Robust loop shaping-fuzzy gain scheduling control of a servo-pneumatic system using particle swarm optimization approach

In this paper, a new technique called robust loop shaping-fuzzy gain scheduled control (RLS-FGS) is proposed to design an effective nonlinear controller for a long stroke pneumatic servo system. In our technique, a nonlinear dynamic model of a long stroke pneumatic servo plant is identified by the fuzzy identification method and is used as the plant for our design. The structure of local controllers is selected as PID control which is proven by many research works that this type of control has many advantages such as simple structure, well understanding, and high performance. The proposed technique uses particle swarm optimization (PSO) to find the optimal local controllers which maximize the average stability margin. In addition, performance weighting function which is normally difficult to obtain is automatically determined by PSO. By the proposed technique, the RLS-FGS controller can be designed, and the structure of local controllers is still not complicated. As seen in the simulation and experimental results, our proposed technique is better than the classical gain scheduled PID controller tuned by pole placement and the conventional fuzzy PID controller tuned by ISE method in terms of robust performance.     

Adaptive coupling control for overhead crane systems

Due to the requirements of high positioning accuracy, small swing angle, short transportation time, and high safety, both motion and stabilization control for an overhead crane system becomes an interesting issue in the field of control technology development. Since the overhead crane system is subject to underactuation with respect to the load sway dynamics, it is very hard to manipulate the crane system in a desired manner, namely, gantry position tracking and sway angle stabilization. Hence, in this paper, a nonlinear control scheme incorporating parameter adaptive mechanism is devised to ensure the overall closed-loop system stability. By applying the designed controller, the position error will be driven to zero while the sway angle is rapidly damped to achieve swing stabilization. Stability proof of the overall system is given in terms of Lyapunov concept. To demonstrate the effectiveness of the proposed controller, results for both computer simulation and experiments are also shown.     

Nonlinear coupling control laws for an underactuated overhead crane system

In this paper, we consider the regulation control problem for an underactuated overhead crane system. Motivated by recent passivity-based controllers for underactuated systems, we design several controllers that asymptotically regulate the planar gantry position and the payload angle. Specifically, utilizing LaSalle's invariant set theorem, we first illustrate how a simple proportional-derivative (PD) controller can be utilized to asymptotically regulate the overhead crane system. Motivated by the desire to achieve improved transient performance, we then present two nonlinear controllers that increase the coupling between the planar gantry position and the payload angle. Experimental results are provided to illustrate the improved performance of the nonlinear controllers over the simple PD controller.     

基于量子粒子群算法的PID参数自整定方法

PID控制在工业生产中应用非常广泛.以直流电机模型为被控对象,提出了基于量子粒子群算法的PID参数自动整定方法.应用经典的Ziegler-Nichols方法整定PID参数,被控对象性超调大往往难以满足要求.粒子群算法是通过模拟鸟群觅食过程中的迁徙和群聚行为而提出的一种基于群体智能的全局随机搜索算法.将量子粒子群算法用于优化PID参数,并与Z-N法整定的PID控制器性能进行对比.仿真结果发现,与Z-N法相比,基于粒子群算法优化的PID控制器,系统超调明显减小.除QPSO-PID(ITSE)对应的系统具有较长调节时间外,虽然应用不同优化目标优化后的PID参数不同,控制对象的响应性能却非常相似.     

基于RBF神经网络的永磁同步伺服电机控制系统

针对永磁同步电机控制系统,建立其磁场定向控制数学模型。运用增量式数字PID的方法实现对PMSM的传统PID控制策略。在此基础上,借助RBF神经网络的学习能力,进行PID控制器参数的自适应整定,进一步改善PID控制器的性能。同时,为提高RBF网络性能,采用粒子群算法对网络进行优化。仿真表明,与传统PID控制比较,基于RBF的PID控制系统能提高PID控制器的性能,改善了PMSM控制系统的收敛速度和跟踪精度。     

An energy exchanging and dropping-based model-free output feedback crane control method

For the overhead crane control problem, velocity-related terms (corresponding to full-state feedback) are generally required in the designed control systems for damping injection to achieve (asymptotic) stability. However, it is known that velocity signals may be noisy or even unmeasurable. Also, most existing controllers require full or partial plant physical parameters like rope length or load mass. To resolve these issues, a model-free energy exchanging and dropping-based control law is proposed to achieve output (only position/swing-angle) feedback control for overhead cranes. We synthesize a total energy function, consisting of the (generalized) crane energy and the controller energy, to render it to achieve its (local) minimum at the desired equilibrium point. The proposed control law is dynamically generated by an artificial block-spring system, which exchanges energy with the crane dynamics and then drops the energy via an elegant dropping mechanism to gradually attenuate the total energy. The corresponding stability and convergence analysis is implemented using some Lyapunov-like analysis. Simulation and experimental results suggest the effectiveness and feasibility of the proposed method for crane control, in terms of rapid swing suppression, efficient trolley positioning, as well as increased robustness.     

