基于遗传算法的双旋翼系统的PID控制器设计

双旋翼多输入多输出系统是一套具有复杂非线性和强交叉耦合性的控制系统实验装置,难以进行控制参数的整定.而遗传算法对问题的依赖性小,适合复杂非线性系统的参数寻优.文中利用遗传算法,借助Matlab语言和Simulink仿真模型,实现了双旋翼多输入多输出系统PID控制器的设计.仿真结果表明,该控制器是有效的.     

基于双阶段网络模型的改进多变量广义预测控制

针对多变量非线性时滞系统存在多变量间复杂的耦合情况,多输入多输出系统转化为多个多输入单输出系统,并构建多变量双阶段神经网络时滞预测模型;在考虑耦合关系的基础上,将改进比例性能指标型广义预测控制器引入到多变量系统中;该控制器含有预测控制增量表征系统未来变化趋势,将其作为当前控制量的补偿,优化控制性能;通过300 MW单元机W型火焰直吹式燃煤锅炉系统的仿真研究验证了控制方案的有效性。     

一种基于多目标遗传算法的非线性控制器

该文利用相位滞后仅38.1°的Clegg积分器构成一个非线性比例积分器,并利用多目标遗传算法对该控制器的三个参数进行优化,其中被优化的两个目标分别为被控系统的给定性能指标和抗负载扰动能力。然后将该控制器应用于一个存在饱和特性和间隙特性的双闭环调速系统,研究并比较了该系统在阶跃给定输入下的性能指标以及抗负载扰动的能力。仿真试验表明将该非线性控制器应用于具有多个非线性特性的动态系统能取得优良的性能。     

基于微分平滑的四旋翼运输系统轨迹跟踪控制

四旋翼系绳运输系统是一种具有8个自由度和4个控制输入的欠驱动、强耦合、多变量的非线性系统.为建立系统模型,将系统解耦为双质点系绳连接子系统和四旋翼姿态控制子系统,基于广义拉格朗日方程推导出系统的动力学模型.然后利用微分平滑理论证明了系统是以载荷位置和四旋翼偏航角为平滑输出的微分平滑系统.进而在等式限制条件下对平滑输出轨迹进行规划,获得前馈开环控制律.为抑制系统扰动并使跟踪误差收敛于原点,利用微分平滑特性推导出动态内反馈控制器.仿真实验验证了所提方法的有效性.     

大容量链式静止同步补偿器的非线性双输出解耦控制

大容量链式静止同步补偿器(STATCOM)是典型非线性系统, 其控制理论和算法复杂且抽象. 本文根据链式STATCOM的有功–无功电流的动态数学模型, 得到非线性动态无功控制策略, 利用双输入双输出线性化的控制方法, 实现双输出变量解耦控制. 仿真及实验研究表明, 该链式STATCOM非线性双输出解耦控制器, 在系统发生短路或大功率负载突变时具有良好的动态性能.     

四旋翼无人机鲁棒自适应姿态控制

 四旋翼无人机的姿态控制是自主飞行控制的核心,针对四旋翼姿态易受外界环境干扰和内部参数摄动等不确定性影响的问题,设计了一种鲁棒自适应反步控制器,以提高四旋翼的鲁棒性。建立了四旋翼完整的姿态运动模型,并将其转化为含有广义不确定性的多输入多输出非线性系统。根据该系统满足严格反馈的结构特点,设计了反步控制器; 针对系统中存在的外部干扰和内部参数摄动等不确定性,引入了一类鲁棒自适应函数来抵消该不确定性对系统的影响; 采用非线性跟踪微分器估计虚拟控制量的微分信号,减小了反步控制器设计中普遍存在的“计算膨胀”问题; 通过构造Lyapunov 函数证明闭环系统是稳定且指数收敛的。仿真结果表明,所设计控制器具有良好的控制效果和鲁棒性。     

多输入多输出系统的一种鲁棒PI控制器设计

本文在线性系统的基础上针对多输入多输出系统提出了一种鲁棒ＰＩ控制器设计方案，该控制器将滑模变结构控制和ＰＩ调节器的优点有机地结合起来。本文给出的控制方案对多输入事输出线性系统和特殊形式的非线性系统是适合的。理论分析和仿真结果表明该方案能使复杂的受控对象具有良好的性能鲁棒性和稳定鲁棒性，能消除各种持续干扰对闭环系统平衡点的影响和消除抖动现象。控制器设计简单，适合于高性能的复杂控制系统设计。     

基于精确线性化的MIMO双线性系统预测函数控制

针对典型多输入多输出双线性系统, 提出了基于非线性过程精确反馈解耦线性化的预测函数控制方法这是一种分层的控制策略, 首先设计一个静态的非线性状态反馈, 使得闭环系统是输入输出解耦和线性的;然后设计一组单输入单输出预测函数控制器, 下层为上层预测函数控制提供一组单输入单输出模型, 而上层预测函数控制以其固有的鲁棒性来补偿参数变化和解耦线性化的近似性, 并以纸机加压网前箱为例进行了仿真实验, 结果是令人满意的.     

