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.

Aeroengine Nonlinear Sliding Mode Control Based on Artificial Bee Colony Algorithm

For a class of aeroengine nonlinear systems,a novel nonlinear sliding mode controller(SMC)design method based on artificial bee colony(ABC)algorithm is proposed.In view of the strong nonlinearity and uncertainty of aeroengines,sliding mode control strategy is adopted to design controller for the aeroengine.On basis of exact linearization approach,the nonlinear sliding mode controller is obtained conveniently.By using ABC algorithm,the parameters in the designed controller can be tuned to achieve optimal performance,resulting in a closedloop system with satisfactory dynamic performance and high steady accuracy.Simulation on an aeroengine verifies the effectiveness of the presented method.

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.

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.

Modeling and Robust Backstepping Sliding Mode Control with Adaptive RBFNN for a Novel Coaxial Eight-rotor UAV

This paper focuses on the robust attitude control of a novel coaxial eight-rotor unmanned aerial vehicles (UAV) which has higher drive capability as well as greater robustness against disturbances than quad-rotor UAV. The dynamical and kinematical model for the coaxial eight-rotor UAV is developed, which has never been proposed before. A robust backstepping sliding mode controller (BSMC) with adaptive radial basis function neural network (RBFNN) is proposed to control the attitude of the eightrotor UAV in the presence of model uncertainties and external disturbances. The combinative method of backstepping control and sliding mode control has improved robustness and simplified design procedure benefiting from the advantages of both controllers. The adaptive RBFNN as the uncertainty observer can effectively estimate the lumped uncertainties without the knowledge of their bounds for the eight-rotor UAV. Additionally, the adaptive learning algorithm, which can learn the parameters of RBFNN online and compensate the approximation error, is derived using Lyapunov stability theorem. And then the uniformly ultimate stability of the eight-rotor system is proved. Finally, simulation results demonstrate the validity of the proposed robust control method adopted in the novel coaxial eight-rotor UAV in the case of model uncertainties and external disturbances. © 2014 Chinese Association of Automation.

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 Fractional-Order Fast Terminal Sliding Mode Control for Some Chaotic Microcomponents

In this paper, we propose a novel fractional-order fast terminal sliding mode control method, based on an integer-order scheme, to stabilize the chaotic motion of two typical microcomponents. We apply the fractional Lyapunov stability theorem to analytically guarantee the asymptotic stability of a system characterized by uncertainties and external disturbances. To reduce chattering, we design a fuzzy logic algorithm to replace the traditional signum function in the switching law. Lastly, we perform numerical simulations with both the fractional-order and integer-order control laws. Results show that the proposed control law is effective in suppressing chaos. © 2017, Tianjin University and Springer-Verlag Berlin Heidelberg.

Controlling chaos based on a novel intelligent integral terminal sliding mode control in a rod-type plasma torch

An integral terminal sliding mode controller is proposed in order to control chaos in a rod-type plasma torch system.In this method, a new sliding surface is defined based on a combination of the conventional sliding surface in terminal sliding mode control and a nonlinear function of the integral of the system states. It is assumed that the dynamics of a chaotic system are unknown and also the system is exposed to disturbance and unstructured uncertainty. To achieve a chattering-free and high-speed response for such an unknown system, an adaptive neuro-fuzzy inference system is utilized in the next step to approximate the unknown part of the nonlinear dynamics. Then, the proposed integral terminal sliding mode controller stabilizes the approximated system based on Lyapunov＇s stability theory. In addition, a Bee algorithm is used to select the coefficients of integral terminal sliding mode controller to improve the performance of the proposed method. Simulation results demonstrate the improvement in the response speed, chattering rejection, transient response,and robustness against uncertainties.

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.