粒子群优化BP-PID的矿井提升机调速系统

传统PID控制器在矿井提升机变频调速系统应用中,由于控制参数固定且不易整定,导致电机转速超调大、电磁转矩和转子磁链脉动大,进而出现矿井提升机调速系统控制效果差的问题。针对这一问题,文中提出一种改进粒子群优化BP神经网络PID控制器的算法。由于BP神经网络算法存在收敛速度慢和极易陷入局部最优的缺点,现将粒子群算法收敛速度快和全局最优特性与神经网络结合,并通过设计神经网络收敛系数进一步加快收敛速度。仿真结果表明,粒子群优化的神经网络控制效果比神经网络好,且效果明显优于传统PID控制器;相较于神经网络PID控制器,矿井提升机转速调节系统稳速调节速度明显提高;与传统PID控制器相比,电机电磁转矩和转子磁链脉动明显降低,具有较强的稳定性和鲁棒性。     

Controller Placement in Software-Defined Multihop Wireless Networks: Optimal Solution and GA-based Approximation

In a multi-controller software-defined networking (SDN) architecture, solving the controller placement problem (CPP) has a direct effect on the generated control overhead in the network. We aim to minimize the control overhead exchanged in the network, especially in software-defined multihop wireless networks (SDMWN), i.e., a network that is built on multihop communications using a wireless medium. We solve this problem both optimally, using a nonlinear optimization model, and via a heuristic algorithm. The proposed heuristic approach is based on the genetic algorithm (GA). The objective of both the proposed optimization problem and the proposed GA algorithm is to find a given number of controllers, controller placements and assignments of controllers to devices while minimizing the generated control overhead in the network. Our results show the impact of different metrics, including the number of controllers, the arrival rate of new flows and the capacity limit of wireless links on the control overhead and the average number of controller-device and inter-controller hops. In addition, our results demonstrate that the GA-based heuristic approach can derive the same optimal solution for a small network with much less computational overhead, and can solve larger networks in a short period of time, making it feasible for non-trivial network sizes.

     

永磁同步电机伺服系统控制最优化方法

永磁同步电机具有强耦合和非线性的特点,其应用环境通常较为复杂且存在干扰,系统稳定性差。传统PI控制方式很难满足控制系统的要求,控制效果不佳。为抑制超调幅度,提高控制精度,提出一种新型粒子群算法,把系统控制精度作为粒子的寻优目标,最优粒子作为最终PI参数,从而对电机进行精确稳定的控制。仿真实验结果表明,粒子群算法的PI控制器使系统具备较好的动静态性能,该研究为伺服控制系统优化设计提供了较强的理论支撑,且应用前景广阔。     

基才PSO—BP神经网络的PID控制器参数优化方法

针对传统PID控制系统参数整定过程存在的在线整定困难和控制品质不理想等问题,结合BP神经网络自学习和自适应能力强等特点,提出采用BP神经网络优化PID控制器参数。其次,为了加快BP神经网络学习收敛速度,防止其陷入局部极小点,提出采用粒子群优化算法来优化BP神经网络的连接权值矩阵。最后,给出了PSO—BP算法整定优化PID控制器参数的详细步骤和流程图。并通过一个PID控制系统的仿真实例来验证本文所提算法的有效性。仿真结果证明了本文所提方法在控制品质方面优于其它三种常规整定方法。     

Adaptive Fuzzy Controller of the Overhead Cranes With Nonlinear Disturbance

Overhead cranes are common industrial structures that are used in many factories and harbors. They are usually operated manually or by some conventional control methods, such as the optimal and PLC-based methods. The theme of this paper is to provide an effective all-purpose adaptive fuzzy controller for the crane. This proposed method does not need the complex dynamic model of the crane system, but it uses trolley position and swing angle information instead to design the fuzzy controller. An adaptive algorithm is provided to tune the free parameters in the crane control system. The ways to speed the transportation and reduce the computational efforts are also given. Therefore, the designing procedure of the proposed controller will be very easy. External disturbance, such as the wind and the hit, which always deteriorates the control performance, is also discussed in this paper to verify the robustness of the proposed adaptive fuzzy algorithm. At last, several experimental results with different wire length and payload weight compare the feasibility and effectiveness of the proposed scheme with conventional methods     

基于神经网络的CMP过程智能R2R预测控制

针对化学机械研磨(CMP)过程非线性、时变和不易在线测量的特性,提出了基于径向基函数(RBF)神经网络和微粒群(PSO)算法的CMP过程run-to-run(R2R)预测控制器NNPR2R。首先通过样本数据用减聚类算法和最小二乘法构建CMP过程的RBF神经网络预测模型,解决了复杂CMP过程难以建立精确数学模型的难题和提高了预测模型的精度。然后通过PSO算法滚动优化求取控制律,解决了基于导数的优化技术易于陷入局部最优的问题并提高了控制精度。仿真结果表明,CMP过程NNPR2R控制器的性能优于常规的EWMA方法,有效抑制了过程漂移和减小了不同批次间产品的差异,显著降低了材料去除率(MRR)的均方根误差。     

Anti-swing control of a hydraulic loader crane with a hanging load

In this paper, anti-swing control for a hydraulic loader crane is presented. The difference between hydraulic and electric cranes are discussed to show the challenges associated with hydraulic actuation. The hanging load dynamics and relevant kinematics of the crane are derived to create the 2-DOF anti-swing controller. The anti-swing controller is added to the electro-hydraulic motion controller via feedforward. A dynamic simulation model of the crane is made, and the control system is evaluated in simulations with a path controller in actuator space. Simulation results show significant reduction in the load swing angle during motion. Experiments are carried out to verify the performance of the anti-swing controller, showing good suppression of the payload angle in practice.     

