Skip to main content
Log in

Shaping the PDF of the state variable based on piecewise linear control for non-linear stochastic systems

非线性随机系统中基于分段线性控制的PDF形状控制研究

  • Research Paper
  • Published:
Science China Information Sciences Aims and scope Submit manuscript

Abstract

In this paper, we propose a shape-control scheme for non-linear stochastic dynamical systems to enable us to shape the probability density function (PDF) of the state variable. First, we derive the PDF analytical expression using the Fokker-Planck-Kolmogorov (FPK) equation obtained from stochastic systems. Then, we control the PDF shape by devising a piecewise linear control law whose parameters are calculated using the conjugated gradient method. Finally, we perform contrast simulation experiments to validate the effectiveness and superiority of the proposed algorithm.

概要

创新点

与传统的均值和方差控制方法相比, 本文提出了一种状态变量的概率密度函数(Probability Density Function, 简称PDF)形状控制方法。我们的研究对象是非线性随机系统, 并且用PDF形状逼近作为性能指标。在过去的PDF形状控制研究中, 由于多项式的灵活性和连续性, 我们一直采用多项式形式作为控制规律, 但这明显增加了计算量和算法的复杂度. 本文设计了一个分段线性控制规律实现PDF形状的控制, 线性控制规律只包含两个系数和一个分段点, 在一定范围内呈现线性特性, 这使得整个算法变得更加简单, 在程序实现时节省运行时间. 线性分段控制尽管已经在文献[22]中研究过, 但本文有三点不同之处: 1、文献中的控制目标是没有考虑测量噪声的的输出PDF, 而本文的控制目标是状态变量的PDF; 2、文献中地稳态PDF是近似的, 而我们解出了一个精确的稳态PDF; 3、尽管我们采用相同的控制结构, 但获取控制器增益的方法是不同的, 我们采用的是共轭梯度法得到了控制器的增益. 对比实验结果表明, 所提出的分段线性控制方法可以有效地控制状态变量的PDF形状, 并且当期望的PDF形状是不对称双峰时, 其控制效果要优于非线性多项式控制. 同时, 不管期望的PDF形状如何, 线性控制方法的运行时间都要比非线性控制方法的短. 仿真实例中的非线性系统是一个强非线性系统, 这说明所提出的控制算法对强非线性系统是有效的.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Li D, Qian F C, Fu P L. Variance minimization approach for a class of dual control problems. IEEE Trans Automat Control, 2002, 47: 2010–2020

    Article  MathSciNet  Google Scholar 

  2. Yue H, Wang H. Minimum entropy control of closed-loop tracking errors for dynamic stochastic systems. IEEE Trans Automat Control, 2003, 48: 118–122

    Article  MathSciNet  Google Scholar 

  3. Li D, Qian F C, Fu P L. Optimal nominal dual control for discrete-time LQG problem with unknown parameters. Automatica, 2008, 44: 119–127

    Article  MathSciNet  MATH  Google Scholar 

  4. Li D, Qian F C, Gao J J. Performance-first control for discrete-time LQG problems. IEEE Trans Automat Control, 2009, 54: 2225–2230

    Article  MathSciNet  Google Scholar 

  5. Qian F C, Xie G, Liu D. Nonlinear optimal trade-off control for LQG problem. In: Proceedings of American Control Conference, Baltimore, 2010. 5: 1931–1936

    Google Scholar 

  6. Qian F C, Xie G, Liu D. Optimal control of LQG problem with an explicit trade-off between mean and variance. Int J Syst Sci, 2011, 42: 1957–1964

    Article  MathSciNet  MATH  Google Scholar 

  7. Qian F C, Gao J J, Li D. Complete statistical characterization of discrete-time LQG and cumulant control. IEEE Trans Automat Control, 2012, 57: 2110–2115

    Article  MathSciNet  Google Scholar 

  8. Sain M K. Control of linear systems according to the minimal variance criterion: a new approach to the disturbance problem. IEEE Trans Automat Control, 1966, 11: 118–122

    Article  Google Scholar 

  9. Sain M K, Liberty S R. Performance measure densities for a class of LQG control systems. IEEE Trans Automat Control, 1971, 16: 431–439

