FPGA-based adaptive backstepping control system using RBFN for linear induction motor drive

A field-programmable gate array (FPGA)-based adaptive backstepping control system with radial basis function network (RBFN) observer is proposed to control the mover position of a linear induction motor (LIM). First, the indirect field-oriented mechanism is adopted for controlling the LIM. Next, a backstepping control law is designed step by step for the tracking control of periodic reference trajectories, in which the uncertainties are lumped by a conservative constant. However, the lumped uncertainty is unknown and difficult to obtain in advance in practical applications. Therefore an RBFN is derived to observe the lumped uncertainty in realtime, and an adaptive backstepping control system with RBFN observer is resulted. Then, an FPGA chip is adopted to implement the indirect field-oriented mechanism and the developed control algorithms for possible low-cost, high-performance industrial applications. The effectiveness of the proposed control scheme is verified by some simulated and experimental results. By using the adaptive backstepping control system with RBFN observer, the FPGA-based LIM drive possesses the advantages of good transient control performance and robustness to uncertainties in the tracking of periodic reference trajectories.     

一种球形机器人的自适应鲁棒轨迹跟踪控制

针对一种不确定球形移动机器人的轨迹跟踪问题,提出一种基于不确定性上界估计的自适应滑模控制策略。所提出的控制策略无需预知系统不确定性上界值,能够利用自适应机制对不确定性上界进行在线估计。与已有控制方法相比,所提出的控制策略对于系统不确定性具有较好的适应性。分析球形机器人的非完整运动学约束,基于带约束的拉格朗日方程建立系统的动力学模型,并利用零空间法和输入变换对动力学方程进行简化处理。基于所得系统运动模型,采用李亚普诺夫稳定性理论选取滑模控制律和参数自适应率。应用Barbalat引理证明系统滑动面的渐近稳定性,仿真和实验结果表明所提出的控制策略具有良好的鲁棒性和控制性能。     

欠驱动无人艇直线航迹跟踪的反步自适应动态滑模控制

针对喷水推进型欠驱动无人艇的由艇艏摇非线性响应模型和舵机伺服系统组成的直线航迹控制系统,在考虑模型参数不确定性和外界干扰随机性特点的情况下,研究了一种反步自适应动态滑模控制方法.首先利用全局微分同胚坐标变换将原系统转化为具有下三角特征的非线性系统,然后基于反步设计法和动态滑模控制理论,设计了反步自适应动态滑模控制器,并利用Lyapunov稳定性理论,证明在该控制器的作用下,直线航迹控制系统是全局渐近稳定的.仿真对比试验表明,该控制器对模型摄动和外界干扰不敏感,具有强鲁棒性和自适应性.     

柔性机械臂的轨迹跟踪与振动模糊控制

由于柔性机械臂的动态模型较为复杂,传统的基于模型的控制策略一般难以处理.基于PD型模糊控制器,提出一种基于新型的模糊控制的柔性机械臂轨迹跟踪与振动控制策略.首先推导用于柔性机械臂角控制的PD型并联模糊控制器,随后将其改进,提出了一种非并联型模糊控制器,并结合输入整形策略,推导了一种新型的柔性机械臂的混合模糊控制器.在此基础上,构建了柔性机械臂实验平台,开展实时控制实验,给出了相关的时域和频域控制效果.相关的实验结果表明,所提出的新型模糊控制器能够较好地完成柔性机械臂的轨迹跟踪任务,并显著降低柔性机械臂的振动.     

Direct torque and flux control of an IPM synchronous motor drive using a backstepping approach

A novel direct torque and flux controlled interior permanent magnet synchronous motor (IPMSM) drive using an adaptive backstepping technique is presented. Closed-loop regulation of the speed, torque and stator flux linkage is achieved by a non-linear controller. Using Lyapunov analysis, the stability of the controller is guaranteed. The reference voltage vectors are generated by a space vector modulation (SVM) unit, which replaces the switching table in the conventional direct torque control (DTC) scheme. While retaining its main advantages, the proposed backstepping controller features reduced torque and flux ripples as compared to the classical DTC. On the one hand, a comparison with the proportional-integral (PI) direct torque and flux control (DTFC) scheme shows that the proposed approach is superior. Experimental results confirm the effectiveness of the proposed method.     

Model reference adaptive control-based adaptive current control scheme of a PM synchronous motor with an improved servo performance

A model reference adaptive control (MRAC)-based current control scheme of a PM synchronous motor with an improved servo performance is presented. Although the predictive current control is known to give ideal transient and steady-state responses among various PWM inverter-fed current control schemes for a PM synchronous motor, its steady-state response may be degraded under the motor parameter variations. To overcome such a limitation, the disturbances caused by the parameter variations will be estimated using an MRAC technique and compensated by a feedforward manner. Thus, the steady-state control performance can be effectively improved, while retaining its good dynamic performance. The proposed control scheme does not require the measurement of the phase voltage unlike the conventional disturbance estimation scheme using observer. This can be an effective way considering the phase voltage contains much harmonics as well as noise. The asymptotic stability of the overall system is proved and the adaptation laws are derived by the Lyapunov stability theory. The proposed scheme is implemented using DSP TMS320C31 and the effectiveness is verified through the comparative simulations and experiments.     

