基于改进遗传算法的多变量DMC参数优化

针对常规PID控制策略对多变量控制系统控制不理想的问题,提出了一种基于改进遗传算法的多变量动态矩阵控制。该方法将控制方法和参数优化算法相结合,对多变量动态矩阵控制参数进行寻优。在此基础上,对遗传算法进行改进,提高了其寻优能力。仿真结果表明,改进后的遗传算法能快速准确地跟踪设定值,减小调节时间。     

多变量非自衡系统的预测控制研究

该文研究了多变量非自衡对象的特点及控制问题，推导了一种适用于非自衡系统的多变量预测控制算法，通过对环境试验设备温度湿度控制系统的仿真实验，证实了此算法的有效性和实用性。     

基于粒子群优化PIDNN的温室温湿度解耦控制仿真

针对温室系统的温湿度解耦控制,提出了利用粒子群算法来优化PID神经元网络权值的控制方法。仿真结果表明优化后的PIDNN解耦控制器调节时间短,超调量小,增强了控制系统的鲁棒性,为多变量强耦合系统的解耦控制提供了一种有效的方法。     

遗传规划算法实现控制器的设计和优化

根据大型火电机组协调控制系统对象的特性,提出了采用遗传规划算法来实现控制器参数的寻优.该方法弥补了多变量控制器传统设计方法的不足,即在将多变量控制问题转化为多个单变量控制问题过程中对系统内部所存在的偶合性的削弱.仿真结果表明借助于该方法获得的控制器参数,能够使被控对象具有优秀的控制品质,并且通过优化PID控制器参数,也验证了遗传规划算法的全局搜索性.     

基于自运动控制的冗余度机器人运动学优化

基于自运动变量与雅可比矩阵零空间矢量的关系,提出了冗余度机器人自运动变量选取的新方法。基于线性化思想,建立了冗余度机器人运动学性能指标统一的表达式,并推导了性能指标的自运动变量解析式。利用非线性反馈,建立了基于自运动变量的优化控制系统,从而提出了冗余度机器人优化控制的新算法。该算法减少了系统的状态变量,从而简化了优化控制系统,比起以往的优化算法,如梯度投影法等,具有运算量小、优化能力强等优点,特别是在多冗余度机器人控制系统中具有很高的应用价值。利用计算机仿真证实了所提优化方法的可行性。     

基于工业控制计算机的移动机器人控制系统设计

为复杂运动控制算法建立实验平台,以工业控制计算机为核心,设计了一种基于工业控制计算机的移动机器人控制系统,介绍了该控制系统的机械结构、硬件组成和软件系统的构成,对控制系统硬件部分进行了详细分析和设计,详细介绍了控制系统的动力系统、感知系统和中央控制系统的构成及布局。针对所构建的机器人系统,能够实现基于人工势场的机器人路径轨迹规划等算法验证,能够用于多传感器信息融合算法测试、路径轨迹规划策略测试、图像检测识别相关算法测试等。     

基于SA算法的压力容器离散变量优化设计研究

考虑制造工艺要求，将所有设计变量均视为离散变量（包括一般离散变量和伪离散变量），并就这两种情况下状态产生函数的设计原理进行深入研究，解决了将模拟退火算法用于离散变量函数优化的关键技术问题，介绍了一种基于模拟退火算法的离散变量函数优化的新方法。建立了以壳体质量最轻为设计目标、以内径（公称直径）和名义壁厚为设计变量的优化设计的数学模型。该模型具有较多的局部极值点，用以数学规划理论为基础的经典约束优化方法求解效果较差，用基于模拟退火算法的离散变量优化设计方法可以取得良好的效果。     

基于RFID的轨道机器人定位系统研究

研究了一种用于工业轨道机器人的射频识别全局定位方法,通过分析圆形轨道定位的技术要求提出了一种针对这种定位要求的解决算法,并设计一种基于PLC的定位控制系统。实验分析表明,RFID具有很高的定位精度和抗干扰能力,是用于工业轨道机器人的一种简单、可靠的定位方法。     