一种非线性多环递归自适应跟踪控制

针对控制输入有界的变参数广义高阶非线性系统跟踪控制问题,给出一种多环递归跟踪的鲁棒控制方法.通过分层引入虚拟跟踪器,将高阶系统分解为多个独立子系统;内环虚拟跟踪器使内环输出指数收敛于外环虚拟输入,最内环设计自适应控制器补偿参数摄动和外部干扰,并保证输出指数收敛于外环虚拟输入;多环递归跟踪实现系统输出精确跟踪期望输入,理论证明闭环系统的全局渐近收敛性.数值仿真验证了多环跟踪控制器的可行性和合理性.     

MIMO系统非线性模糊免疫PID的设计与优化

针对工业中常规控制器难以达到较好控制效果的控制难点,提出了免疫PID控制器,利用模糊控制可以无限逼近非线性的特性,构成非线性模糊免疫PID控制器,并基于正交函数将M1MO(多输入多输出)控制系统的非线性模糊免疫PID控制器的设计过程转化为由一个只含有代数方程的算法来实现,最后用具有自适应功能的遗传算法对其进行优化.仿真结果表明,所设计的控制器在控制性能上优于常规PID,并能快速得到最优参数.另方面对于多输入多输出控制系统,控制器的设计和优化显得更为简便和有效.     

基于合作粒子群算法的PID神经网络非线性控制系统

PID神经元网络 (PIDNN)模型为一种新型的神经网络模型,兼有PID与神经网络的共同优点,应用于复杂的控制系统.取得优良控制性能,但其后向传播算法 (BP)限制了该模型的应用范围.为实现对非线性多变量系统的有效控制,扩展神经网络的应有范围,本文采用PIDNN神经网络设计了多变量PIDNN神经网络 (MPIDNN)控制器,并用本文作者提出的合作粒子群算法 (CPSO)取代了传统BP后向传播算法,通过比较MPIDNN_CPSO、MPIDNNCRPSO、MPIDNN_PSO和MPIDNN_BP4种控制器的控制性能,仿真结果表明,基于CPSO算法的MPIDNN控制器实现了对非线性多变量不对称系统的有效控制.与传统的BP算法相比,CPSO算法提高了控制系统的稳定性、精确性与鲁棒性.     

Robust adaptive fuzzy control of twin rotor MIMO system

This paper considers designing an adaptive fuzzy controller to position the yaw and pitch angles of a twin rotor MIMO system (TRMS) in two degrees of freedom. The goal of the controller is to stabilize the TRMS in a desired position or track a specified trajectory. The parameters of the fuzzy controller are updated using the gradient descent algorithm in order to increase its robustness against external disturbances and/or changes in system parameters. Moreover, the stability of the overall closed-loop system is guaranteed based on the Lyapunov stability theory. The proposed controller is applied to a TRMS with heavy cross coupling between its axes. Experimental results show good performance of the proposed controller as compared to the non-adaptive fuzzy and PID controllers, especially when there are system uncertainties and external disturbances.     

基于增强蚁群优化的海量规模MIMO系统快速检测算法

大规模MIMO系统的符号向量检测算法计算复杂度较高,对此结合粒子群优化与蚁群优化提出一种低计算复杂度的海量规模MIMO系统快速检测算法。首先,推导出一种新的概率搜索模型,将基于距离的蚁群搜索与基于速度的粒子搜索结合;然后,将ACO距离指标与PSO的方向、速度指标结合生成一种新的概率指标,将ACO的信息素更新步骤变为PSO速度的更新;最终,将MIMO检测问题建模为路径寻找问题,寻找MIMO符号检测问题的次优解。对比仿真实验结果表明,本算法的检测性能优于部分传统算法以及其他新颖的MIMO检测算法,在获得与最大似然估计检测法接近的误码率性能下,具有极快的计算速度,适用于海量规模的MIMO系统。     

基于系数图法的多输入多输出系统控制器设计方法

多输入多输出系统的交互作用会限制适用于单输入单输出系统的多种先进设计方法的使用,且多数关于多输入多输出系统控制研究只重视消除交互作用的影响,忽略系统控制问题,这样不但弱化系统的鲁棒性,且设计过程复杂,解耦后系统阶次也较高.鉴于此,提出一种基于系数图法的多入多出系统的控制器设计方法,将多输入多输出系统解耦问题转化为参数优化问题.首先,给出目标函数和两个线性约束条件,通过在频域基于粒子群算法优化目标函数,从而设计补偿器实现解耦;其次,基于系数图法确定控制器结构及参数整定,兼顾系统的稳定性和鲁棒性;最后,通过仿真实验验证所提出方法的有效性.     

