逆变式等离子切割电源控制策略

以逆变式等离子切割电源为研究对象,对其强非线性、时变、多变量耦合的特点及工艺要求,提出基于PID-NN算法的控制策略,从非线性受控对象建模、多工艺参数解耦、控制器加速及精度提高等方面入手,对控制器进行性能优化.通过仿真及切割实验,与传统PID控制器和单一的BP神经网络控制器进行比较,验证了该方案使系统在鲁棒性、响应速度等方面有较大的改善,切割质量得到了明显提高,证明了该控制器在处理离散事件及多变量的解耦问题的优势.     

对角CARIMA模型多变量广义预测控制器系数直接算法

为了简化多变量广义预测控制MGPC 的设计与实现,提出了对角CARIMA (Controlled autoregressive integrated moving average) 模型MGPC 控制器系数的直接求解方法. 利用多变量对角CARIMA 模型直接递推得到了非常简洁的 MGPC 控制器，控制增量等于控制器系数与设定值、过程输入输出历史数据、模型预测误差历史数据的乘积，控制器系数只与模型参数和设计参数有关，控制器系数维数只由模型结构参数决定. 避免了Diophantine 方程的求解，减少了在线计算量，简化了MGPC 控制器的实现. 在一个DCS 控制的非线性液位装置上的对比实验结果表明了该方法的有效性.     

多变量多时滞系统的前馈补偿解耦及IMC-PID控制

多变量系统控制器设计中遇到的主要难题是多时滞和强铰链耦合问题;对于非奇异方阵系统,根据解耦理论通过串级前馈时滞补偿器将原系统解耦为多个单变量小时滞系统,运用模型降阶技术,将解耦后的复杂单变量小时滞系统逼近为FOPDT(一阶环节+延时)形式,采用IMC控制策略实现多个单变量系统单位反馈控制,运用了麦克劳林级数展开式,通过相应项系数的比对得到了传统PID控制器;仿真分析表明了该方法能够有效性地补偿系统时滞,同时现实解耦;解决了多变量多时滞系统控制器设计复杂性的难题,有一定的工程参考价值。     

一种FIR型内模控制器设计的时域逆序列方法

根据内模控制器是被控对象逆动态逼近的原理，由被控对象的脉冲响应来设计ＦＩＲ型控制器。引入逼近函数序列与残差函数序列来表征控制器对被控对象逆动态的逼近程序，导出了控制器系数矢量应满足的矩阵方程，并以残差函数序列累积平方极小化为准则，给出了妥控制器系数矢量的矩阵ＱＲ分解算法。     

对角CARIMA模型多变量广义预测控制改进算法

为了增强多变量广义预测控制算法(MGPC)的实用性,对其实现形式进行了进一步的简化.利用对角CARIMA模型的结构特点,先对系统中单个输出变量期望值的自由响应部分进行分解推导,将其表达成自由响应项系数与系统输入输出变量已知值乘积的形式,得到此输出变量的预测表达式,然后将系统所有输出变量的预测表达式代入目标函数中,得到的控制增量等于控制器系数与参考轨迹、过程输入输出历史数据的乘积.控制器系数只与模型参数及设计参数有关,求解控制量时不再需要进行模型输出预报,控制器结构简单,实现容易.对比实验结果表明了该方法保持了常规MGPC方法的优秀控制性能.     

基于AT89C52的多路电压量输出设计

根据高频机箱中受控对象的使用要求,采用AT89C52为核心电路,设计了多路电压量输出控制器。经运行表明,该控制器运行稳定,使用方便。     

机器人鲁棒控制器参数演化设计

论文将讨论具有控制输入幅值限制的机器人轨迹跟踪控制问题。将利用基于信号补偿的鲁棒控制方法设计机器人的子关节系统控制器。该控制器由标称控制器和鲁棒补偿器组成。标称控制器对于一标称受控对象实现所希望的轨迹跟踪特性,鲁棒补偿器则用于减小实际受控对象和标称受控对象之间的差异对跟踪特性的影响。当输入存在饱和约束的情况下,对鲁棒补偿器进行了修改,并且基于演化寻优的方法求取鲁棒补偿器参数。     

基于模糊组合变量的自适应加权控制

针对多变量非线性系统,提出一种基于模糊组合变量的自就加权控制方法,首先利用模糊逻辑系统构造模糊组合变量,通过赋予各个模糊组合变量不同的权重来形成多变量非线性系统的加权控制;然后利用反向传播算法对量化系数和加权系数进行学习,从而有效地解决了多变量模糊控制系统难以设计和实现多维控制器的问题,最后通过仿真实验证明了该方法的有效性。     

多变量系统控制器的参数满意优化设计

为了将满意优化拓展到多变量系统中以解决多变量控制系统线性二次型(LQ)控制器设计中加权系数阵确定难的问题,提出多变量系统满意优化设计方法,通过设计满意度函数,构造出多变量系统的满意优化数学模型,并用改进遗传算法实现二级倒立摆系统LQ控制器满意优化设计.仿真结果显示,系统具有更满意的综合性能指标.证实了该方法的有效性和实用性.本文的研究对多变量系统优化设计具有一定的参考价值.     