    Article  MathSciNet  Google Scholar 

  10. Li D, Qian F C. Closed-loop optimal control law for discrete time LQG problems with a mean-variance objective. In: Proceedings of the 43rd IEEE Conference on Decision and Control, Paradise Island, 2004. 3: 2291–2296

    Google Scholar 

  11. Li D, Qian F C, Fu P. Mean-variance control for discrete time LQG problems. In: Proceedings of the American Control Conference, Denver, 2003. 5: 4444–4449

    Google Scholar 

  12. Jacobson D H. Optimal stochastic linear systems with exponential performance criteria and their relation to deterministic differential games. IEEE Trans Automat Control, 1973, 18: 124–131

    Article  MathSciNet  MATH  Google Scholar 

  13. Liberty S R, Hartwig R C. On the essential quadratic nature of LQG control-performance measure cumulants. Inf Control, 1976, 32: 276–305

    Article  MathSciNet  MATH  Google Scholar 

  14. Whittle P. Risk-sensitive Optimal Control. New York: Wiley, 1990

    MATH  Google Scholar 

  15. Wang H, Zhang J H. Bounded stochastic distributions control for pseudo-ARMAX stochastic systems. IEEE Trans Automat Control, 2001, 46: 486–490

    Article  MathSciNet  MATH  Google Scholar 

  16. Wang H. Minimum entropy control of non-Gaussian dynamic stochastic systems. IEEE Trans Automat Control, 2002, 47: 398–403

    Article  MathSciNet  Google Scholar 

  17. Guo L, Wang H. Fault detection and diagnosis for general stochastic systems using B-spline expansions and nonlinear observers. In: Proceedings of the 43rd IEEE Conference on Decision and Control, Paradise Island, 2004. 5: 4782–4787

    Google Scholar 

  18. Guo L, Wang H. PID controller design for output PDFs of stochastic systems using linear matrix inequalities. IEEE Trans Syst Man Cybern Part B-Cybern, 2004, 35: 65–71

    Article  Google Scholar 

  19. Forbes M G, Guay M, Forbes J F. Control design for first-order processes: shaping the probability density of the process state. J Process Control, 2004, 14: 399–410

    Article  Google Scholar 

  20. Guo L, Wang H, Wang A P. Optimal probability density function control for NARMAX stochastic systems. Automatica, 2008, 44: 1904–1911

    Article  MathSciNet  MATH  Google Scholar 

  21. Zhu C X, Zhu W Q. Feedback control of nonlinear stochastic systems for targeting a specified stationary probability density. Automatica, 2011, 47: 539–544

    Article  MathSciNet  MATH  Google Scholar 

  22. Pigeon B, Perrier M, Srinivasan B. Shaping probability density functions using a switching linear controller. J Process Control, 2011, 21: 901–908

    Article  Google Scholar 

  23. Zhang J F, Yue H, Zhou J L. Predictive PDF control in shaping of molecular weight distribution based on a new modeling algorithm. J Process Control, 2015, 30: 80–89

    Article  Google Scholar 

  24. Wang L Z, Qian F C, Liu J. Shape control on probability density function in stochastic systems. J Syst Eng Electron, 2014, 25: 144–149

    Article  Google Scholar 

  25. Wang L Z, Qian F C, Liu J. The PDF shape control of the state variable for a class of stochastic systems. Int J Syst Sci, 2013, 46: 1–9

    Article  MathSciNet  Google Scholar 

  26. Xu W, Du L, Xu Y. Some recent developments of nonlinear stochastic dynamics (in Chinese). Chin J Eng Math, 2006, 26: 951–960

    MathSciNet  MATH  Google Scholar 

  27. Crespo L G, Sun J Q. Nonlinear control via stationary probability density functions. In: Proceedings of the American Control Conference, Anchorage, 2002. 2: 2029–2034

    Google Scholar 

  28. Rong H W, Meng G, Wang X D, et al. Approximation solution of FPK equations (in Chinese). Chin J Appl Mech, 2003, 20: 95–98

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Fucai Qian.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, L., Qian, F. Shaping the PDF of the state variable based on piecewise linear control for non-linear stochastic systems. Sci. China Inf. Sci. 59, 112207 (2016). https://doi.org/10.1007/s11432-015-0401-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11432-015-0401-9

Keywords

关键词

Navigation