自适应模糊滑模软切换的PMSM无速度传感器鲁棒无源控制

针对永磁同步电机(PMSM)转速调节和估计问题,提出一种无速度传感器的PMSM调速系统.利用双曲正切函数代替符号函数,设计了自适应模糊滑模软切换控制器,实现了软切换连续控制,削弱了抖动现象.通过设计鲁棒无源控制器,得到了旋转坐标系下的u_d和u_q.建立了自适应滑模观测器,并给出了速度辨识律,观测器的增益通过求解线性矩阵不等式得到.仿真结果表明了该控制策略与观测器配合的有效性,且控制系统具有良好的动、稳态性能.     

Nonlinear adaptive control of robotic manipulators - hyperstability approach

A nonlinear model reference adaptive controller based on hyperstability approach, is presented for the control of robot manipulators. Use of hyperstability approach simplifies the stability proof of the adaptive system. The unknown parameters of the system, as well as its variable payload, are estimated on line and are adaptive to their actual values; tending to reduce the system error. In addition, any sudden change in the system parameters or payload is detected by the proposed intelligent controller. Robot path tracking, with unknown parameter values and variable payload, is simulated to show the effectiveness of the proposed adaptive control algorithm. Both system output error and parameter estimation error vanish under the proposed parameter adaptation algorithm.     

基于ESO的无速度传感器PMSM系统自适应滑模FCS-MPC

为了提高三相永磁同步电机(PMSM)控制系统的性能,基于反双曲正弦函数的扩张状态观测器(ESO)技术,提出一种新颖的无速度传感器自适应滑模有限控制集模型预测控制(FCS-MPC)策略,采用ESO技术构造PMSM系统转速和反电动势的观测器,实现对电机转速和反电动势快速准确估计.用带有负载ESO的自适应滑模控制作为系统的转速调节器,以提高系统的鲁棒性;利用基于快速矢量选择的FCS-MPC策略,达到减少转矩脉动、降低系统算法计算量的目的.仿真结果表明,基于ESO的无速度传感器自适应滑模FCS-MPC策略能够使PMSM系统可靠稳定运行,达到满意的转矩和转速控制效果.与基于积分型滑模面的自适应滑模FCS-MPC策略相比,所提出的控制策略能使系统具有良好的动态性能和抗负载干扰能力.     

基于多层蚁群算法的变论域模糊控制

针对模糊控制器控制精度不高、自适应能力有限等问题,提出一种变论域自适应模糊控制方式.首先在对离散蚁群算法改进的基础上,提出用于连续域寻优的多层蚁群算法.其通过将解空间分成有限网格,并且算法在迭代过程中采用三个阶段的搜索策略,每个阶段采用异构搜索机制.然后根据系统性能利用改进算法动态调整伸缩因子,从而构成基于多层蚁群算法的变论域自适应模糊控制器.最后将此控制器用于中厚板液压位置伺服系统中.仿真结果表明,采用自适应模糊控制器的伺服系统收敛速度明显加快,此控制策略在适应能力与鲁棒性好于其它控制方式.     

An adaptive artificial bee colony algorithm based on objective function value information

Artificial bee colony (ABC) algorithm is a novel biological-inspired optimization algorithm, which has many advantages compared with other optimization algorithm, such as less control parameters, great global optimization ability and easy to carry out. It has proven to be more effective than some evolutionary algorithms (EAs), particle swarm optimization (PSO) and differential evolution (DE) when testing on both benchmark functions and real issues. ABC, however, its solution search equation is poor at exploitation. For overcoming this insufficiency, two new solution search equations are proposed in this paper. They apply random solutions to take the place of the current solution as base vector in order to get more useful information. Exploitation is further improved on the basis of enhancing exploration by utilizing the information of the current best solution. In addition, the information of objective function value is introduced, which makes it possible to adjust the step-size adaptively. Owing to their respective characteristics, the new solution search equations are combined to construct an adaptive algorithm called MTABC. The methods our proposed balance the exploration and exploitation of ABC without forcing severe extra overhead in respect of function evaluations. The performance of the MTABC algorithm is extensively judged on a set of 20 basic functions and a set of 10 shifted or rotated functions, and is compared favorably with other improved ABCs and several state-of-the-art algorithms. The experimental results show that the proposed algorithm has a higher convergence speed and better search ability for almost all functions.     