基于等级域的多服务Agent协作求解问题研究

在合同网当中引入等级域的概念,建立相应的基于等级域的多服务Agent模型GF-CNM,并采用随机TOP-N算法对等级域中不同等级的各个服务Agent的等级跃迁进行了算法描述和分析。该模型能减少任务协作时引起的网络通信量,避免对不相关服务Agent求解的时间开销,并均衡协作任务的分布,在一定程度上避免了“忙者越忙,闲者越闲”的“马太效应”,有效地缓解了资源受限条件下的任务协作求解问题。     

基于复合控制策略的电动负载模拟器研究

针对某雷达随动系统电动负载模拟器自身复杂的非线性以及多余力矩对系统加载性能的影响,提出了一种基于改进的小波神经网络和灰预测的控制策略。该策略主要由变结构的小波神经网络控制器（VSWNNC）和灰预测补偿器（GPC）构成,前者利用自学习算法动态改变隐含神经元数目,加快了系统的收敛速度,降低了系统的计算复杂度,提高了系统的动静态响应性能;后者利用灰理论来预测输入力矩偏差,进一步提高了系统的稳定性和准确性。半实物台架仿真试验结果表明：该复合控制策略具有较强的鲁棒性和较高的控制精度,保证了系统动态加载时的稳定性和抗干扰能力。     

Model-Based Adaptive Predictive Control with Visual Servo of a Rotary Crane System

This paper investigated the implementation of an adaptive predictive controller using nonlinear dynamic echo state neural (ESN) model for a rotary crane system by the visual servo method. The control sequences within the control horizon were described using cubic spline interpolation to enlarge the predictive horizon. Verification of the proposed scheme in the face of exogenous disturbances and modeling error with inaccurate string length was demonstrated by both simulations and experiments.     

基于AVR单片机的旋转倒立摆设计

对简易旋转倒立摆的各个模块做了相关介绍,对系统的实现方案和设计步骤进行描述。采用ATmega16单片机作为主控元件,以BTS7960模块作为驱动器,使用直流伺服电机作为驱动电机。采用增量式PID算法控制摆杆达到稳定,对摆杆所处位置的角度进行分段,然后采用分段PID算法进行控制,以使摆杆快速趋于稳定状态,得到良好的控制效果。     

Parameter identification and position control for helical hydraulic rotary actuators based on particle swarm optimization

It is difficult to obtain an accurate mathematical model in electro-hydraulic servo control system, due to the nonlinear factors such as dead zone, saturation, flow coefficient, and friction. Hence, a parameter identification algorithm, combining recursive least squares (RLS) with modified nonlinear particle swarm optimization (NPSO) algorithm, is proposed. On this basis, another improved NPSO algorithm is also put forward, aiming at searching for the optimal proportional–integral (PI) controller gain of the nonlinear hydraulic system while giving comprehensive consideration to the system performance indexes. The system identification experiments and position tracking control are conducted, respectively. As indicated by the comparison with the least squares (LS), RLS, PSO, and RLS–LPSO results, the proposed method shows higher identification and control accuracy.     

二维耦合光学摆镜伺服控制系统

二维耦合光学摆镜是扫描式星载红外光学系统的关键运动部件,其运动特性对伺服系统提出了高精度位置控制与运动解耦的特殊要求。在建模与仿真分析的基础上,提出了一种新的耦合偏移补偿与分割步进的解耦策略,采用位置环与速度环双闭环的PID控制算法,使用有限转角力矩电机和高精度旋转变压器作为执行与测量元件,以DSP为核心构建了二维耦合光学摆镜伺服控制系统。实验结果表明:该方法设计的控制系统二维运动解耦正确,控制精度高,响应时间短,动态特性好且超调小,可广泛应用于高精度摆动扫描控制系统的研究领域,具有很好的工程应用前景。     

Neural network-based control of balanced robotic manipulators with joint flexibility

This paper addresses an improvement on the controlled performance of balanced manipulators in a practical level by implementing neural network based controller. The mass balancing of robotic manipulators has been shown to have favorable effects on the dynamic characteristics. However, it was also pointed out that for the manipulators having a certain degree of flexibility at the joints, due to the lowered structural natural frequencies and reduced velocity related terms, mass balancing results in vibratory motion at high speed operation. Such a vibratory tendency of the balanced flexible joint manipulator limits the admissible range of servo gains of the conventional controllers, making those controllers unsuitable for controlling the manipulator at high speeds.

To avoid such difficulty, an artificial neural network (NN) controller is introduced to realize the dynamic control of the balanced flexible joint manipulators. A feedforward type of NN controller is proposed and its performance is evaluated through a series of numerical simulations. The proposed NN controller showed much better tracking performances over the conventional PD controller.