Online detection of the breathing crack using an adaptive tracking technique

Summary Early detection of structural damage is an important goal of any structural health monitoring system. Among numerous data analysis techniques, those which are used for online damage detection have received considerable attention recently, although the problem of online detection in continuous structures, for example beams, is quite challenging. In this paper, it is shown how the type, the size and the location of breathing cracks are identified online with the use of the records which are gathered from a continuous beam. For determining the existence of a breathing crack in a beam, its vibrating behavior is simulated. The algorithm of the least square estimation with the use of adaptive tracking is employed for identification purposes. This algorithm is capable of detecting the abrupt changes in problem parameters and traces its variations. With the use of reducing domain algorithm, this identification method shows better results and can detect the breathing crack in beams more efficiently. Finally, it is shown that with the use of sufficient mode shapes the method is capable of identifying the breathing crack in beams and frames. The efficiency of the proposed algorithm is shown through some case studies.     

Woofer-tweeter control in an adaptive optics system using a Fourier reconstructor

Future adaptive optics systems seeking a higher degree of correction may need to split the command between two deformable mirrors (DMs), the woofer and the tweeter. We first present a method to specify the order of correction of the woofer DM for a given tweeter stroke requirement using an analytical and Monte Carlo approach. We then develop a computationally efficient algorithm to split the command between the two DMs in the case where a Fourier reconstructor is used. We find that the computational efficiency can be dramatically improved by splitting the command in Fourier space and by sending to the woofer only the modes it can reproduce accurately.     

Pneumatic motor speed control by trajectory tracking fuzzy logic controller

In this study, trajectory tracking fuzzy logic controller (TTFLC) is proposed for the speed control of a pneumatic motor (PM). A third order trajectory is defined to determine the trajectory function that has to be tracked by the PM speed. Genetic algorithm (GA) is used to find the TTFLC boundary values of membership functions (MF) and weights of control rules. In addition, artificial neural networks (ANN) modelled dynamic behaviour of PM is given. This ANN model is used to find the optimal TTFLC parameters by offline GA approach. The experimental results show that designed TTFLC successfully enables the PM speed track the given trajectory under various working conditions. The proposed approach is superior to PID controller. It also provides simple and easy design procedure for the PM speed control problem.     

基于自适应模糊PID控制的四旋翼飞行器的自主跟踪

为了提高四旋翼飞行器控制系统的动态性能和工作效率,研究了自适应模糊PID控制。目前在实际应用中,四旋翼的控制算法主要采用经典PID算法和模糊控制算法,然而经典PID算法,系统容易超调,动态性能较差;模糊控制算法,系统的稳态误差难以消除,工作效率不高。为此采用了经典PID与模糊控制的分段策略,提出了一种新的控制算法:当误差较大时,为提高系统的动态性能,控制器选取模糊控制算法;当误差较小时,为减小稳态误差,提高工作效率,选取经典PID算法。完成了悬停飞行实验,对自适应模糊PID、模糊控制和经典PID三种控制算法进行性能对比,结果表明自适应模糊PID算法能有效解决上述问题。采用自适应模糊PID算法完成了自主跟踪实验,将该算法成功应用到导航制导领域。     

An adaptive recurrent radial basis function network tracking controller for a two-dimensional piezo-positioning stage

An adaptive recurrent radial basis function network (ARRBFN) tracking controller for a two-dimensional piezo-positioning stage is proposed in this study. First, a mathematical model that represents the dynamics of the two-dimensional piezo-positioning stage is proposed. In this model, a hysteresis friction force that describes the hysteresis behavior of one-dimensional motion is used; and a nonconstant stiffness with the cross-coupling dynamic due to the effect of bending of a lever mechanism in x and y axes also is included. Then, according to the proposed mathematical model, an ARRBFN tracking controller is proposed. In the proposed ARRBFN control system, a recurrent radial basis function network (RRBFN) with accurate approximation capability is used to approximate an unknown dynamic function. The adaptive learning algorithms that can learn the parameters of the RRBFN on line are derived using Lyapunov stability theorem. Moreover, a robust compensator is proposed to confront the uncertainties, including approximation error, optimal parameter vectors, higher-order terms in Taylor series. To relax the requirement of the value of the lumped uncertainty in the robust compensator, an adaptive law is investigated to estimate the lumped uncertainty. Using the proposed control scheme, the position tracking performance is substantially improved and the robustness to uncertainties, including hysteresis friction force and cross-coupling stiffness, can be obtained as well. The tracking performance and the robustness to external load of the proposed ARRBFN control system are illustrated by some experimental results.     

