed loop servo and servo-regulatory responses of GHS are compared quantitatively as well as qualitatively. The results implicate that IMC based PI controller using PSO provides better performance than the IMC based PI controller using GA. Also, it is observed that the disturbance introduced in one loop will not affect the other loop due to feedback-feed forward linearization and decoupling. Such a control scheme used for GHS would result in better yield in production of crops such as tomato, lettuce and broccoli.

Time-varying Sliding Mode Controls in Rigid Spacecraft Attitude Tracking

To solve the problem of attitude tracking of a rigid spacecraft with an either known or measurable desired attitude trajectory, three types of time-varying sliding mode controls are introduced under consideration of control input constraints. The sliding surfaces of the three types initially pass arbitrary initial values of the system, and then shift or rotate to reach predetermined ones. This way, the system trajectories are always on the sliding surfaces, and the system work is guaranteed to have robustness against parameter uncertainty and external disturbances all the time. The controller parameters are optimized by means of genetic algorithm to minimize the index consisting of the weighted index of squared error （ISE） of the system and the weighted penalty term of violation of control input constraint. The stability is verified with Lyapunov method. Compared with the conventional sliding mode control, simulation results show the proposed algorithm having better robustness against inertia matrix uncertainty and external disturbance torques.

A Novel Genetic Algorithm and Its Application in Variable Structure Control of Robot

A novel genetic algorithm (NGA) is proposed, which possesses micro-regulation and renascence operation. The optimized variable searching interval is regulated gradually according to the sub-group of excellent individuals. The NGA is used to optimize the parameters of the variable structure control (VSC), which satisfies the new reaching law and sliding mode. It is used in robot control systems. Simulation results are given.

整车主动悬架滑模控制及多目标优化

为改善车辆主动悬架系统性能,在建立整车主动悬架系统模型的基础上,结合滑模变结构控制理论,提出了一种基于多目标优化的主动悬架系统控制参数的优化方法。基于滑模面到达条件结合李雅普诺夫稳定性理论对滑模控制器进行稳定性验证,建立以车身侧倾角加速度、车身俯仰角加速度和车身垂直加速度为控制目标的多目标优化问题,并利用粒子群算法对控制器参数进行优化。仿真结果表明,优化后主动悬架系统的3个控制目标函数值较优化前分别减小了10.3%、27.3%和20.0%,基于多目标优化的整车主动悬架滑模控制策略改善了整车的乘坐舒适性和操纵稳定性。

Intelligent fractional-order integral sliding mode control for PMSM based on an improved cascade observer

In this paper,an intelligent fractional-order integral sliding mode control(FOISMC)strategy based on an improved cascade observer is proposed.First,an FOISMC strategy is designed to control a permanent magnet synchronous motor.It has good tracking performance,is strongly robust,and can effectively reduce chattering.The proposed FOISMC strategy associates strong points of the integral action(which can eliminate steady-state tracking errors)and the fractional calculus(which is flexible).Second,an improved cascade observer is proposed to detect the rotor information with a smaller observation error.The proposed observer combines an adaptive sliding mode observer and an extended high-gain observer.In addition,an improved variable-speed grey wolf optimization algorithm is designed to enhance controller parameters.The effectiveness of the strategy is tested using simulations and an experiment involving model uncertainty and external disturbance.

深海起重机升沉补偿滑模预测控制

受海浪、风力等因素的干扰,深海起重机升沉补偿系统的响应速度缓慢,系统对于负载位移的控制精度较差。为了提高升沉补偿系统响应速度与系统对负载位移的控制精度,保证起重机在各种海况下正常作业,提出了基于CNN-LSTM深度学习网络的滑模预测控制方法。首先,将CNN网络与LSTM网络结合,建立CNN-LSTM深度学习网络控制系统预测模型。其次,通过参考位移与实际位移的误差建立滑模面,并根据幂次函数设计滑模面参考轨迹;采用粒子群算法(PSO)对性能指标进行寻优,得出控制律,根据控制律控制负载实际位移跟随参考位移。最后,进行了仿真研究。结果表明与传统模型预测控制相比,在该方法的控制作用下,系统的响应速度更快,系统对负载位移的控制精度更高,系统的鲁棒性能更强。