准最优控制方法在发电机机炉负荷协调控制系统中应用

针对火力发电机组这一复杂的多变量的受控对象,本文提出了准最优机炉负荷协调控制系统的设计方案,该方案带有积分环节和前馈控制,并在PDP-11/44和FOX-3两个计算机系统中分别进行了实时和非实时的仿真试验,取得了满意的效果。     

Integrated servo-mechanical design of high-performance mechatronics using generalized KYP Lemma

High-performance mechatronics have specifications which are difficult to achieve when the mechanical plant is non-minimum phase and a low-order controller is used. In this paper, an integrated servo-mechanical design algorithm is proposed for systematic finite frequency redesign of a mechanical plant using the generalized Kalman–Yakubovic–Popov Lemma. The synthesis of a minimum phase plant is carried out based on a predesigned low-order controller, positive realness constraints, and performance specifications of the overall control system. Our simulation results using the proposed algorithm achieve a high-bandwidth control system with disturbance attenuation capabilities at the phase-stabilized resonant modes of the plant with the low-order controller.     

Adaptive control of rigid body satellite

The minimal controller synthesis （MCS） is an extension of the hyperstable model reference adaptive control algorithm. The aim of minimal controller synthesis is to achieve excellent closed-loop control despite the presence of plant parameter variations, external disturbances, dynamic coupling within the plant and plant nonlinearities. The minimal controller synthesis algorithm was successfully applied to the problem of decentralized adaptive schemes. The decentralized minimal controller synthesis adaptive control strategy for controlling the attitude of a rigid body satellite is adopted in this paper. A model reference adaptive control strategy which uses one single three-axis slew is proposed for the purpose of controlling the attitude of a rigid body satellite. The simulation results are excellent and show that the controlled system is robust against disturbances.     

Globally optimal robust process control

A computational approach is developed for designing a globally optimal controller which is robust to time-varying nonlinear perturbations in the plant. This controller design problem is formulated as an optimization with bilinear matrix inequality (BMI) constraints, and is solved to optimality by a branch and bound algorithm. The algorithm is applied to a reactive ion etcher, and provides superior performance while providing robustness to nonlinear plant/model mismatch. The algorithm is also applied to a well known benchmark problem.     

An Application of a Neural Self-Tuning Controller to an Overhead Crane

A neural network-based self-tuning controller is presented. The scheme of the controller is based on using a multilayer perceptron, or a set of them, as a self-tuner for a controller. The method proposed has the advantage that it is not necessary to use a combined structure of identification and decision, common in a standard self-tuning controller. The paper explains the algorithm for a general case, and then a specific application on a nonlinear plant is presented. The plant is an overhead crane which involves an interesting control problem related to the oscillations of the load mass. The method proposed is tested by simulation in different conditions. A comparison was made with a conventional controller to evaluate the efficiency of the algorithm.     

参数摄动机械臂鲁棒控制器的演化设

对于存在参数区间摄动的机械臂，提出了一种利用遗传算法进行鲁棒控制器演化设计的方法．将具有给定结构的控制器的参数编码后作为控制器种群，将机械臂的摄动参数编码后作为受控对象种群；对两个种群进行双向演化操作，得到对区间摄动系统具有足够鲁棒性的控制器和最差控制性能所对应的受控对象模型．对存在参数摄动的二自由度机械臂进行鲁棒控制器设计的结果表明，所提出的方法是有效的．     

A self-organizing time-optimal controller†

A kind of self-organizing controller is proposed, which can accomplish time-optimal control of the unknown plant through adaptation. The self-organizing controller consists of the controller which has a variable switching surface and the adaptation logic net which observes the state of the controlled plant and the control signal yielded by the controller. It can improve the parameters of the controller according to the adaptation algorithm. The proposed method required neither information on the plant dynamics nor its identification. Adaptation is carried out on line. Computer simulation shows the effectiveness of the method.     

Artificial neural networks controlled fast valving in a powergeneration plant

This paper presents an artificial neural-network-based controller to realize the fast valving in a power generation plant. The backpropagation algorithm is used to train the feedforward neural networks controller. The hardware implementation and the test results of the controller on a physical pilot-scale power plant setup are described in detail. Compared with the conventional fast valving methods applied to the same system, test results both with the computer simulation and on a physical pilot-scale power plant setup demonstrate that the artificial neural network controller has satisfactory generalization capability, reliability, and accuracy to be feasible for this critical control operation.     

具有大不确定性对象的分层切换控制方法

针对大模型不确定性对象的控制问题,提出了一种基于鲁棒控制理论的多模型分层切换控制方法.为减少覆盖不确定性需要的模型数量,采用多个乘性不确定模型来描述对象,并应用LMI方法设计控制器集合.考虑鲁棒控制中常用系统增益来度量不确定性,设计了一种基于不确定性增益估计的切换指标函数,并据此将控制器集合中合适的控制器连接到反馈回路中.理论分析表明系统BIBO稳定,且具有一定的扰动抑制能力.仿真实验结果验证了控制方法的有效性.     

基于神经网络的PID控制器

提出了一种新型PID控制器,该控制器利用BP网络实现PID参数的在线调整,采用RBF网络对被控对象在线辨识。仿真结果表明该控制器的控制效果优于传统的PID控制算法和模糊自适应PID控制算法。     

Nonlinear controller tuning based on a sequence of identifications of linearized time-varying models

A novel algorithm for tuning controllers for nonlinear plants is presented. The algorithm iteratively minimizes a criterion of the control performance. In each iteration one experiment is performed with a reference signal slightly different from the previous reference signal. The input–output signals of the plant are used to identify a linear time-varying model of the plant which is then used to calculate an update of the controller parameters. The algorithm requires an initial feedback controller that stabilizes the closed loop for the desired reference signal and in its vicinity, and that the closed-loop outputs are similar for the previous and current reference signals. The tuning algorithm is successfully tested on a laboratory set-up of the Furuta pendulum.