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。     

Considerations for multivariable control of the twin-roller strip caster

Twin-roller steel strip casters may offer advantages with respect to classical continuous casting hot rolling processes. Some works have reported control aspects of this process, however, the cross coupling elements have not been evaluated. In this work, the derivation of a 3 x 3 linearized multivariable model for process control purposes proposed in previous investigations is presented. The model was simplified to a 2 x 2 plant. The purpose of this work is to show that the process is highly interactive by performing an analysis of its multivariable characteristics. Some important considerations for process control design based on such multivariable characteristics of the process are discussed.     

多变量协调自适应控制器初步研究

本文初步研究了多变时过程控制器设计的优化目标和约束的统一表达体系,将模块多变量控制技术引入自适应控制器的设计过程,提出了多变量协调自适应控制器的概念,并初步解决了有约束的多目标优化控制的设计问题。     

软测量技术和约束控制在精馏塔优化控制中的应用

该文对过程控制中的软测量技术进行了论述,详细阐述了基于多元化逐步回归分析软测量模型的建立,并提出避免约束边界上跳变,保证测量值尽量往中心给定值靠近的方法。软测量技术和约束控制已成功应用于丁二烯精馏塔优化控制中。     

自适应神经元PID控制器在单元机组负荷控制系统中的应用

针对火电单元机组这类集炉、机、电为一体具有多变量强耦合,非线性及参数时变的受控对象,基于常规解耦控制技术的控制系统无法应用的问题,讨论了多变量自适应单神经元PID控制器在单元机组负荷控制中的设计及仿真,并给出了火电机组负荷微机控制系统的硬件和软件设计方案。     

An approach to design controllers for MIMO fractional-order plants based on parameter optimization algorithm

The parameter optimization method for multivariable systems is extended to the controller design problems for multiple input multiple output (MIMO) square fractional-order plants. The algorithm can be applied to search for the optimal parameters of integer-order controllers for fractional-order plants with or without time delays. Two examples are given to present the controller design procedures for MIMO fractional-order systems. Simulation studies show that the integer-order controllers designed are robust to plant gain variations.     

面向打磨机械臂的模糊自适应阻抗控制算法

为解决打磨机械臂在加工过程中轨迹移动和末端接触力的控制问题,讨论了一种自适应阻抗算法,能够实现力和位置的柔顺控制,并保证系统的运动稳定。同时引入模糊控制理论,对阻抗参数进行实时调整。以简化模型二自由度机械臂为例进行仿真实验。仿真结果表明,模糊自适应阻抗算法能够较为准确地控制机械臂末端的接触力,降低系统的超调,改善实时性,可以提高打磨机械臂的工作效率,在一定程度上满足打磨的精度要求。     

多变量PI型有约束后移步限预测控制器在水泥窑控制中的仿真研究

本文设计了具有比例积分结构的多变量ＰＩ型有移步限预测控制器，并给出在旋转式水泥窑过程控制中的仿真研究结果。     

Genetic Algorithm (GA) for Multivariable Surface Grinding Process Optimisation Using a Multi-objective Function Model

A genetic algorithm (GA) based optimisation procedure has been developed to optimise the surface grinding process using a multi-objective function model. The following ten process variables are considered in this work: wheel speed, workpiece speed, depth of dressing, lead of dressing, cross-feedrate, wheel diameter, wheel width, grinding ratio, wheel bond percentage, and grain size. The procedure evaluates the production cost and production rate for the optimum grinding conditions, subject to constraints such as thermal damage, wheel-wear parameters, machine-tool stiffness and surface finish. A worked example is used to illustrate how this procedure can be used to produce optimum production rate, low production cost, and fine surface quality for the surface grinding process.     

Properties of extended predictive control

Many tuning strategies for model predictive control algorithms have been proposed in the literature depending on the conditionality of the system matrix and the choice of its cost function. In this paper, the properties of a new predictive controller termed extended predictive control (EPC) are investigated and presented. These properties are important to the understanding of the unique tuning strategy of EPC. EPC is based on the assumption of infinite horizon which is preferable to guarantee stability. The EPC properties are derived using a second order plant with relatively large dead time and is applicable to any open-loop stable system. The tuning strategy of EPC was applied to generalized predictive control with good results.     

