Cascade generalized predictive control strategy for boiler drum level

This paper proposes a cascade model predictive control scheme for boiler drum level control. By employing generalized predictive control structures for both inner and outer loops, measured and unmeasured disturbances can be effectively rejected, and drum level at constant load is maintained. In addition, nonminimum phase characteristic and system constraints in both loops can be handled effectively by generalized predictive control algorithms. Simulation results are provided to show that cascade generalized predictive control results in better performance than that of well tuned cascade proportional integral differential controllers. The algorithm has also been implemented to control a 75-MW boiler plant, and the results show an improvement over conventional control schemes.     

一种改进的广义预测控制算法

为了解决广义预测控制(GPC)算法本身计算量大以及无法兼顾快速性和超调的问题,提出了一种改进的广义预测控制算法。首先介绍了广义预测控制的基本算法和一种新型算法,包括算法的基本理论和优缺点,然后经过一定的数学分析,提出了一种改进算法,最后对3种算法进行了Matlab仿真,以比较静态误差。实验结果表明,该改进算法不仅能大大减少计算量,而且能很好地抑制超调,同时兼顾了快速性。     

基于神经网络的非线性系统约束预测控制

利用非线性激励函数的局部线性表示,提出一种可用于非线性过程的基于神经网络模型的约束广义预测控制算法。该算法将非线性搜索转化为只对当前控制增量的约束,避免了非线性优化求解,并不需要很多的计算量。文中给出了仿真结果。     

一类非线性系统的广义预测控制研究

提出一种针对双线性Hamm erste in模型的预测控制策略。该策略将双线性Hamm erste in模型中的无记忆非线性静态增益环节,改进成易于由中间变量求取控制量的环节,避免求解高阶方程根的困难,又对双线性环节采用双线性系统的广义预测控制。避免解非线性优化问题,使得到的中间变量的表达式具有解析形式。由于引入广义预测控制中多步预测的思想,抗噪声的能力显著提高。仿真结果验证了该策略的有效性。     

Experimental sensorless control for a coupled two-tank system using high gain adaptive observer and nonlinear generalized predictive strategy

In this paper an adaptive control for a coupled two-tank system is proposed. This control strategy uses a generalized predictive controller, whose main method is to minimize a multistage cost function defined over a prediction horizon. Furthermore, to implement this controller in the framework of the sensorless control from the only measurement of the liquid level in the bottom tank, an adaptive interconnected high gain observer is developed for estimating the liquid level in the upper tank and the two constant parameters of the system. Two design features are worth to be emphasized. Firstly, the control calibration is achieved through the tuning of only two scalar design parameters. Secondly, the exponential convergence to zero of the state observation and parameter estimation errors is established under a well defined condition. Finally, the theoretical results are corroborated through simulation and experimental results which highlight the efficiency and applicability of the proposed observer–controller scheme.     

Nonlinear model identification and adaptive model predictive control using neural networks

This paper presents two new adaptive model predictive control algorithms, both consisting of an on-line process identification part and a predictive control part. Both parts are executed at each sampling instant. The predictive control part of the first algorithm is the Nonlinear Model Predictive Control strategy and the control part of the second algorithm is the Generalized Predictive Control strategy. In the identification parts of both algorithms the process model is approximated by a series-parallel neural network structure which is trained by a recursive least squares (ARLS) method. The two control algorithms have been applied to: 1) the temperature control of a fluidized bed furnace reactor (FBFR) of a pilot plant and 2) the auto-pilot control of an F-16 aircraft. The training and validation data of the neural network are obtained from the open-loop simulation of the FBFR and the nonlinear F-16 aircraft models. The identification and control simulation results show that the first algorithm outperforms the second one at the expense of extra computation time.     

一种克服模型参数失配的广义预测控制算法

针对模型参数失配对广义预测控制输出的影响,提出了一种输出增量反馈的广义预测控制简化算法.该算法通过引入一个输出增量速度函数,设计了输出增量参考序列,以控制输出增量的方式间接控制系统输出;同时利用阶梯控制方式,对输入增量引入柔化系数矩阵进行约束,既避免了传统预测控制律中逆矩阵的求解,减少了计算量,又防止了控制量的剧烈变化;最后引入控制增量增益,利用这个自由度提高了系统的鲁棒稳定性.仿真结果表明:该预测控制简化算法能有效克服模型参数失配带来的影响,抑制系统输出调整过程中的输出波动,缩短调整时间,提高系统的动态特性,并抑制系统控制输入的剧烈变化.     

