基于闭环降阶模型的pH中和过程自适应控制

针对pH中和过程,提出了基于闭环系统降阶模型的自适应PI控制器.首先利用三阶模型直接辨识高阶闭环系统,然后从闭环系统模型中提取反映闭环系统动态性能的特征量,并利用特征量建立相应的规则来整定控制器.该方法解决了传统自适应控制中难以获取精确对象模型的问题.仿真结果表明,所设计的方法实现了对pH中和过程的有效控制.     

模态跳变概率可控的Markov跳变线性系统的优化

研究模态跳变概率可控的Markov跣变线性二次模型的最优控制问题,考虑两类模态跳变控制策略:开环模态控制和闭环模态控制,应用策略迭代和性能势的概念,给出了最优的闭环模态控制优于最优的开环模态控制的充分条件,以指导最优控制器的设计,在已知最优的开环模态控制策略的基础上,应用策略迭代给出了构造闭环模态控制策略的方法,以进一步改善系统的性能.     

数据驱动闭环子空间预测控制方法研究与应用

提出一种完全数据驱动的闭环子空间辨识及预测控制器设计方法. 该方法完全由闭环系统的输入输出数据辨识子空间矩阵, 通过子空间矩阵的拆分, 排除了与扰动相关的模型输入, 进而获取子空间矩阵参数的无偏估计; 将辨识得到的闭环系统子空间矩阵描述直接作为预测模型, 设计预测控制器; 将其应用于某钢铁集团焦炉炭化室压力控制系统, 取得了良好的控制效果.

     

带遗忘因子的高阶闭环迭代学习控制器设计

为了解决迭代学习控制对系统存在的不确定性和非重复性干扰的鲁棒性问题,提出了一种带有遗忘因子的高阶闭环迭代学习控制器.该控制器中控制量包括反馈和前馈部分;其中,反馈控制采用简单的PID控制,迭代学习控制器设计为高阶PID型,它以前馈控制的形式作用于对象.通过引入遗忘因子对迭代学习控制器沿迭代方向进行滤波以,削弱系统模型的不确定部分及非重复干扰对系统收敛性的影响.仿真实验证明了该学习控制器的有效性和实用性.     

基于H∞方法的不确定系统迭代学习控制设计

针对不确定线性离散系统,研究了开闭环型的鲁棒迭代学习控制器。给出了控制器收敛的充分条件,根据此条件,将迭代学习控制的设计问题转化为H∞设计问题,提出了一种兼具反馈闭环控制与前馈学习控制的鲁棒迭代学习控制律,并采用H∞性能指标对系统进行优化,使系统的收敛率基于H∞最优,然后使用线性不等式（LMI）方法求解迭代学习控制器的参数。仿真实例表明了该设计方法的有效性。     

高超声速飞行器应用保护映射的大包线控制律

针对高超声速飞行器模型具有高度非线性和易变的动态特性,应用保护映射理论提出了一种高超声速飞行器大包线控制律设计方法.首先,结合间隙度量理论建立高超声速飞行器线性变参数(linear parameter-varying,LPV)模型,然后设计控制器结构并计算初始点的控制器参数,并根据保护映射理论分析初始控制器使闭环系统稳定的参数区间,通过迭代运算自适应地获得满足性能要求的控制器参数集合.仿真结果表明,建立的LPV模型具有良好的精确度;所设计的大包线控制律能够满足高超声速飞行器的性能要求,并且保证系统在飞行域内全局稳定.     

自适应的时变比例增益的预测PID控制器

为了利用PID控制获得先进的控制性能,将广义预测控制(GPC)用于PID参数的实时优化,在此基础上提出了一种新的基于GPC的自适应PID控制器的设计方法.该PID控制器具有时变的比例增益,并且PID控制器的设计利用了GPC的未来参考输入.因此,GPC控制律能由设计的PID控制器精确实现.为使GPC控制器稳定地获得比例增益,采用了基于互质因子分解扩展的强稳定GPC,独立于利用标准GPC设计的闭环系统而重新设计GPC控制器,保证了闭环系统的稳定性.此外,利用递推最小二乘法对系统进行在线辨识,修正模型参数,增强了系统的抗扰性.以一阶时滞非最小相位系统为被控对象,在Matlab中对该设计方法进行了仿真,仿真结果验证了该方法的有效性.     

同步发电机励磁控制器设计

针对基于开环迭代学习控制的同步发电机励磁控制器存在稳定性差、易受干扰影响、不能完全跟踪期望轨迹的问题,设计了一种基于开闭环PID迭代学习控制与电力系统稳定器结合的同步发电机励磁控制器;在开环迭代学习控制的基础上,引入闭环反馈控制,将上一次的输入和输出误差及当前的输出误差作为当前的控制输入,从而构成开闭环PID型迭代学习控制;同时,利用电力系统稳定器提供的附加正阻尼信号来改善同步发电机励磁控制系统的电压稳定性和功角稳定性。仿真结果表明,该励磁控制器具有较强的鲁棒性,提高了系统稳定性。     

最优迭代学习控制的鲁棒稳定性及其在注塑机控制中的应有

提出了一种鲁棒最优迭代控制器的设计方法.对于任意有界的参考输出和不确定的初 始值,建立了由最优迭代学习控制器保证闭环系统有界输入有界输出(BIBO)鲁棒稳定性的充要 条件.实际应用中可根据不确定初始设定值和干扰对加权矩阵进行调整,从而保证闭环系统性能 随迭代过程的进行而得到改进.在注塑机控制中的应用验证了本文结论的有效性.     

