Identification-based linear control of a twin rotor MIMO system via dynamical neural networks

Armenta, Carlos; Bernal, Miguel; Hernández, Francisco; Villafuerte, R.

doi:10.1109/iceee.2017.8108829

Cited by 5 publications

(3 citation statements)

References 19 publications

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“…However, a drawback of this approach is that it requires process inputs to be persistently exciting. This constraint is inadequate for ill conditioned multivariable processes because in this case model order can be underestimated, leading to subsequent identification of poor models (Misra and Nikolaou, 2003;Haider et al, 2011;Musoff and Zarchan, 2009;DeBitetto, 1989;Ehrman and Lanterman, 2008;Palmer et al, 1996;Woo-han et al, 2006;Armenta et al, 2017;Chandra et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Subspace identification of fault modes for a twin-rotor system

Haider

Huda

Rasool³

et al. 2020

IJIUS

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PurposeThe purpose of this paper is to identify the fault modes of a nonlinear twin-rotor system (TRS) using the subspace technique to facilitate fault identification, diagnosis and control applications.Design/methodology/approachFor identification of fault modes, three types of system malfunctions are introduced. First malfunction resembles actuator, second internal system dynamics and third represents sensor malfunction or offset. For each fault scenario, the resulting TRS model is applied with persistently exciting inputs and corresponding outputs are recorded. The collected input–output data are invoked in NS4SID subspace system identification algorithm to obtain the unknown fault model. The identified actuator fault modes of the TRS can be used for fault diagnostics, fault isolation or fault correction applications.FindingsThe identified models obtained through system identification are validated for correctness by comparing the response of the actual model under the fault condition and identified model. The results certify that the identified fault modes correctly resemble the respective fault conditions in the actual system.Originality/valueThe utilization of proposed work for current research emphasized the area of fault detection, diagnosis and correction applications that makes its value significantly. These modes when used for diagnosis purposes allow users to timely get intimated and rectify the performance degradation of the plant before it gets totally malfunctioned. Moreover, the slight performance degradation is also indicated when fault diagnosis is performed.

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Section: Introductionmentioning

confidence: 99%

Subspace identification of fault modes for a twin-rotor system

Haider

Huda

Rasool³

et al. 2020

IJIUS

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“…Jagannath et al (2017), introduction of PID control for the design of pitch and yaw angle of the system which stabilize the main and tail rotor and give better transient response. Carlos et al (2017), worked on realtime implementation and stabilization scheme and tracking control of pitch and yaw-angle of the system. Vrazevsky et al (2016), proposed their work on PID control with suboptimal controller LQR for good response of the system to achieve the better steady state response.…”

Section: Introductionmentioning

confidence: 99%

Control of Twin Rotor MIMO System using PID and LQR Controller

Chaudhary

Kumar

2019

Journal of Aircraft and Spacecraft Technology

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Control design of a TRMS system such as helicopter is very complicated task. Since the TRMS are very high non-linear system, so its non-linearity too high and the control design is very difficult problem for TRMS system. It has two rotors, the first one is the main rotor and the second one is tail rotor. The TRMS System are based on the beam and support by a counter balance. Horizontal and vertical plane are included in the TRMS System for PID and LQR controller both are discuss in this paper. PID and LQR controller both are discuss in this paper. By Simulink result for vertical and horizontal plane, LQR controller better the PID control. All the simulation work is done by the MATLAB/Simulink of the results, environment and the working of simulation results have been done at the end of this paper.

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“…Once a RHONN is properly trained, controller design for a given purpose (stabilization, trajectory tracking) based on the identification model should be carried out; once the control law structure is found, the plant states are used instead of the RHONN ones as they are assumed to be known. 1 In prior literature, for simplicity, linear control techniques have been applied to RHONN models at the cost of losing important characteristics (Armenta et al 2017); nonlinear control, on the other hand, has been applied via model inversion (Sanchez and Bernal 2000) and sliding modes (Castañeda et al 2013), but these approaches cancel out the RHONN nonlinearities instead of using them appropriately. Recently, a solution for implementing nonlinear control to a RHONN in the form of parallel distributed compensation (PDC) (Wang et al 1996) has appeared in (Armenta et al 2019); a Takagi-Sugeno (TS) model allowing design conditions in the form of linear matrix inequalities (LMIs) (Boyd et al 1994) and sum-of-squares (SOS) (Prajna et al 2004) has been obtained via a suitable transformation of the RHONN.…”

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confidence: 99%

Exact Takagi-Sugeno descriptor models of recurrent high-order neural networks for control applications

et al. 2019

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This work presents an exact Takagi-Sugeno descriptor model of a recurrent high-order neural network arising from identification of a nonlinear plant. The proposed rearrangement allows exploiting the nonlinear characteristics of the neural model for H ∞-optimal controller design whose conditions are expressed as linear matrix inequalities. Simulation and real-time results are presented that illustrate the advantages of the proposal. Keywords Descriptor system • Linear matrix inequality • Takagi-Sugeno model • Recurrent high-order neural network Mathematics Subject Classification 93C10 • 93C95 • 93C42 • 93B36 • 93B30 • 93D15 • 93D05 • 92B20 1 Introduction An accurate control of a nonlinear plant is usually based on the knowledge of a mathematical model of the process. Such representation might be obtained by means of first principles Communicated by Anibal Tavares de Azevedo.

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Identification-based linear control of a twin rotor MIMO system via dynamical neural networks

Cited by 5 publications

References 19 publications

Subspace identification of fault modes for a twin-rotor system

Subspace identification of fault modes for a twin-rotor system

Control of Twin Rotor MIMO System using PID and LQR Controller

Exact Takagi-Sugeno descriptor models of recurrent high-order neural networks for control applications

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