一种隐式广义预测自校正控制算法及仿真研究

广义预测控制是一种新型的远程预测控制方法,它集多种算法的优点为一体,具有较强的鲁棒性。采用不辨识对象模型参数的隐式算法,更是大大减少了计算工作量,节省了时间。该文介绍了一种隐式广义预测自校正控制算法且对其进行仿真研究,分析仿真结果,总结参数变化对整个系统性能的影响,结果说明了该算法的优越性和可行性。     

A simplified predictive control algorithm for disturbance rejection

Model predictive control (MPC) offers several advantages for control of chemical processes. However, the standard MPC may do a poor job in suppressing the effects of certain disturbances. This shortcoming is mainly due to the assumption that disturbances remain constant over the prediction horizon. In this paper, a simple disturbance predictor (SDP) is developed to provide predictions of the unmodeled deterministic disturbances for a simplified MPC algorithm. The prediction is developed by curve fitting of the past information. A tuning parameter is employed to handle a variety of disturbance dynamics and a procedure is presented to find an optimum value of the tuning parameter online. A comparison is made with the commonly used disturbance prediction on three example problems. The results show that an improved regulatory performance and zero offset can be achieved under both regular and ramp output disturbances by using the proposed disturbance predictor.     

Robust current control-based generalized predictive control with sliding mode disturbance compensation for PMSM drives

This paper addresses the current control of permanent magnet synchronous motor (PMSM) for electric drives with model uncertainties and disturbances. A generalized predictive current control method combined with sliding mode disturbance compensation is proposed to satisfy the requirement of fast response and strong robustness. Firstly, according to the generalized predictive control (GPC) theory based on the continuous time model, a predictive current control method is presented without considering the disturbance, which is convenient to be realized in the digital controller. In fact, it's difficult to derive the exact motor model and parameters in the practical system. Thus, a sliding mode disturbance compensation controller is studied to improve the adaptiveness and robustness of the control system. The designed controller attempts to combine the merits of both predictive control and sliding mode control, meanwhile, the controller parameters are easy to be adjusted. Lastly, the proposed controller is tested on an interior PMSM by simulation and experiment, and the results indicate that it has good performance in both current tracking and disturbance rejection.     

隐式广义预测自校正控制算法及其在氯化聚乙烯生产中的应用研究

广义预测控制具有柔化作用、多步预测、滚动优化和在线自适应校正等优点,能直接处理具有大滞后和不确定性的工业对象。该文采用隐式广义预测自校正控制算法,并将其应用于氯化聚乙烯生产控制中,仿真结果表明,该算法适用范围广,提高了控制精度。     

A nonlinear regression model-based predictive control algorithm

This paper presents a unique approach for designing a nonlinear regression model-based predictive controller (NRPC) for single-input-single-output (SISO) and multi-input-multi-output (MIMO) processes that are common in industrial applications. The innovation of this strategy is that the controller structure allows nonlinear open-loop modeling to be conducted while closed-loop control is executed every sampling instant. Consequently, the system matrix is regenerated every sampling instant using a continuous function providing a more accurate prediction of the plant. Computer simulations are carried out on nonlinear plants, demonstrating that the new approach is easily implemented and provides tight control. Also, the proposed algorithm is implemented on two real time SISO applications; a DC motor, a plastic injection molding machine and a nonlinear MIMO thermal system comprising three temperature zones to be controlled with interacting effects. The experimental closed-loop responses of the proposed algorithm were compared to a multi-model dynamic matrix controller (MPC) with improved results for various set point trajectories. Good disturbance rejection was attained, resulting in improved tracking of multi-set point profiles in comparison to multi-model MPC.     

基于启发式遗传算法的非线性神经网络预测控制器

本文提出一种以小脑模型（CMAC）网络为多步预测模型的非线性预测控制算法，并将启发式遗传算法引入到滚动优化中，以提高优化过程中的收敛速度和求解精度。仿真结果表明该算法是有效可行的。     

预测函数控制在水分定量控制中的应用

针对抄纸过程中水分定量系统具有非线性和纯滞后的特征,提出了将预测函数控制方法应用到水分定量系统中,对水分定量的控制,改善了控制系统稳定性及性能指标。仿真实验表明该方法有较强的跟踪性和鲁棒性。实际运行结果表明该方法具有较好的鲁棒性和较好的控制精度。     