基于灰狼算法的步进电机滑模控制系统设计

为了改善转台方位轴的位置精准度及抖振现象,本文围绕步进电机控制系统展开研究。设计了指数趋近律的位置方程,应用了灰狼优化算法整定滑模控制系统的多项参数。在此基础上建立了数学模型,使用MATLAB/Simulink构建了位置三闭环仿真,对系统的位置精准度、抗干扰能力进行分析,并与PID控制系统、传统的滑模控制系统进行了比较。仿真结果表明优化后的控制系统较稳定,响应速度快,抗干扰能力强。最后基于STM32F10ZET6主控芯片搭建了硬件平台,实验证明了优化后的控制策略有效提升了位置精准度,保证了系统的可靠性。

改进GA整定滑模参数的直流电机控制研究

在转速环控制器采用传统的滑模控制(Sliding mode control,SMC)方法的直流电机双闭环控制系统中,常因滑模参数难以整定导致直流电机控制效果不佳。针对该问题,提出了一种基于改进遗传算法(Improved genetic algorithm,IGA)整定滑模参数的方法。首先,建立出直流电机双闭环控制系统转速环各环节传递函数,通过IGA联合转速环MATLAB/Simulink仿真模型整定出滑模参数的最优值。最后,对采用该最优值的滑模速度控制器与传统的PID、普通的滑模速度控制器和普通GA整定的滑模速度控制器进行仿真比较分析,并基于dSPACE 1202半实物仿真平台搭建了直流电机双闭环控制硬件在环实验平台,进行了实验验证。仿真和实验结果均表明经IGA整定的滑模速度控制器可使直流电机双闭环控制系统具有无超调、响应快速和强鲁棒性的优点,证明了该方法能够有效解决滑模速度控制器参数整定问题。

新型永磁同步电机有限控制集无模型预测电流控制

为了减小永磁同步电机在启动时转速容易出现超调的现象,提高系统在参数摄动时的鲁棒性,采用自抗扰控制取代传统速度环中的比例积分(proportional integral,PI)控制、白鲸算法优化自抗扰控制参数,从而降低人工整定参数的复杂度;同时采用无模型控制电流环及模型预测与超局部模型结合的方法,设计一款滑模观测器。最后通过Matlab/Simulink软件进行仿真实验,由仿真结果可知:电机启动时超调较小,在负载突变时转速能够快速恢复到稳态;转速波动由原来的4.33%降低至3.85%,且达到稳态的时间显著减少,由200 ms降低至5 ms,电流脉动仅为±0.35 A。

改进自适应遗传算法优化的船用增压锅炉上锅筒水位滑模控制方法

[目的]船用增压锅炉的上锅筒水位由于航行中负荷的频繁变化,难以获得理想的控制效果。为了保证上锅筒水位的稳定,对大负荷扰动下的上锅筒水位控制方法进行研究。提出一种基于改进自适应遗传算法优化的增压锅炉上锅筒水位滑模控制方法。[方法]将上锅筒水位偏差的速度和速度变化率引入S函数来设计滑模控制器,采用李雅普诺夫稳定性原理证明其稳定性。在此基础上,利用改进的自适应遗传算法优化滑模控制器。[结果]将改进的自适应遗传算法优化的增压锅炉上锅筒水位滑模控制方法与传统的PID控制方法进行了对比分析。在响应斜坡扰动信号和阶跃扰动信号时,改进自适应遗传算法优化的滑模控制器均能够无差跟踪输入信号,其稳定时间比PID缩短5 s,超调量也小于PID。[结论]仿真结果表明,改进自适应遗传算法优化的滑模控制方法具有更优的控制效果。

