基于PLC的PSO算法PID输送机速度控制技术研究

本文主要利用粒子群优化算法(PSO)确定输送机系统速度控制中的最佳PID控制器增益。该项目通过三菱Q系列UDV PLC实现,该PLC带有多个编码器传感器和一个内置以太网模块来监控反馈。为了验证该方法的性能,将PSO离线确定的最优增益应用于模拟器和实际模型。实验结果表明,与其它方法相比,所提出的基于粒子群算法的PID控制...     

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。     

MIMO系统中改进的二进制粒子群天线选择算法

在多输入多输出系统中,发射端和接收端的多天线配置提高了信道容量和传输可靠性,而天线选择技术能在保持系统优点的同时有效地降低运算复杂度以及硬件成本。为了能在时变的信道条件下快速地选择出一组最优的天线子集,提出了一种基于二进制粒子群算法的改进的天线选择算法。推导出了二进制粒子群联合收发端天线选择的信道容量公式,并将其作为粒子群算法的适应度函数,使天线选择问题转换成二进制编码串的组合优化问题。通过改进模糊函数提高粒子群算法的收敛性,让二进制粒子群尽可能地收敛于全局最优位置。仿真结果表明,改进的算法能在降低运算复杂度的同时提高收敛性,且系统信道容量趋近于最优算法。     

PM2.5测量系统中改进神经网络控制算法优化补偿

针对现阶段PM2.5测量系统的测量精度较低的问题,提出了改进的BP神经网络PID控制算法对其进行优化补偿。通过对粒子群优化算法的速度公式进行了改进,采用优化的粒子群算法优化了BP神经网络,将其用于PID的在线参数调节,以PM2.5测量系统作为研究对象,将改进的BP神经网络PID控制算法与传统PID分别作了仿真研究。研究结果表明,基于改进的粒子群优化算法改进的BP神经网络PID控制算法与传统的PID控制相比,提高了测量精度,在一定程度上减少了误差。     

基于改进PID神经元网络的多变量系统控制算法

PID神经元网络具有动态特性,在系统控制应用中相比于传统的PID控制方法可取得更优的效果,但其学习算法为梯度学习算法,初始权值随机取得,为了提高其控制量逼近控制目标的速度和系统响应时间,引入粒子群算法对初始权值进行优化,最后应用Matlab软件对改进后的PID神经元网络算法进行仿真。仿真结果表明,该方法具有较好的控制性能。     

Low-Complexity Throughput-Based Antenna Selection Method

Antenna selection is a low-cost low-complexity attractive approach in MIMO systems that capture many advantages of these systems. In this paper, our objective is to select the best antennas that maximize throughput with truncated selective repeat automatic repeat request at data link layer in zero-forcing MIMO receivers. We propose a novel binary particle swarm optimization method with throughput as its fitness function for joint transmit and receive antenna selection. The results of simulations demonstrate that the proposed throughput based antenna selection method has better performance compared to capacity based methods, and PSO algorithm can significantly reduce computational complexity.     

Digital Modeling and PID Controller Design for MIMO Analog Systems with Multiple Delays in States, Inputs and Outputs

This paper presents a discrete-time state-space methodology for the digital modeling and design of an optimal digital proportional-integral-derivative (PID) plus state-feedback controller for multiple-input, multiple-output (MIMO) continuous-time systems with multiple time delays in states, inputs and outputs. To implement the digital design, first the Chebyshev quadrature formula together with a linear interpolation method is employed to obtain an extended discrete-time state-space model from the continuous-time multiple time-delay system. Then, a partially predetermined digital PID controller and the extended discrete-time state-space model are formulated as an augmented discrete-time state-space system utilizing state-feedforward and state-feedback linear-quadratic regulator (LQR) design. As a result, the parameters of the optimal PID controller and its associated state-feedback controller can be determined by tuning the weighting matrices in the LQR performance criteria. Further, an optimal discrete-time observer is jointly constructed for the multivariable system with multiple delays in states, inputs and outputs. The proposed design methodology can be applied to general MIMO continuous-time multiple time-delay systems for performance improvement and disturbance rejection. An illustrative example is given to demonstrate the effectiveness of the developed method. This work was supported in part by U.S. Army Research Office under Grant W911NF-06-1-0507, the National Science Foundation under Grant NSF0717860, and Research Contract 1440234.     

