Multivariable Control Performance Assessment Based on Generalized Minimum Variance Benchmark

This paper is concerned with the control performance assessment based on the multivariable generalized minimum variance benchmark.An explicit expression for the feedback controller-invariant(the generalized minimum variance)term of the multivariable control system is obtained,which is used as a standard benchmark for the assessment of the control performance for multi input multi output(MIMO)process.The proposed approach is based on the multivariable minimum variance benchmark.In comparison with the minimum variance benchmark, the developed method is more reasonable and practical for the control performance assessment of multivariable systems.The approach is illustrated by a simulation example and an industrial application.

Robust Design for Generalized Minimum Variance Controllers

In order to improve the robustness and noise resistance of generalized minimum valance cothrol systems, several generalizedminimum variance control schemes are synthetically analyzed. The output variance caused by stochastic noise is decomposed to two parts. One part accords with the output variance of minboum vedance control and the other is the additional term of output variance causedby the control weighting factors. At the same time, the sensitivity function of modeling error is also deduced. A new robast design method that can minimize the sensitivity and the additional part of output variance is Presented by regulating variable parameters of contollers. The simulation results of self-tuning control show the effect of this method.

SIMULATION STUDY OF GENERALIZED MINIMUM VARIANCE CONTROL FOR AN EXTRACTION TURBINE

In an extraction turbine, pressure of the extracted steam and rotate speed of the rotor are two important controlled quantities. The traditional linear state feedback control method is not perfect enough to control the two quantities accurately because of existence of nonlinearity and coupling. A generalized minimum variance control method is studied for an extraction turbine. Firstly, a nonlinear mathematical model of the control system about the two quantities is transformed into a linear system with two white noises. Secondly, a generalized minimum variance control law is applied to the system. A comparative simulation is done. The simulation results indicate that precision and dynamic quality of the regulating system under the new control law are both better than those under the state feedback control law.

基于MVC算法的机械爪控制器设计

传统机械手爪控制器多采用经典PID控制算法,尽管PID控制实施简单,但是很难保证手爪以最优的方式运动。基于最小方差控制(MVC)理论以手爪的波动方差为控制目标,通过求解丢番图方程获得控制器增益,实现手爪波动方差最小的控制效果。在实例仿真中与传统的PID算法进行比较,结果表明,MVC算法控制器有效地改善了动态性能,有较强的信号跟踪能力。

Modeling and Adaptive Self-Tuning MVC Control of PAM Manipulator Using Online Observer Optimized with Modified Genetic Algorithm

In this paper, the application of modified genetic algorithms (MGA) in the optimization of the ARX Model-based observer of the Pneumatic Artificial Muscle (PAM) manipulator is investigated. The new MGA algorithm is proposed from the genetic algorithm with important additional strategies, and consequently yields a faster convergence and a more accurate search. Firstly, MGA-based identification method is used to identify the parameters of the nonlinear PAM manipulator described by an ARX model in the presence of white noise and this result will be validated by MGA and compared with the simple genetic algorithm (GA) and LMS (Least mean-squares) method. Secondly, the intrinsic features of the hysteresis as well as other nonlinear disturbances existing intuitively in the PAM system are estimated online by a Modified Recursive Least Square (MRLS) method in identification experiment. Finally, a highly efficient self-tuning control algorithm Minimum Variance Control (MVC) is taken for tracking the joint angle position trajectory of this PAM manipulator. Experiment results are included to demonstrate the excellent performance of the MGA algorithm in the NARX model-based MVC control system of the PAM system. These results can be applied to model, identify and control other highly nonlinear systems as well.

测量噪声对GMVC性能影响分析与改进方法

由于工作环境的改变及各类干扰信号的影响,易使控制器性能降低,影响控制效果,因此需设计出一种可以在复杂情况下能够稳定工作的控制器。针对上述问题,提出了一种基于广义最小方差控制(GMVC)的控制器设计方法。GMVC控制器设计过程一般不考虑测量噪声,然而在实际过程中,系统不可能工作于理想情况下,会受到测量噪声的影响。因此首先分析了测量噪声对GMVC控制系统输出的影响,提出了采用数字滤波器的方式减小测量噪声对控制系统输出的影响,仿真结果说明了该方法的可行性和有效性。

Two Adaptive Control Algorithms in Semi-Active Suspension System

To reduce the vibration in the suspension, semi active suspension system was employed. And its CARMA model was built. Two adaptive control schemes, the minimum variance self tuning control algorithm and the pole configuration self tuning control algorithm, were proposed. The former can make the variance of the output minimum while the latter can make dynamic behavior satisfying. The stability of the two schemes was analyzed. Simulations of them show that the acceleration in the vertical direction has been reduced greatly. The purpose of reducing vibration is realized. The two schemes can reduce the vibration in the suspension and have some practicability.

