Probabilistic message passing control and FPD based decentralised control for stochastic complex systems

This paper offers a novel decentralised control strategy for a class of linear stochastic largescale complex systems. The proposed control strategy is developed to address the main challenges in controlling complex systems such as high dimensionality, stochasticity, uncertainties, and unknown system parameters. To overcome a wide range of domain of complex systems, the proposed strategy decomposes the complex system into several subsystems and controls the system in a decentralised manner. The global control objective is achieved by individually controlling all the local subsystems and then exchanging information between subsystems about their state values. This paper mainly focuses on the probabilistic communication between subsystems, therefore the detailed process of message-passing probabilistic framework is provided. For each subsystem, the regulation problem is considered, and fully probabilistic design (FPD) is applied to take the stochastic nature of complex systems into consideration. Also, since the governing equations of the system dynamics are assumed to be unknown, linear optimisation methods are employed to estimate the parameters of the subsystems. To demonstrate the effectiveness of the proposed control framework, a numerical example is given.

[1]  Qichun Zhang,et al.  An introductory survey of probability density function control , 2019, Systems Science & Control Engineering.

[2]  M. Mézard,et al.  Spin Glass Theory And Beyond: An Introduction To The Replica Method And Its Applications , 1986 .

[3]  Miroslav Kárný,et al.  Fully probabilistic control design in an adaptive critic framework , 2011, Neural Networks.

[4]  Robert G. Gallager,et al.  Low-density parity-check codes , 1962, IRE Trans. Inf. Theory.

[5]  Hong Wang,et al.  EKF-Based Enhanced Performance Controller Design for Nonlinear Stochastic Systems , 2018, IEEE Transactions on Automatic Control.

[6]  Maurizio Porfiri,et al.  Criteria for global pinning-controllability of complex networks , 2008, Autom..

[7]  Qichun Zhang,et al.  Decoupling control in statistical sense: minimised mutual information algorithm , 2016 .

[8]  Wenxue Li,et al.  Almost sure exponential stabilization of hybrid stochastic coupled systems via intermittent noises: A higher-order nonlinear growth condition , 2020 .

[9]  Qichun Zhang,et al.  Output Feedback Stabilization for MIMO Semi-linear Stochastic Systems with Transient Optimisation , 2020, Int. J. Autom. Comput..

[10]  A. Bemporad,et al.  Decentralized model predictive control of constrained linear systems , 2007, 2007 European Control Conference (ECC).

[11]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[12]  Randa Herzallah,et al.  Improved robust control of nonlinear stochastic systems using uncertain models , 2002 .

[13]  Tatiana V. Guy,et al.  Fully probabilistic control design , 2006, Syst. Control. Lett..

[14]  Miroslav Kárný,et al.  Towards probabilistic synchronisation of local controllers , 2017, Int. J. Syst. Sci..

[15]  Randa Herzallah,et al.  Fully Probabilistic Design for Stochastic Discrete System with Multiplicative Noise , 2019, 2019 IEEE 15th International Conference on Control and Automation (ICCA).

[16]  Miroslav Kárný,et al.  Towards fully probabilistic control design , 1996, Autom..

[17]  M. Fornasier,et al.  Mean-Field Optimal Control , 2013, 1306.5913.

[18]  Randa Herzallah,et al.  DOBC Based Fully Probability Design for Stochastic System With the Multiplicative Noise , 2020, IEEE Access.

[19]  Randa Herzallah,et al.  Enhancing the performance of intelligent control systems in the face of higher levels of complexity and uncertainty , 2011, Int. J. Model. Identif. Control..

[20]  Hilbert J. Kappen,et al.  Graphical Model Inference in Optimal Control of Stochastic Multi-Agent Systems , 2008, J. Artif. Intell. Res..

[21]  A. Hasman,et al.  Probabilistic reasoning in intelligent systems: Networks of plausible inference , 1991 .

[22]  Randa Herzallah,et al.  Distribution Modeling of Nonlinear Inverse Controllers Under a Bayesian Framework , 2007, IEEE Transactions on Neural Networks.

[23]  Randa Herzallah Generalised Probabilistic Control Design for Uncertain Stochastic Control Systems , 2018 .

[24]  Tianyou Chai,et al.  Minimized coupling in probability sense for a class of multivariate dynamic stochastic control systems , 2015, 2015 54th IEEE Conference on Decision and Control (CDC).

[25]  Václav Peterka,et al.  Bayesian system identification , 1979, Autom..

[26]  Zidong Wang,et al.  Distributed estimation and control for general systems , 2014, Int. J. Gen. Syst..

[27]  Randa Herzallah,et al.  Robust control of nonlinear stochastic systems by modelling conditional distributions of control signals , 2003, Neural Computing & Applications.

[28]  Randa Herzallah Probabilistic DHP adaptive critic for nonlinear stochastic control systems , 2013, Neural Networks.

[29]  Robert J. McEliece,et al.  The generalized distributive law , 2000, IEEE Trans. Inf. Theory.

[30]  Yuyang Zhou,et al.  Dynamic performance enhancement for nonlinear stochastic systems using RBF driven nonlinear compensation with extended Kalman filter , 2020, Autom..

[31]  Wenxue Li,et al.  FINITE-TIME SYNCHRONIZATION FOR COUPLED SYSTEMS WITH TIME DELAY AND STOCHASTIC DISTURBANCE UNDER FEEDBACK CONTROL , 2020 .