Statistic Tracking Control for Non-Gaussian Systems Using T-S Fuzzy Model

Yi, Yang; Li, Tao

doi:10.3182/20080706-5-kr-1001.01960

Cited by 5 publications

(2 citation statements)

References 14 publications

(5 reference statements)

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“…For measurable output PDFs, the B-spline approach is used to re-express the measured output PDF and the system dynamics is described by a set of differential or difference equations which link the B-spline weighting vector to the control input (see e.g. [59]) and Equation (5) should be considered to further formulate the relationship of the system output PDF and the control input even if the PDF is unmensurable. [60] presented a recursive optimization solution based on MEE control which guaranteed the local stability for the closed-loop system.…”

Section: Extensions: Minimum Entropy Control Filtering Fault Diamentioning

confidence: 99%

A Survey of the Probability Density Function Control for Stochastic Dynamic Systems

Ren

Zhang

2018

2018 24th International Conference on Automation and Computing (ICAC)

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Probability density function (PDF) control strategy investigates the controller design approaches in order to to realise a desirable distributions shape control of the random variables for the stochastic processes. Different from the existing stochastic optimisation and control methods, the most important problem of PDF control is to establish the evolution of the PDF expressions of the system variables. Once the relationship between the control input and the output PDF is formulated, the control objective can be described as obtaining the control input signals which would adjust the system output PDFs to follow the pre-specified target PDFs. This paper summarises the recent research results of the PDF control while the controller design approaches can be categorised into three groups: 1) system model-based direct evolution PDF control; 2) model-based distribution-transformation PDF control methods and 3) databased PDF control. In addition, minimum entropy control, PDFbased filter design, fault diagnosis and probabilistic decoupling design are also introduced briefly as extended applications in theory sense.

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Section: Extensions: Minimum Entropy Control Filtering Fault Diamentioning

confidence: 99%

A Survey of the Probability Density Function Control for Stochastic Dynamic Systems

Ren

Zhang

2018

2018 24th International Conference on Automation and Computing (ICAC)

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“…Furthermore, a multiobjective control problem is studied for the more complex T-S fuzzy weight model involving non-zero equilibriums, multi-delays, parametric uncertainties, state constraints and exogenous disturbances. For such complex T-S fuzzy model, instead of PI control input [4,11] and adaptive NN control [9] , a generalized PID controller [3,10,21,22] can be obtained through solving improved convex LMI algorithms such that the stability, tracking performance, robustness and state constraint are guaranteed simultaneously. Also, in order to enhance the robustness, the peak-to-peak measure is applied to optimize the tracking performance, thus generalizing the corresponding result on linear systems with zero equilibrium [23] .…”

Section: Introductionmentioning

confidence: 99%

Multi-objective PID control for non-Gaussian stochastic distribution system based on two-step intelligent models

Zhang

2009

Sci. China Ser. F-Inf. Sci.

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A new method for controlling the shape of the conditional output probability density function (PDF) for general nonlinear dynamic stochastic systems is proposed based on B-spline neural network (NN) model and T-S fuzzy model. Applying NN approximation to the measured PDFs, we transform the concerned problem into the tracking of given weights. Meanwhile, the complex multi-delay T-S fuzzy model with exogenous disturbances, parametric uncertainties and state constraints is used to represent the nonlinear weight dynamics. Moreover, instead of the non-convex design algorithms and PI control, the improved convex linear matrix inequality (LMI) algorithms and the generalized PID controller are proposed such that the multiple control objectives including stability, robustness, tracking performance and state constraint can be guaranteed simultaneously. Simulations are performed to demonstrate the efficiency of the proposed approach.probability density function, non-Gaussian stochastic systems, PID controller, B-spline NN, L 1 performance index, T-S fuzzy model

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Anti-windup TS Fuzzy PI-like Control for Discrete-Time Nonlinear Systems with Saturated Actuators

Lopes

Leite

Silva

et al. 2020

Int. J. Fuzzy Syst.

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Statistic Tracking Control for Non-Gaussian Systems Using T-S Fuzzy Model

Cited by 5 publications

References 14 publications

A Survey of the Probability Density Function Control for Stochastic Dynamic Systems

A Survey of the Probability Density Function Control for Stochastic Dynamic Systems

Multi-objective PID control for non-Gaussian stochastic distribution system based on two-step intelligent models

Anti-windup TS Fuzzy PI-like Control for Discrete-Time Nonlinear Systems with Saturated Actuators

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