Composite adaptive control of belt polishing force for aero-engine blade

The existing methods for blade polishing mainly focus on robot polishing and manual grinding.Due to the difficulty in high-precision control of the polishing force,the blade surface precision is very low in robot polishing,in particular,quality of the inlet and exhaust edges can not satisfy the processing requirements.Manual grinding has low efficiency,high labor intensity and unstable processing quality,moreover,the polished surface is vulnerable to burn,and the surface precision and integrity are difficult to ensure.In order to further improve the profile accuracy and surface quality,a pneumatic flexible polishing force-exerting mechanism is designed and a dual-mode switching composite adaptive control(DSCAC) strategy is proposed,which combines Bang-Bang control and model reference adaptive control based on fuzzy neural network(MRACFNN) together.By the mode decision-making mechanism,Bang-Bang control is used to track the control command signal quickly when the actual polishing force is far away from the target value,and MRACFNN is utilized in smaller error ranges to improve the system robustness and control precision.Based on the mathematical model of the force-exerting mechanism,simulation analysis is implemented on DSCAC.Simulation results show that the output polishing force can better track the given signal.Finally,the blade polishing experiments are carried out on the designed polishing equipment.Experimental results show that DSCAC can effectively mitigate the influence of gas compressibility,valve dead-time effect,valve nonlinear flow,cylinder friction,measurement noise and other interference on the control precision of polishing force,which has high control precision,strong robustness,strong anti-interference ability and other advantages compared with MRACFNN.The proposed research achieves high-precision control of the polishing force,effectively improves the blade machining precision and surface consistency,and significantly reduces the surface roughness.     

A new automatic contact point detection algorithm for AFM force curves

A new method for estimating the contact point in AFM force curves, based on a local regression algorithm, is presented. The main advantage of this method is that can be easily implemented as a computer algorithm and used for a fully automatic detection of the contact points in the approach force curves on living cells. The estimated contact points have been compared to those obtained by other published methods, which were applied either for materials with an elastic response to indentation forces or for experiments at high loading rates. We have found that the differences in the values of the contact points estimated with three different methods were not statistically significant and thus the algorithm is reliable. Also, we test the convenience of the algorithm for batch‐processing by computing the contact points of a force curve map of 625 (25×25) curves. Microsc. Res. Tech. 76:870–876, 2013. © 2013 Wiley Periodicals, Inc.     

基于T-S模糊模型的非线性系统广义预测控制

针对离散非线性系统,提出一种基于T-S模糊模型的广义预测控制方法。该方法将采样点的T-S模糊模型转化为采样点线性模型与非线性误差叠加的线性形式,通过迭代修正非线性误差,使具有非线性误差的线性模型预测控制律逐渐逼近采样点T-S模糊模型预测控制律。同时,该预测控制方法也能适用于当系统受输入输出约束时的控制。仿真结果验证了所提出的TS模糊模型广义预测方法有效。     

一种非线性系统的自适应无模型预测控制方法

为了避免基于模型的控制方法在控制非线性系统时存在建模困难和模型失配的问题,提出一种非线性系统的自适应无模型预测控制方法。该方法首先将非线性系统转化为由一组伪偏导数描述的线性系统,然后利用一种改进的投影算法在线估计这组伪偏导数,得到被控系统的泛模型。根据得到的泛模型,推导出预测模型,在此基础上根据预测控制滚动的优化策略求解二次目标函数得出最优控制律。通过对CSTR过程进行仿真验证,结果表明该方法具有良好的跟踪性能和较强的鲁棒性。     

一种抑制超调的快速跟踪预测控制律

提出一种抑制超调快速跟踪广义预测控制算法，此方法充分利用预测信息，在实际控制中考虑控制量的变化趋势，控制算法简单，在线计算量小，与其它算法相比提高了系统的瞬态响应品质，仿真实验说明了该算法的有效性。     

基于环境刚度模糊自适应估计的机器人力控制

在未知环境中为实现精确的接触力控制,需要力控制器能够适应环境的变化。该文将多模型模糊控制器引入到机器人力控制中来适应未知环境的变化,针对几种典型的接触环境刚度设计相应的模糊控制器。由于在环境变化时,很难得到精确的环境刚度值,该文对环境刚度进行模糊自适应估计,进而确定各个模糊力控制器的加权系数,模糊力控制器生成机器人位置控制系统的输入指令。仿真研究表明所设计的控制器是可行和有效的。