退火炉温控系统的模糊免疫控制研究与仿真

采用模糊控制与人工免疫控制方法结合的策略,设计了一种模糊免疫PID控制器用于炉温控制系统,仿真实验表明,该控制器具有良好的自适应性和鲁棒性能。     

真空退火温度的建模与解耦控制

真空退火炉中温度的精确控制是一个多变量控制问题。为实现退火温度的精确控制,以真空退火炉现场实际采集的数据为基础,辨识出真空退火炉温区温度的二阶滞后模型,提出一种二阶滞后系统的多变量预测函数解耦控制算法。将多变量系统的解耦控制问题化简为若干个单变量系统的预测函数控制,采取分散优化策略代替整体优化,利用预测函数控制算法的特点,引入基函数增加了设计的自由度,减少了在线计算量,使参数设计和算法过程求解大为简化,得到一个解析的控制量计算方程,实现退火温度二阶滞后系统的多变量预测函数解耦控制。仿真结果表明,控制方法优于传统或改进的PID控制系统。将该控制方法应用于实际的 2 100×2 100真空退火炉退火温度控制系统中,取得了很好的控制效果。     

多变量主动悬架系统的一种自适应神经元控制策略

研究了车辆主动悬架这个多变量不确定系统的自适应控制问题。提出了一种新型的单神经元多变量控制器。给出一种综合误差的概念,将综合误差与传统的单神经元控制器相结合,得到一种基于综合误差理论的单神经元控制器,它可以同时直接调控被控对象的多输出变量。将该控制方法应用于1/4主动悬架系统,采用二次型性能指标对控制器参数进行了优化设计。研究了在不同的悬架参数及随机路面输入情况下控制器的自适应性能,并与被动悬架及传统神经元控制的主动悬架进行了性能对比。仿真结果表明,所提出的控制器可使车辆获得更为优良的综合减振性能,可显著改善平顺性,是一种简单有效且鲁棒性较好的自适应控制器。该控制方法为主动悬架及类似的多变量不确定系统的控制提供了一种可能的简捷有效的新途径。     

MIMO extended predictive control-implementation and robust stability analysis

The objective of this work is to develop a new tuning strategy for multivariable extended predictive control (EPC). A natural concern is the problem of ill conditionality in controlling multi-input multi-output (MIMO) systems. The main advantage of EPC is that it has a simple and effective tuning strategy that results in a well-conditioned system which can achieve tight closed-loop response. Moreover, unlike most existing model predictive control tuning strategies, the proposed strategy establishes a direct relationship between one main tuning parameter for each subprocess of the MIMO system. This tuning method has been derived based on the assumption of an infinite control horizon resulting in powerful stability for the nominal case and in the presence of model uncertainty. This tuning method is applicable to unconstrained multivariable processes, and was proven to have good control on nonsquare systems. The main features of the new tuning strategy are practically illustrated on a MIMO temperature system with improved control performance as compared to move suppressed predictive control.     

Adaptive Control of Multivariable Process Using Recurrent Neural Networks

Abstract

Industrial processes are naturally multivariable in nature, which also exhibit non-linear behavior and complex dynamic properties. The multivariable four-tank system has attracted recent attention, as it illustrates many concepts in multivariable control, particularly interaction, transmission zero, and non-minimum phase characteristics that emerge from a simple cascade of tanks. So, the multivariable laboratory process of four interconnected water tanks is considered for modeling and control. For processes which show nonlinear and multivariable characteristics, classical control strategies like PIDs have performance limitations. Hence, intelligent approaches like Neural Networks (NN) is an important term in this juncture. The use of Recurrent Neural Network (RNN) is apt for modeling and control of nonlinear dynamic processes as it contains the past information about the process. The objective of the current study is to design and implement an adaptive control system using RNN for a nonlinear multivariable process.

The proposed adaptive design comprises an estimator based on RNN, which adapts online and predicts one step ahead output. A Recursive Least Square (RLS) based back propagation algorithm is used for training the network. The controller used is also a RNN, which minimizes the difference between the predicted output and reference trajectory. The objective function is minimized using a steepest descent algorithm which gives the optimum control input. Desired performance of the system is ensured by the parallel operation of both. The proposed control strategy is implemented in a laboratory scale four tank system. The trajectory tracking and disturbance rejection response obtained are compared with the response obtained by using a well designed decoupled, decentralized IMC controller.     