基于GA-PSO融合算法的二自由度PID参数优化

在工业过程控制中,PID参数调节直接影响工业生产的质量和效率。针对PID参数调节难这一问题,文中提出了一种将遗传算法和粒子群算法相结合的智能融合算法,并将该算法应用于二自由度PID参数的优化中。该算法在遗传算法的变异算子中引入粒子群算法,充分发挥两种单一智能算法的优点,并弥补了两者的缺点。算例仿真验证结果显示,该算法可以很好的应用于PID参数优化,且在调节PID参数的过程中具有优良的性能指标数值,在目标值跟踪特性和外扰动抑制特性上具有更好的控制效果。     

Joint Transmit and Receive Antenna Selection in Mimo Systems Based on Swarm Intelligence Algorithm

Multiple-input-multiple-output (MIMO) can provide superior performances such as system capacity, linkage, etc. But also it will bring high RF costs and system complexity, especially in large scale MIMO systems. Antenna selection (AS) is proved to be a trade-off between good performances and complexity. Specifically, from the perspective of both transmit and receive antennas, the joint transmit and receive antenna selection (JTRAS) is employed in MIMO systems. Up to now, some algorithms of JTRAS have been studied in MIMO systems. However, most of them are mainly focused on just one aspect about accuracy or complexity. Especially, compared to numerical analysis, the implementation of swarm intelligence algorithm in JTRAS needs to be studied extensively. In the paper, three intelligent algorithms, i.e. genetic algorithm, cat swarm algorithm and particle swarm algorithm are studied and compared in terms of accuracy, cost, and complexity. In addition, fractional coding is proposed in the algorithms instead of binary integer coding. The simulation results demonstrate that all three algorithms can efficiently accomplish the antenna selection. PSO has the best accuracy and stability, but the complexity of PSO is also highest. If we take overall performances in consideration, CSO is the best choice especially in practical implementation. Moreover, fractional coding will provide better performance than binary integer coding.

     

Symbol detection in spatial multiplexing system using particle swarm optimization meta-heuristics

Symbol detection in multi-input multi-output (MIMO) communication systems using different particle swarm optimization (PSO) algorithms is presented. This approach is particularly attractive as particle swarm intelligence is well suited for real-time applications, where low complexity and fast convergence is of absolute importance. While an optimal maximum likelihood (ML) detection using an exhaustive search method is prohibitively complex, PSO-assisted MIMO detection algorithms give near-optimal bit error rate (BER) performance with a significant reduction in ML complexity. The simulation results show that the proposed detectors give an acceptable BER performance and computational complexity trade-off in comparison with ML detection. These detection techniques show promising results for MIMO systems using high-order modulation schemes and more transmitting antennas where conventional ML detector becomes computationally non-practical to use. Hence, the proposed detectors are best suited for high-speed multi-antenna wireless communication systems. Copyright © 2008 John Wiley & Sons, Ltd.     

二进制猫群算法在大规模MIMO天线选择中的应用

在多输入多输出(MIMO)系统中,天线选择技术平衡了系统的性能和硬件开销,但大规模MI-MO系统收发端天线选择复杂度问题一直没有得到很好的解决.基于信道容量最大化的准则,采用两个二进制编码字符串分别表示发射端和接收端天线被选择的状态,提出将二进制猫群算法(BCSO)应用于多天线选择中,以MIMO系统信道容量公式作为猫群的适应度函数,将收发端天线选择问题转化为猫群的位置寻优过程.建立了基于BCSO的天线选择模型,给出了算法的实现步骤.仿真结果表明所提算法较之于基于矩阵简化的方法、粒子优化算法具有更好的收敛性和较低的计算复杂度,选择后的系统信道容量接近于最优算法,非常适用于联合收发端天线选择的大规模MIMO系统中.     

基于粒子群算法的PID控制器参数优化

粒子群算法是一种智能算法,在PID控制器参数整定的应用中可取得更优的效果。为解决传统的粒子群算法早熟收敛和收敛速度慢的缺点,文中采用了一种基于相似度动态调整惯性权重的方法,即越靠近目前最优粒子的个体被赋予越小的惯性权重值。最后用MATLAB对等温连续搅拌釜反应器仿真。与标准的PSO算法整定方法相比,改进的粒子群算法稳定时间为230.1 s,比传统粒子群算法524.7 s的稳定时间缩小了一半,表明改进的算法对PID控制器的参数优化有着较优的收敛效果。     

基于改进WFPSO算法的无刷直流电机模型分析

针对传统的无刷直流电机控制无法在线调整参数、难以精确控制的问题,提出一种基于改进的粒子群优化(PSO)算法的模糊PID控制器设计。通过对粒子群优化算法的参数进行分时段更新,实现模糊PID控制器参数动态全局优化,来确定使用双闭环控制模型的无刷直流电机的最优参数。Matlab仿真结果表明,该研究方法较传统方法可使得转速无超调、减少调节时间,同时启动时转矩脉动较小。     

Enhancing Design of Visual-Servo Delayed System

A robust adaptive predictor is proposed to solve the time-varying and delay control problem of an overhead crane system with a stereo-vision servo. The predictor is based on the use of a recurrent neural network (RNN) with tapped delays, and is used to supply the real-time signal of the swing angle. There are two types of discrete-time controllers under investigation, i.e., the proportional-integral-derivative (PID) controller and the sliding controller. Firstly, a design principle of the neural predictor is developed to guarantee the convergence of its swing angle estimation. Then, an improved version of the particle swarm optimization algorithm, the parallel particle swarm optimization (PPSO) method is used to optimize the control parameters of these two types of controllers. Finally, a homemade overhead crane system equipped with the Kinect sensor for the visual servo is used to verify the proposed scheme. Experimental results demonstrate the effectiveness of the approach, which also show the parameter convergence in the predictor.     

