Data-driven controller design for general MIMO nonlinear systems via virtual reference feedback tuning and neural networks

Yan, Pengfei; Liu, Derong; Wang, Ding; Ma, Hongwen

doi:10.1016/j.neucom.2015.07.017

Cited by 74 publications

(46 citation statements)

References 48 publications

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“…The goal of VRFT is to automatically tune the parameters of a linear or nonlinear controller such that the closed-loop CS tracks a reference model [30,31]. With this respect, only the I/O data…”

Section: Overview On Nonlinear Vrftmentioning

confidence: 99%

Virtual Reference Feedback Tuning of Model-Free Control Algorithms for Servo Systems

et al. 2017

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This paper proposes the combination of two data-driven techniques, namely virtual reference feedback tuning (VRFT) and model-Free Control (MFC) in terms of the VRFT of MFC algorithms dedicated to servo systems. VRFT ensures the automatic optimal computation of the parameters of three MFC algorithms represented by intelligent proportional (iP), intelligent proportional-integral (iPI), and intelligent proportional-integral-derivative (iPID) controllers. The combination of MFC and VRFT leads to a novel mixed MFC-VRFT approach. The approach is validated by experimental results related to the angular speed control of modular servo system laboratory equipment. The performance of the control systems with the MFC algorithms (iP, iPI, and iPID controllers) tuned by the mixed MFC-VRFT approach is compared with that of control systems with MFC algorithms tuned by a metaheuristics gravitational search algorithm (GSA) optimizer, and of control systems with I, PI and PID controllers optimally tuned by VRFT and GSA in the same optimization problem.

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Section: Overview On Nonlinear Vrftmentioning

confidence: 99%

Virtual Reference Feedback Tuning of Model-Free Control Algorithms for Servo Systems

et al. 2017

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“…Nonlinear VRFT uses only a single openloop experiment, where a rich spectrum frequency signal is applied as input to the stable nonlinear process, then the I/O signals are collected, and then used to compute the controller parameters [3,4,19].…”

Section: Nonlinear Vrftmentioning

confidence: 99%

“…is the tracking error, ) (k r is the reference input vector applied to the closed-loop CS, According to [19], in MIMO VRFT there is no need for any time-varying filter to make ) (θ VRFT. The two o.f.s can be made approximately equal for a rich parameterization of the controller, which can be, for example, a neural network [19,20].…”

Section: Nonlinear Vrftmentioning

confidence: 99%

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Data-driven Model-Free Adaptive Control Tuned by Virtual Reference

2016

APH

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This paper proposes a new tuning approach, by which, all parameters of a datadriven Model-Free Adaptive Control (MFAC) algorithm are automatically determined using a nonlinear Virtual Reference Feedback Tuning (VRFT) algorithm. The approach is referred to as mixed MFAC-VFRT control and it leads to mixed MFAC-VFRT algorithms. An advantage of mixed MFAC-VFRT control, is that it combines systematically, the features of VRFT (it computes the controller parameters using only the input/output data) with those of MFAC. This is especially illustrated by comparison with the classical MFAC algorithms, the initial values of the parameters, which are obtained through a process that involves solving an optimization problem. The application that validates the mixed MFAC-

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“…DDC approaches do not require a model of a plant, and the modeling process, the unmodeled dynamics, and the theoretical assumptions all disappear [16][17][18][19]. Therefore, DDC has attracted considerable attention in recent years [18], and there are many DDC approaches together with their practical applications in many fields that could be found in the literature, like the following: modelfree adaptive control (MFAC) [16,17,[20][21][22][23][24], data-driven optimal iterative learning control (DDOILC) [25][26][27], virtual reference feedback tuning [28][29][30], lazy learning control [31], dynamic programming methods [32], and others [33][34][35][36]. In spite of this, to the best of authors' knowledge, there is no report about the research on application of DDC to SFBGP that has been published in the literature.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Model-Free Adaptive Control of Particle Quality in Drug Development Phase of Spray Fluidized-Bed Granulation Process

et al. 2017

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A novel data-driven model-free adaptive control (DDMFAC) approach is first proposed by combining the advantages of modelfree adaptive control (MFAC) and data-driven optimal iterative learning control (DDOILC), and then its stability and convergence analysis is given to prove algorithm stability and asymptotical convergence of tracking error. Besides, the parameters of presented approach are adaptively adjusted with fuzzy logic to determine the occupied proportions of MFAC and DDOILC according to their different control performances in different control stages. Lastly, the proposed fuzzy DDMFAC (FDDMFAC) approach is applied to the control of particle quality in drug development phase of spray fluidized-bed granulation process (SFBGP), and its control effect is compared with MFAC and DDOILC and their fuzzy forms, in which the parameters of MFAC and DDOILC are adaptively adjusted with fuzzy logic. The effectiveness of the presented FDDMFAC approach is verified by a series of simulations.

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Data-driven controller design for general MIMO nonlinear systems via virtual reference feedback tuning and neural networks

Cited by 74 publications

References 48 publications

Virtual Reference Feedback Tuning of Model-Free Control Algorithms for Servo Systems

Virtual Reference Feedback Tuning of Model-Free Control Algorithms for Servo Systems

Data-driven Model-Free Adaptive Control Tuned by Virtual Reference

Data-Driven Model-Free Adaptive Control of Particle Quality in Drug Development Phase of Spray Fluidized-Bed Granulation Process

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