Two data-driven control algorithms for a MIMO aerodynamic system with experimental validation

Roman, Raul‐Cristian; Rădac, Mircea-Bogdan; Precup, Radu‐Emil; Stînean, Alexandra-Iulia

doi:10.1109/icstcc.2015.7321381

Cited by 6 publications

(2 citation statements)

References 25 publications

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“…Over the recent years, various computational approaches, such as finite element, finite difference time domain, finite-difference frequency-domain, and Lyapunov stability analysis have been successfully applied to a variety of control systems, including photonic crystals [ 73 , 74 , 75 , 76 , 77 , 78 ], high-frequency passive components [ 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 ], electromagnetic devices [ 88 , 89 , 90 , 91 , 92 , 93 , 94 ], fluid mechanical engineering [ 95 , 96 ], and MIMO control systems [ 97 , 98 ]. Simulation studies using MATLAB are carried out to evaluate the performances of the proposed MANNC in tracking setpoints, reducing unwanted overshoots or undershoots, and securing the global stability of a closed-loop system during the system’s entire control process.…”

Section: Simulation Resultsmentioning

confidence: 99%

Introducing a Novel Model-Free Multivariable Adaptive Neural Network Controller for Square MIMO Systems

Mehrafrooz

Lalbakhsh

2022

Sensors

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In this study, a novel Multivariable Adaptive Neural Network Controller (MANNC) is developed for coupled model-free n-input n-output systems. The learning algorithm of the proposed controller does not rely on the model of a system and uses only the history of the system inputs and outputs. The system is considered as a ‘black box’ with no pre-knowledge of its internal structure. By online monitoring and possessing the system inputs and outputs, the parameters of the controller are adjusted. Using the accumulated gradient of the system error along with the Lyapunov stability analysis, the weights’ adjustment convergence of the controller can be observed, and an optimal training number of the controller can be selected. The Lyapunov stability of the system is checked during the entire weight training process to enable the controller to handle any possible nonlinearities of the system. The effectiveness of the MANNC in controlling nonlinear square multiple-input multiple-output (MIMO) systems is demonstrated via three simulation studies covering the cases of a time-invariant nonlinear MIMO system, a time-variant nonlinear MIMO system, and a hybrid MIMO system, respectively. In each case, the performance of the MANNC is compared with that of a properly selected existing counterpart. Simulation results demonstrate that the proposed MANNC is capable of controlling various types of square MIMO systems with much improved performance over its existing counterpart. The unique properties of the MANNC will make it a suitable candidate for many industrial applications.

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Section: Simulation Resultsmentioning

confidence: 99%

Introducing a Novel Model-Free Multivariable Adaptive Neural Network Controller for Square MIMO Systems

Mehrafrooz

Lalbakhsh

2022

Sensors

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“…Unlike [17], the MFC algorithm proposed in this paper has a discrete-time implementation, and it guarantees the CS stability under some assumptions by fulfilling certain constrains. MFC algorithms have also been successfully applied to many processes [28][29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

Multi‐input–multi‐output system experimental validation of model‐free control and virtual reference feedback tuning techniques

Roman

Rădac

Precup

2016

IET Control Theory & Applications

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This study proposes data-driven model-free adaptive control (MFAC), model-free control (MFC) and virtual reference feedback tuning (VRFT) techniques applied to the control of a representative non-linear multi-input-multi-output (MIMO) system represented by the twin rotor aerodynamic system (TRAS). These data-driven techniques are implemented for both a single MIMO controller and two separately designed single-input-single-output controllers running in parallel for azimuth and pitch control. The three techniques are implemented as MFAC and MFC algorithms and as linear controllers tuned by VRFT. The performance of the three data-driven MIMO control system structures is compared systematically on the basis of the experimental results in terms of the values of the sum of mean squared control errors measured on TRAS equipment.

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Neurocontrol Design for an Aerodynamics System: Simple Backpropagation Approach

Norsahperi

Danapalasingam

2019

10th International Conference on Robotics, Vision, Signal Processing and Power Applications

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Two data-driven control algorithms for a MIMO aerodynamic system with experimental validation

Cited by 6 publications

References 25 publications

Introducing a Novel Model-Free Multivariable Adaptive Neural Network Controller for Square MIMO Systems

Introducing a Novel Model-Free Multivariable Adaptive Neural Network Controller for Square MIMO Systems

Multi‐input–multi‐output system experimental validation of model‐free control and virtual reference feedback tuning techniques

Neurocontrol Design for an Aerodynamics System: Simple Backpropagation Approach

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