Adaptive neural control of uncertain MIMO nonlinear systems

In this paper, adaptive neural control schemes are proposed for two classes of uncertain multi-input/multi-output (MIMO) nonlinear systems in block-triangular forms. The MIMO systems consist of interconnected subsystems, with couplings in the forms of unknown nonlinearities and/or parametric uncertainties in the input matrices, as well as in the system interconnections without any bounding restrictions. Using the block-triangular structure properties, the stability analyses of the closed-loop MIMO systems are shown in a nested iterative manner for all the states. By exploiting the special properties of the affine terms of the two classes of MIMO systems, the developed neural control schemes avoid the controller singularity problem completely without using projection algorithms. Semiglobal uniform ultimate boundedness (SGUUB) of all the signals in the closed-loop of MIMO nonlinear systems is achieved. The outputs of the systems are proven to converge to a small neighborhood of the desired trajectories. The control performance of the closed-loop system is guaranteed by suitably choosing the design parameters. The proposed schemes offer systematic design procedures for the control of the two classes of uncertain MIMO nonlinear systems. Simulation results are presented to show the effectiveness of the approach.     

Type-1 and Type-2 effective Takagi-Sugeno fuzzy models for decentralized control of multi-input-multi-output processes

Effective model is a novel tool for decentralized controller design to handle the interconnected interactions in a multi-input-multi-output (MIMO) process. In this paper, Type-1 and Type-2 effective Takagi-Sugeno fuzzy models (ETSM) are investigated. By means of the loop pairing criterion, simple calculations are given to build Type-1/Type-2 ETSMs which are used to describe a group of non-interacting equivalent single-input-single-output (SISO) systems to represent an MIMO process, consequently the decentralized controller design can be converted to multiple independent single-loop controller designs, and enjoy the well-developed linear control algorithms. The main contributions of this paper are: i) Compared to the existing T-S fuzzy model based decentralized control methods using extra terms to characterize interactions, ETSM is a simple feasible alternative; ii) Compared to the existing effective model methods using linear transfer functions, ETSM can be carried out without requiring exact mathematical process functions, and lays a basis to develop robust controllers since fuzzy system is powerful to handle uncertainties; iii) Type-1 and Type-2 ETSMs are presented under a unified framework to provide objective comparisons. A nonlinear MIMO process is used to demonstrate the ETSMs’ superiority over the effective transfer function (ETF) counterparts as well as the evident advantage of Type-2 ETSMs in terms of robustness. A multi-evaporator refrigeration system is employed to validate the practicability of the proposed methods.     

A Wavelet Neural Network Based Non-linear Model Predictive Controller for a Multi-variable Coupled Tank System

In this paper, a novel real time non-linear model predictive controller(NMPC) for a multi-variable coupled tank system(CTS) is designed. CTSs are highly non-linear and can be found in many industrial process applications. The involvement of multi-input multi-output(MIMO) system makes the design of an effective controller a challenging task. MIMO systems have inherent couplings,interactions in-between the process input-output variables and generally have an complex internal structure. The aim of this paper is to design, simulate, and implement a novel real time constrained NMPC for a multi-variable CTS with the aid of intelligent system techniques. There are two major formidable challenges hindering the success of the implementation of a NMPC strategy in the MIMO case. The first is the difficulty of obtaining a good non-linear model by training a non-convex complex network to avoid being trapped in a local minimum solution. The second is the online real time optimisation(RTO) of the manipulated variable at every sampling time.A novel wavelet neural network(WNN) with high predicting precision and time-frequency localisation characteristic was selected for an MIMO model and a fast stochastic wavelet gradient algorithm was used for initial training of the network. Furthermore, a genetic algorithm was used to obtain the optimised parameters of the WNN as well as the RTO during the NMPC strategy. The proposed strategy performed well in both simulation and real time on an MIMO CTS. The results indicated that WNN provided better trajectory regulation with less mean-squared-error and average control energy compared to an artificial neural network. It is also shown that the WNN is more robust during abnormal operating conditions.     

宽带MIMO信道下的并行干扰抵消接收机

基于数据块传输模型,将窄带MIMO并行干扰抵消接收机推广到宽带MIMO信道,在理想信道和信道估计下,通过仿真评估了所提出的宽带MIMO并行干扰抵消接收机的性能。仿真结果表明：所建议的方案在宽带MIMO信道下的性能好于窄带MIMO信道下的性能,在宽带MIMO信道下,接收机可获得比窄带下更大的分集增益。