基于MVC的实时流媒体自适应跟踪传输算法

为了更好地保证流媒体系统实时约束下的同步性能,解决流媒体缓冲区设置中同步与播放时延的矛盾,使缓冲区长度设置在实时性约束下最大化,在实时环境条件下,给出了一种缓冲区占用水平设定值的动态确定方法,在仿真实验中以此作为流媒体速率控制系统的设定值.为了克服传统控制方法超调量过大的弊端,选择了最小方差控制(MVC,minimum variance control),设计了基于最小方差控制的实时流媒体缓冲区自适应设定值跟踪传输算法.仿真实验结果表明,该方法能够对不同的网络时延自适应,计算出合适的缓冲区占用水平设定值,实现了缓冲区占用水平对设定值的跟踪,播放具有良好的实时性.

动态数据校正技术用于GMVC控制器性能评估的改进

在实际工业生产过程中,控制器性能的好坏直接影响了系统的收益;事实上,由于工作环境的复杂性,控制器容易受到各种干扰信号的影响,这通常会导致控制效果不符合预期;因此,对控制器的性能进行评估显得尤为重要;针对反馈系统中测量噪声的干扰问题,分析了测量噪声对基于广义最小方差控制(GMVC)的控制器性能评估(CPA)结果的影响;为了提升CPA的精度,提出了一种动态数据校正(DDR)方法来降低测量噪声对系统性能的影响;首先,在SISO和MIMO系统中引入基于GMVC的CPA;接着讨论了测量噪声对CPA结果的影响;最后,采用DDR滤波器来提升CPA结果的准确性;仿真中系统在不同情况下输出的比较验证了DDR滤波器的良好性能。

Nonlinear Decoupling PID Control Using Neural Networks and Multiple Models

For a class of complex industrial processes with strong nonlinearity, serious coupling and uncertainty, a nonlinear decoupling proportional-integral-differential （PID） controller is proposed, which consists of a traditional PID controller, a decoupling compensator and a feedforward compensator for the unmodeled dynamics. The parameters of such controller is selected based on the generalized minimum variance control law. The unmodeled dynamics is estimated and compensated by neural networks, a switching mechanism is introduced to improve tracking performance, then a nonlinear decoupling PID control algorithm is proposed. All signals in such switching system are globally bounded and the tracking error is convergent. Simulations show effectiveness of the algorithm.

Performance Assessment of a PID Controller

Performance assessment of a proportional-integral-derivative （PID） controller is condueted using the PIDachievable minimum variance as a benchmark. When the process model is unknown, we can estimate the PID-achievable minimum variance and the corresponding parameters by routine closed-loop operation data. Simulation results show that the process output variance is reduced by retuning controller parameters.

Assessment of Model Predictive Control Performance Criteria

The current highly competitive environment has driven industries to operate with increasingly restricted profit margins. Thus, it is imperative to optimize production processes. Faced with this scenario, multivariable predictive control of processes has been presented as a powerful alternative to achieve these goals. Moreover, the rationale for implementation of advanced control and subsequent analysis of its post-match performance also focus on the benefits that this tool brings to the plant. It is therefore essential to establish a methodology for analysis, based on clear and measurable criteria. Currently, there are different methodologies available in the market to assist with such analysis. These tools can have a quantitative or qualitative focus. The aim of this study is to evaluate three of the best current main performance assessment technologies： Minimum Variance Control-Harris Index; Statistical Process Control （Cp and Cpk）; and the Qin and Yu Index. These indexes were studied for an alumina plant controlled by three MPC （model predictive control） algorithms （GPC （generalized predictive control）, RMPCT （robust multivariable predictive control technology） and ESSMPC （extended state space model predictive controller）） with different results.

一类线性随机系统的DOBC与GMVC复合控制方法

为提高控制精度,针对一类带有附加干扰的线性随机系统,提出了一种基于干扰观测器的复合分层控制方法。假定位于输入通道的干扰可由一个带有摄动的外部动态方程来描述。基于最小方差准则,就系统状态可量测和系统状态不可量测两种情形来分别设计干扰观测器,并且可以依据分离原理,将观测器设计与控制器设计分解成两个独立的步骤先后进行。通过将基于干扰观测器的控制方法(DOBC)与广义最小方差控制(GMVC)方法相结合,不仅能够抵消可建模干扰,而且能够提高系统的动态性能。最后,仿真算例验证了该方法的有效性。

Innovations twostage dual control

A novel dual control method is proposed for the stochastic systems with unknown parameters, which converts the unsolvable dynamic programming problem into a tractable twostep ahead minimum variance control problem in a stochastic suboptimal view. Innovation variance is used to improve the learning effect, and the instant weight is introduced to reduce the influence of the future output estimation error on the system. Simulation results show the satisfactory performance of the new controller.