基于粒子群算法的预编码设计

针对多用户多输入多输出系统的下行链路传输,提出了一种基于粒子群算法的预编码设计方案。该方案首先通过理论分析,推导出错误符号概率的函数,该函数是将平均符号错误概率构造为预编码矩阵和各用户信道信息的函数,并以最小符号错误概率为判断准则。然后利用模拟鸟群觅食的粒子群方法对其进行优化搜索,得到最佳效果。理论分析与仿真结果表明,该方案性能比传统的预编码性能更优。     

Implementation of Synchronized Chaotic Lü Systems and Its Application in Secure Communication Using PSO-Based PI Controller

This paper investigates the synchronization of chaotic systems and its application in secure communication. First, a particle swarm optimization (PSO)-based proportional -integral (PI) controller is proposed for synchronization of general chaotic systems. By using the PSO algorithm, optimal control gains in PI controller are derived such that a performance index of integrated squared error (ISE) is as minimal as possible and synchronization can be achieved. Then a chaotic secure communication system based on synchronized coupled Lü systems is implemented using basic electronic components. Finally, both simulation results and the experimental results demonstrate the proposed PSO-based PI scheme’s success in the secure communication application.     

基于克隆粒子群算法的图像分割方法

最大熵阈值法是目前图像分割中应用最广泛的方法之一。为了快速准确地自动确定图像分割阈值,把克隆选择算法和粒子群算法相结合,提出克隆粒子群优化算法。利用这种改进方法对最大熵图像分割函数进行全局寻优。克隆选择算法和粒子群算法的结合克服了各自的缺点,克隆选择的多样性补偿了粒子群的多样性差的缺点,粒子群的快速性补偿了克隆选择的收敛速度慢的缺点。克隆粒子群方法克服了传统遗传算法易出现早熟、陷入局部最优等的问题,加快了图像分割函数收敛速度,最后能够快速准确地得到图像分割的最佳阈值。实验表明,改进后的算法分割速度较快,易于收敛到最优解,并且得到的分割阈值更加稳定。     

有限反馈预编码优化中基于性能准则的码字选择

多输入多输出(MIMO)空分复用系统中,采用有限比特反馈方式设计最优预编码矩阵,使用较少的反馈开销,可以有效地提高系统性能。对于有限反馈预编码设计方案,提出了一种基于Qos准则的码字选择方法,以最优化系统MSE性能和BER性能为目标选择最佳码字。仿真结果表明,与已有的码字选择方式相比,本文提出的方法更加直接,对于各种码本具有通用性,而且进一步改善了有限比特反馈预编码的性能,系统BER值降低了1.5 dB。     

Investigation on the evolutionary algorithms with their applications in MIMO detecting systems

In this paper, with the purpose of integrating the advantages of both the genetic algorithm and the particle swarm optimization, a new genetic particle swarm optimization (GPSO) algorithm is proposed. Furthermore, these three evolutionary algorithms are successfully applied to address the MIMO detection problem. Simulation results reveal that the GPSO‐based detection algorithm takes much less population size and iteration number when compared with the particle swarm optimization‐based detection method and the genetic algorithm‐based detection method. Besides, when compared with the optimal maximum likelihood detection method, the GPSO‐based detection algorithm can strike a much better balance between the BER performance and the computational complexity. Copyright © 2012 John Wiley & Sons, Ltd.     

A hybrid fuzzy logic and neural network algorithm for robot motioncontrol

Robotic manipulators are multivariable nonlinear coupling dynamic systems. Industrial robots were controlled by using a traditional controller, the control performance of which may change with respect to operating conditions. Since the robotic manipulators have complicated nonlinear mathematical models, control systems based on the system model are difficult to design. In this paper, a model-free hybrid fuzzy logic and neural network algorithm was proposed to control this multi-input/multi-output (MIMO) robotic system. First, a fuzzy logic controller was designed to control individual joints of this 4-degree-of-freedom (DOF) robot. Secondly, a coupling neural network controller was introduced to take care of the coupling effect among joints and refine the control performance of this robotic system. The experimental results showed that the application of this control strategy effectively improved the trajectory tracking precision