电液仿射非线性系统的离散模型跟随自适应控制

提出了一种将非线性状态反馈变换与模型跟随自适应控制(AMFC)相结合，适用于电液仿射非线性系统且无需在线参数估计和状态检测的离散AMFC控制方法。该方法利用对象的输入--输出数据序列取代在构造控制律时所需的对象状态信息，同时保证系统仍然能够实现准确的模型跟随控制。给出了满意的仿真结果。     

基于龙格-库塔法的自适应PID控制算法及其应用

针对大滞后系统提出一种基于四阶龙格-库塔预测模型的自适应PID控制算法,该算法是先对系统的状态变量进行步长预测,然后将此预测值代入系统输出方程,求出被控对象的步长测量值,再将该测量值作为反馈值进行PID控制运算。在控制运算中,沿二次型性能指标的负梯度方向对加权系数进行在线修改,实现了自适应PID的优化控制。仿真结果表明该预测控制算法的响应速度快,鲁棒性强,有很强的实用性。     

自适应跟踪算法在非圆柱表面磨削加工中的应用

研究了非圆柱表面磨谢加工新方法,对该磨削加工方法的关键技术进行了探讨。提出了采用自适应跟踪控制算法,使控制系统对给定高次曲线具有较强的跟踪精度,从而保证非国表面轴类磨创精度。     

Research on Power Control of Wind Power Generation Based on Neural Network Adaptive Control

-For the characteristics of wind power generation system is multivariable,nonlinear and random,in this paper the neural network PID adaptive control is adopted.The size of pitch angle is adjusted in time to improve the performance of power control.The PID parameters are corrected by the gradient descent method,and Radial Basis Functinn(RBF)neural network is used as the system identifier in this method.Simulation results shaw that by using neural adaptive PID controller the generator power control can inhibit effectively the speed and affect the output power of generator.The dynamic performance and robustness of the controlled system is good,and the performance of wind power system is improved.     

Fuzzy control of multivariable process by modified error decoupling

In this paper, a control concept for the squared (equal number of inputs and outputs) multivariable process systems is given. The proposed control system consists of two parts, single loop fuzzy controllers in each loop and a centralized decoupling unit. The fuzzy control system uses feedback control to minimize the error in the loop and the decoupler uses an adaptive technique to mitigate loop interactions. The decoupler predicts the interacting loop changes and modifies the input (error) of the loop controller. The controller was tested on the simulation model of "single component vaporizer" process. The results indicate that the decoupling controller shows better performance for set point and load changes.     

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.     

多变量时滞对象控制的DCS实现

采用软件控制仪表的概念，在DCS中实现了基于多变量频域理论和多变量Smith预估技术的多变量时滞对象的控制,并成功应用于一大型啤酒发 酵计算机控制系统，大大提高了系统的控制精度和DCS的自动化水平。     

Fuzzy model predictive control of non-linear processes using convolution models and foraging algorithms

In this paper, a fuzzy model predictive control (FMPC) approach is introduced to design a control system for nonlinear processes. The proposed control strategy has been successfully employed for representative, benchmark chemical processes. Each nonlinear process system is described by fuzzy convolution models, which comprise a number of quasi-linear fuzzy implications (FIs). Each FI is employed to describe a fuzzy-set based relation between control input and model output. A quadratic optimization problem is then formulated, which minimizes the difference between the model predictions and the desired trajectory over a predefined predictive horizon and the requirement of control energy over a shorter control horizon. The present work proposes to solve this optimization problem by employing a contemporary population-based evolutionary optimization strategy, called the Bacterial Foraging Optimization (BFO) algorithm. The solution of this optimization problem is utilized to determine optimal controller parameters. The utility of the proposed controller is demonstrated by applying it to two non-linear chemical processes, where this controller could achieve better performances than those achieved by similar competing controller, under various operating conditions and design considerations. Further comparisons between various stochastic optimization algorithms have been reported and the efficacy of the proposed approach over similar optimization based algorithms has been concluded employing suitable performance indices.     

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.     

Fractional adaptive control for an automatic voltage regulator

This paper presents the application of a direct Fractional Order Model Reference Adaptive Controller (FOMRAC) to an Automatic Voltage Regulator (AVR). A direct FOMRAC is a direct Model Reference Adaptive Control (MRAC), whose controller parameters are adjusted using fractional order differential equations. Four realizations of the FOMRAC were designed in this work, each one considering different orders for the plant model. The design procedure consisted of determining the optimal values of the fractional order and the adaptive gains for each adaptive law, using Genetic algorithm optimization. Comparisons were made among the four FOMRAC designs, a fractional order PID (FOPID), a classical PID, and four Integer Order Model Reference Adaptive Controllers (IOMRAC), showing that the FOMRAC can improve the controlled system behavior and its robustness with respect to model uncertainties. Finally, some performance indices are presented here for the controlled schemes, in order to show the advantages and disadvantages of the FOMRAC.