Multiobjective optimization with an adaptive weight determination scheme using the concept of hyperplane

Finding an optimum design that satisfies all performances in a design problem is very challenging. To overcome this problem, multiobjective optimization methods have been researched to obtain Pareto optimum solutions. Among the different methods, the weighted sum method is widely used for its convenience. However, since the different weights do not always guarantee evenly distributed solutions on the Pareto front, the weights need to be determined systematically. Therefore, this paper presents a multiobjective optimization using a new adaptive weight determination scheme. Solutions on the Pareto front are gradually found with different weights, and the values of these weights are adaptively determined by using information from the previously obtained solutions' positions. For an n-objective problem, a hyperplane is constructed in n -dimensional space, and new weights are calculated to find the next solutions. To confirm the effectiveness of the proposed method, benchmarking problems that have different types of Pareto front are tested, and a topology optimization problem is performed as an engineering problem. A hypervolume indicator is used to quantitatively evaluate the proposed method, and it is confirmed that optimized solutions that are evenly distributed on the Pareto front can be obtained by using the proposed method.     

半挂式车辆自主驾驶的轨迹跟踪控制研究

该文针对半挂式车辆的自主驾驶,设计一种基于迭代线性二次型调节器(iLQR)的轨迹跟踪控制器.通过深入研究转向过程中半挂式车辆的运动,建立满足非完整性约束的非线性车辆运动学模型,并根据期望轨迹对其进行线性化,得到线性的轨迹跟踪误差模型.基于扩展卡尔曼滤波器设计车辆状态观测器,估计车辆的航向角.通过对期望轨迹进行基于三次B...     

Reduced-rank space-time adaptive processing using a modified projection approximation subspace tracking deflation approach

The projection approximation subspace tracking deflation (PASTd) algorithm is investigated in the context of the reduced-rank space-time adaptive processing (RR-STAP), where a recursive approach is employed to estimate the clutter subspace. Instead of a direct use of the classic PASTd algorithm in the RRSTAP, which suffers from a slow convergence rate because of the sequential tracking of multiple clutter eigenvectors, a modified PASTd adapted to the STAP is presented. In comparison with the conventional eigenvalue decomposition approach, the presented algorithm is computationally much more efficient and is also able to achieve comparable convergence effectiveness. The presented methodology is validated by the Monte Carlo simulation.     

无人机地面目标跟踪系统的建模与控制

对无人机地面目标跟踪系统进行了分析,并建立了各个子系统的数学模型,针对跟踪过程中可能导致目标丢失的两个关键问题--无人机的飞行轨迹和云台摄像机的控制,给出了一种轨迹规划与跟踪算法,该算法既可以使无人机与目标保持一定距离,又保证了无人机在飞行过程中机头始终朝向目标.此外,设计了一种云台摄像机控制方法,该方法利用无人机与目标的相对位姿和目标偏离图像中心的偏差作为反馈信息对云台摄像机进行控制.仿真结果表明本文所设计的无人机地面目标跟踪系统具有良好的性能.     

Design of exponential control charts using a sequential sampling scheme

Control charts for monitoring the time between events can be applied in various areas. In this study, we focus on the exponential control chart and consider the phase II problem (when process parameters are known) as well as the phase I problem (when process parameters are unknown). An exponential chart designed with the conventional approach has the disadvantage that the Average Run Length (ARL) value may increase when the process deviates from the nominal state. An ARL-unbiased design approach is therefore proposed for both phase II and phase I exponential charts. A sequential sampling scheme is adopted for the phase I exponential chart. The proposed ARL-unbiased design approach has several advantages over the conventional one, as it provides a self-starting feature and can significantly improve the ARL performance. Specific guidelines are suggested regarding the time to stop updating the estimates of parameters and control limits based on the actual false alarm rate. The phase I exponential chart can be calibrated to a constant in-control ARL value for each successive event accumulated to date. Simulated and real data examples are given to demonstrate the use and efficiency of the proposed design approach.     