Hybrid recurrent wavelet neural network control of PMSM servo-drive system for electric scooter

Due to nonlinear uncertainties of the electric scooter such as nonlinear friction force of the transmission belt and clutch, these will lead to degenerate tracking responses in command current and speed of the permanent magnet synchronous motor (PMSM) servo-driven electric scooter. In this study a novel hybrid recurrent wavelet neural network (HRWNN) control system is proposed to raise robustness of the PMSM servo-driven electric scooter under the occurrence of the variation of rotor inertia and load torque disturbance. First, the field-oriented mechanism is applied to formulate the dynamic equation of the PMSM servo drive. Then, a novel HRWNN control system is proposed to control motion for a PMSM servo-driven electric scooter. The HRWNN control system composed of a supervisor control, a RWNN and a compensated control with adaptive law. The online parameter training methodology with adaptive law in the RWNN is derived based on the Lyapunov stability theorem. Then adaptive law of the parameter in the RWNN can be updated by using the gradient descent method and the backpropagation algorithm. Finally, the effectiveness of the proposed control scheme is verified by experimental results.     

Bollinger bands approach on boosting ABC algorithm and its variants

In this study, a new algorithm that will improve the performance and the solution quality of the ABC (artificial bee colony) algorithm, a swarm intelligence based optimization algorithm is proposed. ABC updates one parameter of the individuals before the fitness evaluation. Bollinger bands is a powerful statistical indicator which is used to predict future stock price trends. By the proposed method an additional update equation for all ABC-based optimization algorithms is developed to speed up the convergence utilizing the statistical power of the Bollinger bands. The proposed algorithm was tested against classical ABC algorithm and recent ABC variants. The results of the proposed method show better performance in comparison with ABC-based algorithm with one parameter update in convergence speed and solution quality.     

永磁同步电动机新型自适应滑模控制

永磁同步电动机(PMSM)是多变量、强耦合、非线性时变系统, 对外界干扰及内部参数摄动较为敏感, 为提高系统的鲁棒性, 本文提出一种基于非线性滑模面的自适应滑模变结构控制方法. 根据复合非线性反馈控制理论, 为PMSM滑模控制系统设计非线性滑模面, 通过实时改变控制系统的阻尼系数来提高PMSM伺服系统的瞬态响应性能. 在PMSM伺服系统外界扰动及内部参数摄动的上下界未知的情况下, 采用自适应参数校正律来调节控制增益的大小, 改善了系统的抖振现象. 此外, 对电机的电流及转速进行了饱和限制, 使得所设计的伺服控制系统可用于大范围的位移跟踪. 仿真结果表明, 与基于线性滑模面的控制器相比较, 本文所设计的基于非线性滑模面的自适应滑模控制器使得电机转子位移能够更快且无超调的到达给定值, 且系统的抖振现象明显减弱.     

基于串级无模型自适应的外传子永磁同步电机跟踪控制

为解决现有煤矿皮带机传动系统占地空间大、传动效率低、维护频度高等问题,提出一种外转子永磁同步电机(outer-rotor PMSM)直接驱动结构,并将一种串级结构的无模型自适应控制算法应用到皮带机驱动电机的速度控制中。根据矿井皮带机的运行要求,给出该电机详细的设计参数和数学模型,设定了启动和稳态时的理想速度曲线。利用无模型自适应控制算法设计出串级无模型自适应控制律,并给出串级控制系统结构图。通过Matlab软件对外转子PMSM在煤矿皮带机的直驱伺服系统按照理想的启动“S”型曲线进行仿真,其结果表明：串级无模型自适应控制算法降低了速度跟踪误差,提高了调速控制精度,有效地抑制了系统噪声和负载变化带来的干扰,实现了皮带机良好的启动和稳态特性。     

FDI based on an adaptive observer for current and speed sensors of PMSM drives

This paper deals with a new method of current and speed sensors faults detection isolation (FDI) and identification for a permanent magnet synchronous motor (PMSM) drives. A new state variable is introduced so that an augmented system can be constructed to treat PMSM sensor faults as actuator faults. This method uses the PMSM model and a bank of adaptive observers to generate residuals. The residuals results are used for sensor fault detection. A logic algorithm is built in such a way to isolate and identify the faulty sensor for a stator phase current fault after detecting the fault occurrence. Simulation results are presented to illustrate the functionality of theoretical developments. Experimental results with 1.1-kW PMSM have validated the effectiveness of the proposed FDI method. The experimental implementation is carried out on powerful dSpace DS1103 controller board based on the DSP TMS320F240.     

A novel IMC controller based on bacterial foraging optimization algorithm applied to a high speed range PMSM drive

This paper is a proposal of a modified internal model control based on an intelligent technique. The indirect field oriented control strategy (IFOC) is used as a permanent magnet synchronous motor (PMSM) drive platform. Neural network controller and estimator are respectively added to replace the conventional speed regulator and the speed encoder in the global drive scheme. A wide speed working range is considered and high speed mode is incorporated in the study testes. In the IFOC inner control loops, the commonly used synchronous frame conventional proportional plus integral (PI) controllers are replaced by two modified internal model control (IMC) regulators. Therefore, a method based on the bacterial foraging optimization (BFO) algorithm is performed to optimize and adjust the IMC low pass filter parameters. The robustness of the proposed PMSM sensorless drive scheme is confirmed by simulation tests in the MATLAB/SIMULINK. Moreover, a comparative evaluation results are illustrated to prove the effectiveness of the proposed control algorithm according to different controllers combinations.     