Simulation research of discrete sliding mode control for active power filter

Because of the widely-used nonlinear loads, the problems of harmonics and low power factor in power systems are becoming more and more serious. In view of the above problems, a shunt three-phase active power filter （SAPF） based on discrete sliding mode control to suppress the harmonics was designed and the power factor improved. First, built the mathematical model of SAPF. Then, controlled the switch through analyzing the harmonic current that was controlled signal using discrete sliding mode control. Through simulation using MATLAB, verified the validity of the algorithm. By the simulation waveforms, it can be found that after the load current containing a large number of harmonics passes the active power filter, the waveform of the power current approximately becomes a sine wave and the harmonics is significantly reduced. As a result, it plays a good role in purifying power systems. Moreover, from the waveforms of harmonic current and power current, it can be found that the algorithm has a faster tracking speed. The waveform can fect.

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.

Marine Predator Algorithm-based Sliding Mode Control of a Novel Motion Simulator for High Column Sloshing Experiments

Sloshing experiment is crucial to determine the reaction performance of regeneration columns on an offshore floating platform.A novel type of column motion simulating device and a Marine Predator Algorithm-based Sliding Mode Controller(MPA-SMC)are proposed for such sloshing experiments.The simulator consists of a Stewart platform and a steel framework.The Stewart platform is located at the column's center of gravity(CoG)and supported by the steel framework.The platform's hydraulic servo system is controlled by a sliding mode controller with parameters optimized by MPA to improve robustness and precision.A numerical sloshing experiment is conducted using the proposed device and controller.The results show that the novel motion simulator has lower torque during the column sloshes,and the proposed controller performs better than a well-tuned PID controller in terms of target tracking precision and anti-interference capability.

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.

Control Strategy for a Quadrotor Based on a Memetic Shuffled Frog Leaping Algorithm

This work presents a memetic Shuffled Frog Leaping Algorithm(SFLA)based tuning approach of an Integral Sliding Mode Controller(ISMC)for a quadrotor type of Unmanned Aerial Vehicles(UAV).Based on the Newton–Euler formalism,a nonlinear dynamic model of the studied quadrotor is firstly established for control design purposes.Since the main parameters of the ISMC design are the gains of the sliding surfaces and signum functions of the switching control law,which are usually selected by repetitive and time-consuming trials-errors based procedures,a constrained optimization problem is formulated for the systematically tuning of these unknown variables.Under time-domain operating constraints,such an optimization-based tuning problem is effectively solved using the proposed SFLA metaheuristic with an empirical comparison to other evolutionary computation-and swarm intelligence-based algorithms such as the Crow Search Algorithm(CSA),Fractional Particle Swarm Optimization Memetic Algorithm(FPSOMA),Ant Bee Colony(ABC)and Harmony Search Algorithm(HSA).Numerical experiments are carried out for various sets of algorithms’parameters to achieve optimal gains of the sliding mode controllers for the altitude and attitude dynamics stabilization.Comparative studies revealed that the SFLA is a competitive and easily implemented algorithm with high performance in terms of robustness and non-premature convergence.Demonstrative results verified that the proposed metaheuristicsbased approach is a promising alternative for the systematic tuning of the effective design parameters in the integral sliding mode control framework.

Revised barrier function-based adaptive finite-and fixed-time convergence super-twisting control

This paper presents an adaptive gain,finite-and fixedtime convergence super-twisting-like algorithm based on a revised barrier function,which is robust to perturbations with unknown bounds.It is shown that this algorithm can ensure a finite-and fixed-time convergence of the sliding variable to the equilibrium,no matter what the initial conditions of the system states are,and maintain it there in a predefined vicinity of the origin without violation.Also,the proposed method avoids the problem of overestimation of the control gain that exists in the current fixed-time adaptive control.Moreover,it shows that the revised barrier function can effectively reduce the computation load by obviating the need of increasing the magnitude of sampling step compared with the conventional barrier function.This feature will be beneficial when the algorithm is implemented in practice.After that,the estimation of the fixed convergence time of the proposed method is derived and the impractical requirement of the preceding fixed-time adaptive control that the adaptive gains must be large enough to engender the sliding mode at time t=0 is discarded.Finally,the outperformance of the proposed method over the existing counterpart method is demonstrated with a numerical simulation.