基于误差的观测器在光电跟踪系统中的应用

对于光电跟踪系统来说,图像传感器例如电荷耦合器件(CCD)只能够探测脱靶量即偏差信息,而无法得到目标运动轨迹,所以,大多数情况下在目标跟踪回路不能直接实现前馈控制,这限制了系统的闭环跟踪性能。本文采用了一种基于误差观测器的等效前馈控制方法来提高运动平台光电跟踪系统的跟踪性能。该方法是在原有的反馈控制回路的基础上加入一个观测前馈通路,通过优化前馈滤波器提高闭环性能。由于是基于最终的视觉误差的观测,该方法对目标跟踪和扰动抑制同时起作用,既可以应用到地基跟踪也可以应用于运动平台上。前馈滤波器没有采用简单的一阶低通滤波器而是选择Q₃₁滤波器。仿真和实验表明,与传统控制方法相比,这种基于误差观测器的控制方法能够有效提高系统的低频跟踪性能。

     

基于模糊迭代Q-学习的冶金工业机器人轨迹跟踪控制研究

冶金工业机器人在现代工业生产中扮演着越来越不可替代的角色。由于工业自动化程度的大幅提升,人们对冶金工业机器人的性能也不断地提出新要求,尤其是对其控制系统的稳定性提出了更高的要求。针对目前冶金工业机器人轨迹跟踪精度较低且不具有自适应动态调节特性等问题,提出了一种模糊迭代Q-学习控制算法。以6-DOF（six degree-of-freedom,六自由度）双臂机器人为研究对象,利用Fuzzy工具箱编写模糊控制规则,以机器人产生的位置误差以及位置误差的变化率为模糊控制器的输入量,并引入Q-学习策略,以调整模糊控制器中的量化因子、比例因子以及迭代学习控制中的PD（proportional derivative,比例微分）参数,完成模糊迭代Q-学习控制器的设计。然后,联合ADAMS（automatic dynamic analysis of mechanical systems,机械系统动力学自动分析）和MATLAB软件搭建6-DOF双臂机器人仿真平台,开展高精度轨迹跟踪的轴孔装配任务模拟。仿真结果表明,6-DOF双臂机器人关节空间的轨迹跟踪精度较高,同时可以完成双臂轴孔协调装配任务,验证了所提出控制算法的有效性和先进性。研究结果可为双臂协作机器人实现高精度轨迹跟踪的轴孔装配提供参考,具有一定的实际应用价值。     

Trajectory tracking of piezoelectric positioning stages using a dynamic sliding-mode control

Trajectory tracking performance of a piezoelectric positioning stage almost depends on whether the tracking controller can effectively compensate the inherent hysteresis phenomenon. In this paper, a dynamic sliding-mode control (DSMC) with backstepping is proposed for the trajectory tracking of the piezoelectric positioning stage, which is suitable for a component of scanning microscopes. An equivalent model developed from a linear motion dynamics with addition of the hysteresis nonlinearity and strain-dependent function first is proposed to approximately represent the dynamics of motion of a one-dimensional piezoelectric positioning stage. Then, based on the equivalent model, the DSMC with an asymptotical sliding surface is proposed for the trajectory tracking control of the piezoelectric positioning stage. Moreover, the analysis of stability can be completed by mathematics, and the convergence rate of the tracking error can be governed by the choice of the control parameter values. Using the DSMC to trajectory tracking control, the piezoelectric positioning stage becomes more suitable for practical applications, especially with the need of various trajectories tracking in microscopy. To validate the proposed control scheme, a computer-based controller and a piezoelectric positioning stage with a capacitive displacement sensor are implemented. Experimental results illustrate the feasibility of the proposed controller for trajectory tracking applications.     

面向大型机场草坪的割草机器人路径规划及轨迹跟踪控制研究

为了提高割草机器人的工作效率及环境适应能力,基于移动机器人平台设计了一种既受遥控操作又能自主运行的适用于大型机场草坪作业的割草机器人。首先,运用高精度差分GPS（global positioning system,全球定位系统）采集机场草坪边界和障碍物的位置信息,根据采集的信息将机场草坪分为最少数目的凸多边形工作区域;考虑到割草机器人无法原地无半径转弯,在传统迂回式路径规划算法的基础上提出一种往返直线型路径规划算法,并在凸多边形路径规划区内推导出遍历路径的显示方程表达式。其次,运用高精度差分GPS测得割草机器人实际轨迹并与规划轨迹对比,设计了一种区间判断型轨迹纠偏算法;以执行电机的PID（proportion integration differentiation,比例积分微分）控制和区间判断型轨迹纠偏算法构造割草机器人双闭环轨迹跟踪控制器,对按传统迂回式路径和往返直线型路径行进的割草机器人进行轨迹跟踪仿真分析。最后,以自制的割草机器人为例,按往返直线型路径运行方式进行样机实验。仿真结果发现：当割草机器人跟踪当前路径到达终点后会自动调头跟踪下一条路径,验证了轨迹跟踪算法的稳定性;传统迂回式路径运行方式下割草机器人的漏割率较高,达到46.42%,而往返直线型路径运行方式下其漏割率为7.15%,明显优于传统迂回式路径仿真结果。样机实验测得的漏割率为8.89%,与仿真实验结果一致,表明所设计的轨迹跟踪算法对大型机场草坪作业割草机器人是适用的。研究结果可为大型机场草坪割草机器人的开发提供理论指导。