执行器饱和受限不确定系统的次序优化多步预测控制

设计一种次序优化机制,提出一类饱和受限不确定系统的次序优化多步预测控制方法.将系统的输入分解成多个子集合,在各采样周期仅对其中一个集合的输入进行优化,待优化的输入保持上一优化值.针对每个输入集合,确定闭环系统的不变集条件,将次序优化预测控制器设计问题转化成“最小-最大”优化问题,通过求解一组线性矩阵不等式问题得到控制器.仿真算例表明,采用次序优化预测控制方法可以减少控制器的设计时间.     

Iterative identification and control design with optimal excitation signals based on <Emphasis Type="Italic">υ</Emphasis>-gap

An iterative identification and control design method based on υ-gap is given to ensure the stability of closed-loop system and control performance improvement. The whole iterative procedure includes three parts: the optimal excitation signals design, the uncertainty model set identification and the stable controller design. Firstly the worst case υ-gap is used as the criterion of the optimal excitation signals design, and the design is performed via the power spectrum optimization. And then, an uncertainty model set is attained by system identification on the basis of the measure signals. The controller is designed to ensure the stability of closed-loop system and the closed-loop performance improvement. Simulation result shows that the proposed method has good convergence and closed-loop control performance. Supported by the National Natural Science Foundation of China (Grant Nos. 60574055, 60874073), the Specialized Research Fund for Doctoral Program of Higher Education of China (Grant No. 20050056037), and the Tianjin Science and Technology Keystone Project (Grant No. 08ZCKFJC27900)     

A closed-loop data based test for robust performance improvement in iterative identification and control redesigns

In robust iterative identification and control redesign techniques, a stabilizing controller connected in a closed loop is normally replaced by an alternative attractive stabilizing controller to improve robustness and performance of the closed-loop system. In this paper, novel test methods are proposed to check whether a new stabilizing controller improves performance or not when the existing controller is replaced by this new controller in the closed loop. The proposed tests are based on closed-loop data and no plant model, and can be used for both the SISO and MIMO linear time-invariant systems. For the proposed tests, the plant dynamics is assumed to be unknown whereas the existing and new controller transfer function matrices are known to the designer. These assumptions are common in iterative identification and control redesign techniques. The performance improvement test methods proposed in this paper build on the experimental set-up proposed in Dehghani, Lecchini, Lanzon, and Anderson (2009) which was used to only check whether controllers ensure internal stability of a feedback interconnection or not. In this paper, new test methods are proposed to ascertain robust performance improvement that cannot be obtained from test results of Dehghani et al. (2009). A numerical example is illustrated to show effectiveness of the proposed test methods.     

A nonparametric approach to design robust controllers for uncertain systems: Application to an air flow heating system

This paper presents an approach to design robust fixed structure controllers for uncertain systems using a finite set of measurements in the frequency domain. In traditional control system design, usually, based on measurements, a model of the plant, which is only an approximation of the physical system, is first built, and then control approaches are used to design a controller based on the identified model. Errors associated with the identification process as well as the inevitable uncertainties associated with plant parameter variations, external disturbances, measurement noise, etc. are expected to all contribute to the degradation of the performance of such a scheme. In this paper, we propose a nonparametric method that uses frequency-domain data to directly design a robust controller, for a class of uncertainties, without the need for model identification. The proposed technique, which is based on interval analysis, allows us to take into account the plant uncertainties during the controller synthesis itself. The technique relies on computing the controller parameters for which the set of all possible frequency responses of the closed-loop system are included in the envelope of a desired frequency response. Such an inclusion problem can be solved using interval techniques. The main advantages of the proposed approach are: (1) the control design does not require any mathematical model, (2) the controller is robust with respect to plant uncertainties, and (3) the controller structure can be chosen a priori, which allows us to select low-order controllers. To illustrate the proposed method and demonstrate its efficacy, an application to an air flow heating system is presented.     

Fixed‐order robust H∞ control and control‐oriented uncertainty set shaping for systems with ellipsoidal parametric uncertainty

In this paper, sufficient conditions for robust output feedback controller design for systems with ellipsoidal parametric uncertainty are given in terms of solutions to a set of linear matrix inequalities. A polynomial method is employed to design a fixed‐order controller that assigns closed‐loop poles within a given region of the complex plane and that satisfies an H_∞ performance specification. The main feature of the proposed method is that it can be extended easily for control‐oriented uncertainty set shaping using a standard input design approach. Consequently, the results can be extended to joint robust control/input design procedure whose controller structure and performance specifications are translated into the requirements on the input signal spectrum used in system identification. This way, model uncertainty set can be tuned for the robust control design procedure. The simulation results show the effectiveness of the proposed method. Copyright © 2010 John Wiley & Sons, Ltd.     