An approach to predictive control of multivariable time-delayed plant: stability and design issues

In this paper, a new method of multivariable predictive control is presented. The main advantage of a predictive approach is that multivariable plants with time delays can be easily handled. The proposed control algorithm also introduces a compact and simple design in the case of higher-order and nonminimal phase plants, but it is limited to open-loop stable plants. The algorithm of the proposed multivariable predictive control is developed, designed, and implemented on an air-conditioned system. The stability of the proposed control law is discussed.     

Quantitative assessment of the tendency of complex multivariable control system to degeneration

The degeneration property of dynamic multivariable control systems is considered. In this work, by degeneration for a multivariable systems is meant primarily a decrease and even a loss of its operating capacity. A technique of quantitative assessment of the tendency of multivariable control systems to degeneration was developed using a singular decomposition of criteria matrices formed on the basis of the matrix formalism of Faddeev-Leverrier algorithm, Gramian representation, matrix coefficient of the expansion of the system output vector in the derivatives of the control input vector, and with the use of the matrix formalism of Sylvester’s and Lyapunov’s equations     

基于神经网络的热轧带钢卷取温度预测

热轧带钢卷取温度是影响成品带钢性能指标的重要工艺参数之一,其层流控制系统具有高度的非线性。采用附加动量BP算法,建立了基于神经网络前馈与数学模型反馈的联合层流控制系统,仿真结果表明,采用神经网络预测的卷取温度与实测温度相近,结果可信,为层流数学模型参数的在线辨识打下了坚实的基础。     

Application of memetic algorithm in modelling discrete-time multivariable dynamics systems

Evolutionary algorithm (EA) such as genetic algorithm (GA) has demonstrated to be an effective method for identification of single-input–single-output (SISO) system. However, for multivariable systems, increasing the orders and the non-linear degrees of the model will result in excessively complex model and the identification procedure for the systems is more often difficult because couplings between inputs and outputs. There are more possible structures to choose from and more parameters are required to obtain a good fit. In this work, a new model structure selection in system identification problems based on a modified GA with an element of local search known as memetic algorithm (MA) is adopted. This paper describes the procedure and investigates the performance and the effectiveness of MA based on a few case studies. The results indicate that the proposed algorithm is able to select the model structure of a system successfully. A comparison of MA with other algorithms such as GAs demonstrates that MA is capable of producing adequate and parsimonious models effectively.     

Centralized PI control for high dimensional multivariable systems based on equivalent transfer function

This article presents a new scheme to design full matrix controller for high dimensional multivariable processes based on equivalent transfer function (ETF). Differing from existing ETF method, the proposed ETF is derived directly by exploiting the relationship between the equivalent closed-loop transfer function and the inverse of open-loop transfer function. Based on the obtained ETF, the full matrix controller is designed utilizing the existing PI tuning rules. The new proposed ETF model can more accurately represent the original processes. Furthermore, the full matrix centralized controller design method proposed in this paper is applicable to high dimensional multivariable systems with satisfactory performance. Comparison with other multivariable controllers shows that the designed ETF based controller is superior with respect to design-complexity and obtained performance.     

基于免疫算法的差速驱动智能车辆轨迹跟踪研究

将车轮运动看成一种无侧滑、纯滚动运动.简化车辆运动模型为两轮共轴但独立差速驱动.并将基于免疫学原理中的人工免疫算法引入到车辆运动控制中.通过对车辆的轨迹跟踪仿真实验,验证免疫算法在车辆控制过程中的优劣.仿真结果表明,将人工免疫算法引入车辆运动控制中取得了良好的效果,具有良好的控制性能.     

基于信息熵的模拟退火算法优化铣削加工模糊控制器参数

提出以信息熵为性能指标、用模拟退火算法优化模糊控制器参数的新方法应用于铣削加工恒力约束控制。仿真表明:将优化后的模糊控制器应用于切深不变的铣削加工控制中可以取得良好的控制效果;变切深时,可在小范围进行参数优化。