基于改进分数阶快速终端滑模的APF优化控制研究

电力电子化系统的快速发展导致电网中的谐波问题日益严重。为进一步提高有源电力滤波器(activepower filter, APF)的补偿电流跟踪性能和滤波效果,提出一种基于金枪鱼群优化(tuna swarm optimization, TSO)算法的改进型分数阶快速终端滑模控制(improved fractional-order fast terminal sliding mode control, IFOFTSMC)策略。首先,搭建三相并联型APF的数学模型,并考虑其参数扰动。其次,提出一种改进的分数阶快速终端滑模控制策略,其中滑模面采用分数阶快速终端滑模与分数阶PI相结合,并进行了有限时间分析,趋近律采用指数趋近律和幂次趋近律相结合,同时以反双曲正弦函作为切换项。然后,利用TSO算法对所设计控制器的参数和阶次进行优化并获得最优解。最后,通过仿真验证了所提控制方法的有效性。此外,经与相关文献比较进一步证实了所提优化控制算法不仅可以获得最优控制参数和最优分数阶阶次,使系统在有限时间内到达稳定,还能使APF具有更好的电流跟踪性能、滤波效果和更强的鲁棒性。

PEMFC系统建模与空气供给子系统氧气过量比的控制

针对质子交换膜燃料电池空气供给子系统氧气过量比的控制问题,首先,建立面向控制的质子交换膜燃料电池系统的四阶非线性动态模型,构建电堆负载电流与最佳氧气过量比之间的拟合曲线方程;随后,设计一种采用新型复合趋近律的滑模控制器,并利用沙丘猫群优化算法对滑模控制中的参数进行寻优和整定;最后,对改进后的滑模控制器进行仿真验证,并与PID和其余3种滑模控制进行对比分析。仿真结果表明:当电堆负载电流变化时,改进后的滑模控制器可根据阴极流量偏差参数调节空压机的驱动电压,此时系统实时氧气过量比会迅速向最佳氧气过量比靠近,可将其两者之间的偏差控制0.1%之内,其所需的平均调节时间和误差性能指标均优于对比组。

经纱张力的神经非奇异积分快速终端滑模控制

为减小织机长片段经纱张力波动,提高张力控制性能,提出神经非奇异积分快速终端滑模控制(RBF-NIFTSMC)方法。通过对送经系统的受力分析,建立了织机的时变张力控制模型,根据系统存在时变数学模型、未建模动力学和外部干扰未知等特点,设计了一种非奇异积分快速终端滑模控制方法。该控制器可在短时间内到达滑模面,无奇异问题,利用NIFTSMC理论自适应估计未知不确定性,并最终由李雅普诺夫理论证明稳定性。为进一步提高滑模控制性能,将滑模面和变步长神经网络相融合,实现时变滑模面,神经网络的参数由改进天鹰优化(AO)算法优化得到,最终通过仿真实验结果证明所提出的控制方法的有效性。

扫路车线控转向前轮转角的双闭环控制策略

针对智能扫路车线控转向系统前轮转角跟踪精度不高的问题,提出一种基于遗传算法_PID控制与RBF网络自适应滑模控制结合的双闭环控制策略。采用层次分析法得到遗传算法适应度函数的多个控制目标的权重,将适用于扫路车的遗传算法_PID控制器作为内环,用于转角跟踪;采用基于横摆角速度的RBF网络自适应滑模控制作为外环,逼近系统中的不确定成分,提高运行的稳定性。将该控制策略与其他控制策略对比,实验结果表明,该控制策略在提高车辆运行稳定性以及前轮转角跟踪上更具优势。

一种管道工程机器人路径跟踪控制方法

为了提高管道工程机器人在野外环境下路径跟踪的精度,通过研究管道工程机器人在受到侧滑扰动情况下的转向特性,建立基于滑动情况下的运动学模型。提出了一种改进的滑膜观测器用于估计机器人的滑移参数,使用遗传算法对滑膜观测器的增益系数进行优化,使得估计结果更加准确。此外,考虑到因外界干扰导致机器人路径跟踪控制精度低的问题,提出一种改进的滚动时域控制器(RecedingHorizon Control,RHC)用于机器人的路径跟踪控制,将模型中的非线性项与线性项都进行离散化,减小计算难度。实验结果表明,所提出的基于滑移参数估计的RHC路径跟踪控制策略能够快速准确的跟踪目标路径,具有更好的跟踪精度。