For model-based control design, closed-loop identification gives better performance

We compare open loop versus closed loop identification when the identified model is used for control design, and when the system itself belongs to the model class, so that only variance errors are relevant. Our measure of controller performance (which is used as our design criterion for identification) is the variance of the error between the output of the ideal closed loop system (with the ideal controller) and that of the actual closed loop system (with the controller computed from the identified model). Under those conditions, we show that, when the controller is a smooth function of the input-output dynamics and the disturbance spectrum, the best controller performance is achieved by performing the identification in closed loop with an operating controller that we characterize. For minimum variance and model reference control design criteria, we show that this ‘optimal operating controller for identification’ is the ideal controller. This then leads to a suboptimal but feasible iterative scheme.     

ILC strategy for progress improvement of economic performance in industrial model predictive control systems

A novel approach to progress improvement of the economic performance in model predictive control (MPC) systems is developed. The conventional LQG based economic performance design provides an estimation which cannot be done by the controller while the proposed approach can develop the design performance achievable by the controller. Its optimal performance is achieved by solving economic performance design (EPD) problem and optimizing the MPC performance iteratively in contrast to the original EPD which has nonlinear LQG curve relationship. Based on the current operating data from MPC, EPD is transformed into a linear programming problem. With the iterative learning control (ILC) strategy, EPD is solved at each trial to update the tuning parameter and the designed condition; then MPC is conducted in the condition guided by EPD. The ILC strategy is proposed to adjust the tuning parameter based on the sensitivity analysis. The convergence of EPD by the proposed ILC has also been proved. The strategy can be applied to industry processes to keep enhancing the performance and to obtain the achievable optimal EPD. The performance of the proposed method is illustrated via an SISO numerical system as well as an MIMO industry process.     

Accurate identification for control: the necessity of an iterativescheme

If approximate identification and model-based control design are used to accomplish a high-performance control system, then the two procedures must be treated as a joint problem. Solving this joint problem by means of separate identification and control design procedures practically entails an iterative scheme. A frequency-response identification technique and a robust control design method are used to set up such an iterative scheme. Each identification step uses the previously designed controller to obtain new data from the plant. The associated identification problem has been solved by means of a coprime factorization of the unknown plant. The technique's utility is illustrated by an example     

A decade of progress in iterative process control design: from theory to practice

One of the most active areas of research in the nineties has been the study of the interplay between system identification and robust control design. It has led to the development of “control-oriented identification design”, the paradigm being that, since the model is only a tool for the design of a controller, its accuracy (or its error distribution) must be tuned towards the control design objective. This observation has led to the concept of “iterative identification and control design” and, subsequently, to model-free iterative controller design, in which the controller parameters are iteratively tuned on the basis of successive experiments performed on the real plant, leading to better and better closed-loop behaviour. These iterative methods have found immediate applications in industry; they have also been applied to the optimal tuning of PID controllers. This paper presents the progress that has been accomplished in iterative process control design over the last decade. It is illustrated with some applications in the chemical industry.     

Application of an iterative identification for control scheme to a neutralization process

In this paper an iterative scheme for identification and control is discussed. During the identification step a plant model which is suitable for the subsequent controller design step is obtained by estimation of the (dual) Youla-parameter from measurements of the input and output of the plant. Using the identified plant model, the frequency response of the ideal controller which perfectly realizes the desired closed-loop response for set-point changes is computed. This controller, in general, may not be realizable or is of high-order. A realizable, low-order controller is then calculated using frequency-weighted approximation. These steps are repeated until the performance of the closed-loop system is satisfactory or cannot be improved further. The proposed scheme is applied successfully to the identification and control of a continuous neutralization reactor.     

Robust iterative learning controller design using the performance weighting function of feedback control systems

Iterative learning controllers combined with existing feedback controllers have prominent capability of improving tracking performance in repeated tasks. However, the iterative learning controller has been designed without utilizing effective information such as the performance weighting function to design a feedback controller. In this paper, we deal with a robust iterative learning controller design problem for an uncertain feedback control system using its explicit performance information. We first propose a robust convergence condition in the ?₂-norm sense for an iterative learning control (ILC) scheme. We present a method to design an iterative learning controller using the information on the performance of the existing feedback control system such as performance weighting functions and frequency ranges of desired trajectories. From the obtained results, several design criteria for iterative learning controller are provided. Through analysis on the remaining error, the loop properties before and after learning are compared. We also show that, in the ?₂-norm sense, the remaining error can be less than the initial error under certain conditions. Finally, to show the validity of the proposed method, simulation studies are performed.