Development and investigation of efficient artificial bee colony algorithm for numerical function optimization

Artificial bee colony algorithm (ABC), which is inspired by the foraging behavior of honey bee swarm, is a biological-inspired optimization. It shows more effective than genetic algorithm (GA), particle swarm optimization (PSO) and ant colony optimization (ACO). However, ABC is good at exploration but poor at exploitation, and its convergence speed is also an issue in some cases. For these insufficiencies, we propose an improved ABC algorithm called I-ABC. In I-ABC, the best-so-far solution, inertia weight and acceleration coefficients are introduced to modify the search process. Inertia weight and acceleration coefficients are defined as functions of the fitness. In addition, to further balance search processes, the modification forms of the employed bees and the onlooker ones are different in the second acceleration coefficient. Experiments show that, for most functions, the I-ABC has a faster convergence speed and better performances than each of ABC and the gbest-guided ABC (GABC). But I-ABC could not still substantially achieve the best solution for all optimization problems. In a few cases, it could not find better results than ABC or GABC. In order to inherit the bright sides of ABC, GABC and I-ABC, a high-efficiency hybrid ABC algorithm, which is called PS-ABC, is proposed. PS-ABC owns the abilities of prediction and selection. Results show that PS-ABC has a faster convergence speed like I-ABC and better search ability than other relevant methods for almost all functions.     

Artificial Bee Colony Algorithm Based on Adaptive Search Equation and Extended Memory

From the perspective of psychology, a modified artificial bee colony algorithm (ABC, for short) based on adaptive search equation and extended memory (ABCEM, for short) for global optimization is proposed in this paper. In the proposed ABCEM algorithm, an extended memory factor is introduced into store employed bees’ and onlooker bees’ historical information comprising recent food sources, personal best food sources, and global best food sources, and the solution search equation for the employed bees is equipped with adaptive ability. Moreover, a parameter is employed to describe the importance of the extended memory. Furthermore, the extended memory is added to two solution search equations for the employed bees and the onlookers to improve the quality of food source. To evaluate the proposed algorithm, experiments are conducted on a set of numerical benchmark functions. The results show that the proposed algorithm can balance the exploration and exploitation, and can improve the accuracy of optima solutions and convergence speed compared with other current improved ABCs for global optimization in most of the tested functions.     

Adaptive fuzzy robust control of PMSM with smooth inverse based dead-zone compensation

It is a challenging work to design high precision/high performance motion controller for permanent magnet synchronous motor (PMSM) due to some difficulties, such as varying operating conditions, parametric uncertainties and external disturbances. In order to improve tracking control performance of PMSM, this paper proposes an adaptive fuzzy robust control (AFRC) algorithm with smooth inverse based dead-zone compensation. Instead of nonsmooth dead-zone inverse which would cause the possible control signal chattering phenomenon, a new smooth dead-zone inverse is proposed for non-symmetric dead-zone compensation in PMSM system. AFRC controller is synthesized by combining backstepping technique and small gain theorem. Discontinuous projectionbased parameter adaptive law is used to estimate unknown system parameters. The Takagi-Sugeno fuzzy logic systems are employed to approximate the unstructured dynamics. Robust control law ensures the robustness of closed loop control system. The proposed AFRC algorithm with smooth inverse based dead-zone compensation is verified on a practical PMSM control system. The comparative experimental results indicate that the smooth inverse for non-symmetric dead-zone nonlinearity can effectively avoid the chattering phenomenon which would be caused by nonsmooth dead-zone inverse, and the proposed control strategy can improve the PMSM output tracking performance.     

无传感器PMSM中基于IGSO优化EKF的速度估计方法

为了提高无传感器永磁同步电机(PMSM)控制系统中速度控制性能,提出一种基于改进群搜索优化(IGSO)算法的扩展卡尔曼滤波(EKF)速度估计方案。首先,分析了PMSM磁场定向控制(FOC)系统模型;然后,将电机的d-q轴电压、电流和转子速度作为状态变量,构建EKF中的状态方程来估计转速和负载。同时,为了提高EKF的估计性能,以估计值与实际值的平方误差积分(ISE)作为适应度函数,通过IGSO算法来优化EKF中的噪声协方差矩阵Q和R,以此获得最优参数。仿真结果表明,提出的控制系统能够精确估计出电机转速并进行有效控制。     

