Benchmark problems for nonlinear system identification and control using Soft Computing methods: Need and overview

Summary In this paper, a distributed model predictive control is proposed to control Lipschitz nonlinear systems. The cooperative distributed scheme is considered where a global infinite horizon objective function is optimized for each subsystem, exploiting the state and input information of other subsystems. Thus, each control law is obtained separately as a state feedback of all system's states by solving a set of linear matrix inequalities. Due to convexity of the design, convergence properties at each iteration are established. Additionally, the proposed algorithm is modified to optimize only one control input at a time, which leads to a further reduction in the computation load. Finally, two application cases are studied to show the effectiveness of the proposed method.

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“…The nonlinear model of a Klatt‐Engell reactor is as follows:

\begin{matrix} lefttrue \\ {\overset{c}{true}}_{A} & = - k_{1} (T) c_{A} - k_{2} (T) c_{A}^{2} + (c_{italicin} - c_{A}) u_{1}, \\ {\overset{c}{true}}_{B} & = k_{1} (T) (c_{A} - c_{B}) - c_{B} u_{1}, \\ \dot{T} & = h (c_{A}, c_{B}, T) + α (T_{c} - T) + (T_{italicin} - T) u_{1}, \\ {\overset{T}{true}}_{c} & = β (T - T_{c}) + γ u_{2}, \end{matrix}

where

h (() c_{A}, c_{B}, T) = - δ ((), k_{1} ((), T) ((), c_{A} normalΔ H_{AB} + c_{B} normalΔ H_{BC}) + k_{2} ((), T) c_{A}^{2} normalΔ H_{AD})

. c A and c B are the concentration of A and B , respectively.…”

Section: Numerical Simulationmentioning

confidence: 99%

LMI‐based cooperative distributed model predictive control for Lipschitz nonlinear systems

Adelipour

Haeri

2019

Optim Control Appl Methods

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“…The normalised root mean squared error J NRMSE y;ŷ À Á (see [18]) is employed as assessment criteria:…”

Section: Simulation Designmentioning

confidence: 99%

Effective wind speed estimation: Comparison between Kalman Filter and Takagi–Sugeno observer techniques

et al. 2016

Self Cite

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“…Let us consider four models of discrete time nonlinear dynamical systems [24] for single input single output (SISO) and multiple input and single output (MISO) system considered in this paper and they are described by difference equations (1)–(4) and Box-Jenkins time series data [10]. Let f : R n → R and g : R m → R be the nonlinear continuous differentiable function of Model I–Model IV approximated by FCPN to the desired degree of accuracy.…”

Section: Problem Formulationmentioning

confidence: 99%

“…Neural network has been widely used for nonlinear dynamical systems [3–5]. Various control systems like back stepping control [6, 7], sliding mode control [8, 9], and control using soft computing [10] are continuous source of interest. The nonlinear dynamical system is a generic problem which finds its application in every field of engineering.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Counter Propagation Neural Network Control for a Class of Nonlinear Dynamical Systems

Sakhre

Jain

Sapkal

et al. 2015

Computational Intelligence and Neuroscience

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Fuzzy Counter Propagation Neural Network (FCPN) controller design is developed, for a class of nonlinear dynamical systems. In this process, the weight connecting between the instar and outstar, that is, input-hidden and hidden-output layer, respectively, is adjusted by using Fuzzy Competitive Learning (FCL). FCL paradigm adopts the principle of learning, which is used to calculate Best Matched Node (BMN) which is proposed. This strategy offers a robust control of nonlinear dynamical systems. FCPN is compared with the existing network like Dynamic Network (DN) and Back Propagation Network (BPN) on the basis of Mean Absolute Error (MAE), Mean Square Error (MSE), Best Fit Rate (BFR), and so forth. It envisages that the proposed FCPN gives better results than DN and BPN. The effectiveness of the proposed FCPN algorithms is demonstrated through simulations of four nonlinear dynamical systems and multiple input and single output (MISO) and a single input and single output (SISO) gas furnace Box-Jenkins time series data.

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Benchmark problems for nonlinear system identification and control using Soft Computing methods: Need and overview

Cited by 71 publications

References 45 publications

LMI‐based cooperative distributed model predictive control for Lipschitz nonlinear systems

LMI‐based cooperative distributed model predictive control for Lipschitz nonlinear systems

Effective wind speed estimation: Comparison between Kalman Filter and Takagi–Sugeno observer techniques

Fuzzy Counter Propagation Neural Network Control for a Class of Nonlinear Dynamical Systems

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