基于混沌控制的采摘机器人防碰撞系统设计

针对采摘机器人在作业过程中遇到障碍物导致路径规划出现混沌现象的问题,基于混沌控制对采摘机器人防碰撞系统进行设计,并采用模糊自适应控制算法和滑模变结构控制算法结构的方式对系统进行控制。为了验证防碰撞系统的有效性,对采摘机器人进行了防碰撞和采摘仿真试验,结果表明:采摘机器人在作业过程中可以避开障碍物,按照规划路线到达预设位置,完成果蔬的采摘。

基于路面等级识别的车辆半主动悬架内外环控制

针对车辆在不同路面等级下对悬架动态性能与馈能特性需求不同的问题,提出一种基于RF-XGBoost路面等级识别算法的半主动悬架内外环控制策略。利用随机森林(Random Forest,RF)模型对极端梯度提升(Extreme Gradient Boosting,XGBoost)算法进行优化,搭建RF-XGBoost算法模型对路面等级进行识别。将路面等级与悬架控制策略相结合,设计外环为天地棚控制,内环为自适应滑模控制的内外环控制,实现非线性悬架的自适应控制。仿真结果表明,相比传统混合天地棚控制的悬架,内外环控制下的悬架在A级路面下簧载质量加速度降低15.52%,并实现50.4 W的振动能量回收,在B、C级路面下簧载质量加速度分别降低15.09%、16.72%,轮胎动载荷分别降低11.63%、11.42%,在D级路面下轮胎动载荷降低14.12%。台架试验的结果与仿真分析的结果基本一致,表明所设计的自适应内外环控制有效。

角钢塔螺栓的紧固控制技术

针对角钢塔螺栓紧固作业需求,研究了一种轻量化、扭矩可准确控制的电动扳手.为了解决轻量化问题,提出了一种自抗扰控制器与滑模观测器融合算法,实现了无扭矩传感器的电动扳手扭矩参数估计与控制.首先,将自抗扰控制器引进扭矩环、速度环,在对其总扰动量进行估计的同时得到角加速度的估计值,然后将该角加速度值作为二阶滑模观测器的输入进行紧固扭矩值的估计,解决了二阶滑模观测器中变量的导数值难以获得的问题.仿真和实验结果表明,与传统PID控制器、滑模控制器相比,所提方法可实现扭矩的准确估计,具有扭矩调节时间短,超调小的特点.

基于双侧电机同步转速调节的履带机器人直行控制研究

由于路面阻力不均衡使履带机器人双侧电机负载扰动不一致,造成两侧电机转速不同步,导致直行运动跑偏。因此,需要提高双电机的转速同步性能。针对路面扰动等因素,考虑到滑模控制算法对参数变化不敏感,且能够抑制一定范围内的不确定扰动。故基于交叉耦合控制方法加入滑模同步控制算法进行改进。将传统交叉耦合控制方法和改进的交叉耦合滑模同步控制方法进行对比仿真和实验。结果表明,突变扰动下,改进后同步误差和直行跑偏量得到明显减小,同步直行性能有了显著提高。

基于超扭曲滑模的双定子绕组感应电机转速磁通控制

针对双定子绕组感应电机DSWIMs(dual-stator winding induction machines)在传统直接转矩和磁通控制下转矩、磁通和电流纹波大,在低速下控制磁通难度大、噪声大等问题,提出1种基于超扭曲滑模控制器STSMC(super twisting sliding mode controller)的DSWIMs新型转速和磁通直接控制方法。通过设计满足李雅普诺夫稳定条件的有限时间收敛误差为0的非线性控制器,提出1种DSWIMs新型转矩分配算法,可使DSWIMs在较宽的转速范围内运行,并包括零转速,所需电磁转矩由2组绕组根据其额定功率提供。此外,针对DSWIMs设计了1种基于滑模控制的全阶观测器,能够高精度地估计绕组磁通、磁通角和转子转速,以实现最优磁通状态。最后,在1个3.3 kWDSWIM驱动系统上进行实验测试,评估所提DSWIM控制方案的性能,实验结果证明了所提控制方法、转矩分配算法和全阶观测器在不同速度区域的有效性。