Multichannel Bin-Wise Robust Frequency-Domain Adaptive Filtering and Its Application to Adaptive Beamforming

Least-squares error (LSE) or mean-squared error (MSE) optimization criteria lead to adaptive filters that are highly sensitive to impulsive noise. The sensitivity to noise bursts increases with the convergence speed of the adaptation algorithm and limits the performance of signal processing algorithms, especially when fast convergence is required, as for example, in adaptive beamforming for speech and audio signal acquisition or acoustic echo cancellation. In these applications, noise bursts are frequently due to undetected double-talk. In this paper, we present impulsive noise robust multichannel frequency-domain adaptive filters (MC-FDAFs) based on outlier-robust M-estimation using a Newton algorithm and a discrete Newton algorithm, which are especially designed for frequency bin-wise adaptation control. Bin-wise adaptation and control in the frequency-domain enables the application of the outlier-robust MC-FDAFs to a generalized sidelobe canceler (GSC) using an adaptive blocking matrix for speech and audio signal acquisition. It is shown that the improved robustness leads to faster convergence and to higher interference suppression relative to nonrobust adaptation algorithms, especially during periods of strong interference     

基于风速估计的风力机状态反馈滑模容错控制

针对风力机变桨距执行机构突变故障,提出了基于风速估计的自适应状态反馈滑模容错控制策略.首先,设计了基于自适应状态反馈滑模理论的鲁棒主动容错控制器,并结合全阶补偿器对控制律进行设计;然后,利用基于变速灰狼优化算法的组合径向基函数神经网络实现风速估计,可以改善风速测量精度并提高控制系统可靠性;最后,根据线性矩阵不等式和Lyapunov理论对控制器稳定性进行讨论,并与现有控制策略进行比较.仿真结果表明,在健康/故障的变桨距执行机构条件下,所提容错控制方法均能获得较好的控制效果.     

Environmental Adaptive Control of a Snake-like Robot With Variable Stiffness Actuators

This work investigates adaptive stiffness control and motion optimization of a snake-like robot with variable stiffness actuators. The robot can vary its stiffness by controlling magnetorheological fluid(MRF) around actuators. In order to improve the robot's physical stability in complex environments, this work proposes an adaptive stiffness control strategy. This strategy is also useful for the robot to avoid disturbing caused by emergency situations such as collisions. In addition, to obtain optimal stiffness and reduce energy consumption, both torques of actuators and stiffness of the MRF braker are considered and optimized by using an evolutionary optimization algorithm. Simulations and experiments are conducted to verify the proposed adaptive stiffness control and optimization methods for a variable stiffness snake-like robots.     

Dynamic mechanism-assisted artificial bee colony optimization for image segmentation of COVID-19 chest X-ray

The artificial bee colony optimization (ABC) algorithm operates efficiently and converges well but still suffers from the problem of easily falling into local optimum, and there is room for improving the convergence speed. For this reason, this paper proposes a dynamic mechanism-assisted ABC algorithm (EABC), which contains a dynamic approximation strategy for the optimal solution and a periodic variable food source number strategy. The dynamic approximation of the optimal solution strategy improves the swarm position update formulation and increases the pre-convergence speed of the ABC algorithm. Utilizing a periodic variable food source number scheme allows for more rapid algorithm convergence while simultaneously producing higher variability and diminishing the chances of the algorithm becoming trapped in local optima. In addition, this paper proposes a multi-threshold image segmentation (MTIS) model for COVID-19 X-ray chest images based on EABC. In this paper, the optimization performance of EABC is verified on the benchmark function of IEEE CEC 2017. The effectiveness of the EABC-based MTIS model is also validated on COVID-19 X-ray chest images.     

永磁同步电机调速系统的多维泰勒网逆控制

针对永磁同步电机(PMSM)的高性能控制问题,在充分考虑时变特性、不确定性以及测量噪声等随机因素的基础上,通过PMSM的逆系统将被控对象补偿成为具有线性传递关系的系统,提出一种基于改进自适应逆控制的控制方案.采用矢量控制的双闭环控制结构,将多维泰勒网逆控制方法引入速度环.首先,对PMSM数学模型的可逆性进行证明以解决非线性系统逆建模的存在性问题;然后,建立新颖的动态网络化控制器-----多维泰勒网(MTN),其具有结构简单、计算复杂度低的优点;最后,为了实现高精度的速度控制,将3个MTN分别作为实现系统建模的自适应模型辨识器、逆建模的自适应逆控制器和噪声干扰消除的非线性自适应滤波器,并将PMSM的动态响应控制和消除干扰的控制分为相对独立的过程进行,同时实现最优控制.仿真结果表明,所提出控制方案能够实现PMSM伺服系统精确的速度控制,具有良好的跟踪性能和较强的抗干扰能力.