Study on Adaptive Control with Neural Network Compensation

A scheme of adaptive control based on a recurrent neural network with a neural network compensation is presented for a class of nonlinear systems with a nonlinear prefix. The recurrent neural network is used to identify the unknown nonlinear part and compensate the difference between the real output and the identified model output. The identified model of the controlled object consists of a linear model and the neural network. The generalized minimum variance control method is used to identify parameters, which can deal with the problem of adaptive control of systems with unknown nonlinear part, which can not be controlled by traditional methods. Simulation results show that this algorithm has higher precision, faster convergent speed.

Robust adaptive control for dynamic systems with mixed uncertainties

The control problem for single-input single-output（SISO） systems in the presence of mixed uncertainties, both stochastic and deterministic uncertainties, is considered. The stochastic uncertainties are modeled as exogenous noises, while the deterministic uncertainties are time invariant and appear as the unknown parameters which lie in a bounded interval. Based on a subdivision for the continuous interval, a robust adaptive controller is designed. The controller can not only realize the system output to track the desired output, but also learn a more accurate interval which contains the true value of the unknown parameter with a learning error given in advance. An example is given finally to demonstrate the effectiveness of the proposed method.

Design of Weigh Feeder Control System for Energy Saving Using Performance-Adaptive Method

This paper discusses a design method for the control system of a weigh feeder that supplies powder and granular material at a constant rate. Most weigh feeders employed in industry are controlled by proportional and integral （PI） compensation, and the control performance is decided by the selection of parameters. To attain advanced control performance by PI control, the PI parameters are designed on the basis of generalized minimum variance control （GMVC）. In this study, to achieve user-specified control performance by GMVC-based PI control, the design parameters of GMVC are automatically adjusted using a performance-adaptive method. The control performance discussed in this study consists of the variance of the control error and that of the difference in the control input. In a conventional performance-adaptive method, the variance of the control error is reduced. In this study, to reduce energy consumption and to achieve user-specified control performance, the variance of the difference in the control input is specified and the design parameter is determined. To demonstrate its effectiveness, the proposed method is applied to an actual weigh feeder.

基于特征模型的高速列车自适应误差补偿控制

针对高速列车运行过程中因不确定运行阻力和模型误差等因素产生的系统误差,提出了新的基于特征模型的高速列车自适应误差补偿控制策略,实现了其对给定目标速度曲线的渐近跟踪。首先通过动力学分析,基于特征建模方法和参数辨识,建立了存在系统误差的高速列车特征模型;其次,利用扩张状态观测器对系统误差的估计能力,设计了基于特征模型的高速列车自适应误差补偿控制器,并结合广义最小方差方法对控制器参数进行了优化,使其在存在系统误差时仍能实现对给定速度曲线的渐近跟踪。该控制策略能够有效处理系统误差带来的不确定性,提高控制精度,从而保障高速列车的安全可靠运行。为了验证本文所提方法的有效性,以CRH380A型高速列车为被控对象进行仿真实验。仿真结果表明设计的补偿控制方法在列车存在未知系统误差的情况下仍能保证理想的控制性能。

数据驱动的广义最小方差控制器设计

针对广义最小方差控制器的实际应用严重依赖过程模型,提出一种数据驱动的广义最小方差控制器设计方法。首先,在一个通用结构下定义用于数据驱动的控制器设计准则函数,并通过梯度实验求得广义输出信号的梯度值。然后,采用高斯-牛顿算法对控制器参数进行迭代寻优,得到广义最小方差性能指标下的最优控制器参数,从而实现数据驱动的广义最小方差控制器设计。最后,以连续搅拌釜式加热器中的温度控制回路为控制对象进行仿真,仿真结果验证了所提方法的有效性。

控制系统性能评估技术发展综述

对控制性能评估(CPA)技术的研究已有近三十年。因CPA在保证控制系统高效、稳定运行方面的显著特点,一直受到学术和工业界的关注。特别是随着工厂所用控制系统的升级和完善,工业过程数据的存储和利用更为方便。这些丰富且包含大量价值的数据成为推动CPA技术发展的重要基础。首先,从控制回路中性能下降的原因分析出发,阐述了性能评估的主要任务和作用。然后,对CPA领域发展比较完善的评估部分,整理综述了多种性能评估指标的研究现状。最后,为便于对该领域感兴趣的研究者入手研究,简要介绍了四种用于CPA的公开数据库,并对CPA技术的发展趋势进行了展望。