Learning Stable Robust Adaptive NARMA Controller for UAV and Its Application to Twin Rotor MIMO Systems

Bulucu, Parvın; Soydemir, Mehmet Uğur; Şahin, Savaş; Kocaoğlu, Aykut; Güzeliş, Cüneyt

doi:10.1007/s11063-020-10265-0

Cited by 6 publications

(3 citation statements)

References 23 publications

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“…Some evaluation indicators are used to check the usefulness of the four estimators, as listed in Table 1. e mean squared error (MSE) and mean of prediction error (PEM) in reference [13], integral square error (ISE), and integral absolute error (IAE) [17] are chosen as evaluation indicators.…”

Section: Simulation Examplementioning

confidence: 99%

“…e deterministic method includes recursive structure [9], iterative structure [10], multi-innovation identi cation [11], subspace identi cation [12], two-stage identi cation [13], and experimental tests [14], etc. While nondeterministic method includes the improved PSO [15], hybrid metaheuristic algorithm [16], supervised learning method [17], gravitational search algorithm [18], weighted differential evolution [19], etc. Liu et al [20] investigated a least-squares-like scheme for a Hammerstein-Wiener-like system using periodic input signals.…”

Section: Introductionmentioning

confidence: 99%

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Parameter Identification with a New Recursive Framework for Wiener–Hammerstein-Like System and Its Application

Liang

Zhang

et al. 2022

Mathematical Problems in Engineering

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In the process of actual system modeling, many systems exhibit nonlinear characteristics with memory. Thus, the parameter identification problem of the nonlinear system with memory usually appears in the system modeling. This report focuses on the nonlinear system identification of Wiener–Hammerstein-like model with memory hysteresis, in which a new recursive estimation way is introduced. In this algorithm, the estimation bias problem can be improved by introducing a data filtering technique. On the basis of the filtered data, some auxiliary matrices and vectors are proposed. Following this, the identification error variable is introduced by using auxiliary matrices and vectors with an adaptive forgetting factor. Afterward, the identification error variable is integrated into the design of parameter estimation adaptive law with recursive gain structure. By comparison with the classic estimation methods, the proposed algorithm shows an alternative identification algorithm design angle. In addition, it is strictly proved that the parameter estimation error converges to zero under a general excitation condition. Based on the results of indices M S E , compared with the existing methods, the performance improvements of the proposed method are 33.9 %, 41.26%, and 53.5%, respectively. In terms of indices P E M , the augmented performances of the developed scheme are 50%, 56.2%, and 68.4%, respectively, in comparison to the available schemes.

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Section: Simulation Examplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Parameter Identification with a New Recursive Framework for Wiener–Hammerstein-Like System and Its Application

Liang

Zhang

et al. 2022

Mathematical Problems in Engineering

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“…In relation to adaptive learning, Bulucu et al [135] introduced an on-line adaptive learning algorithm for robust adaptive non-linear auto-regressive moving average (NARMA) control strategy based on Hammerstein-based plant and Wiener-based controller models for twin rotor and quadcopter systems and verified them experimentally on the real TRMS subjected to cross-coupling effect. The results showed the ability of the proposed control scheme in ensuring closed-loop system stability, providing robustness against noise and disturbances, and improving the tracking performance.…”

Section: Quad-rotor Systemsmentioning

confidence: 99%

Control Strategies and Novel Techniques for Autonomous Rotorcraft Unmanned Aerial Vehicles: A Review

2020

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This paper presents a review of the various control strategies that have been conducted to address and resolve several challenges for a particular category of unmanned aerial vehicles (UAVs), the emphasis of which is on the rotorcraft or rotary-wing systems. Initially, a brief overview of the important relevant definitions, configurations, components, advantages/disadvantages, and applications of the UAVs is first introduced in general, encompassing a wide spectrum of the flying machines. Subsequently, the focus is more on the two most common and versatile rotorcraft UAVs, namely, the twin-rotor and quadrotor systems. Starting with a brief background on the dual-rotor helicopter and a quadcopter, the full detailed mathematical dynamic model of each system is derived based on the Euler-Lagrange and Newton-Euler methods, considering a number of assumptions and considerations. Then, a state-of-the-art review of the diverse control strategies for controlling the rotorcraft systems with conceivable solutions when the systems are subjected to the different impediments is demonstrated. To counter some of these limitations and adverse operating/loading conditions in the UAVs, several innovative control techniques are particularly highlighted, and their performance are duly analyzed, discussed, and compared. The applied control techniques are deemed to produce a useful contribution to their successful implementation in the wake of varied constraints and demanding environments that result in a degree of robustness and efficacy. Some of the off-the-shelf developments in the rotorcraft systems for research and commercial applications are also presented.

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Online learning of stable robust adaptive controllers design based on data-dependent feedback linearization with application to rotary inverted pendulum

Soydemir,

Şahin,

Kocaoğlu

et al. 2024

Neural Comput & Applic

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This study introduces an online (supervised) learning method to design nonlinear auto-regressive moving average (NARMA) controllers for feedback-linearized nonlinear single-input single-output (SISO) systems. The algorithm ensures Schur stability of the overall closed-loop system and provides adaptiveness and robustness for the NARMA controllers. The first stage of the method derives, in a data-dependent way, a feedback-linearized model of the nonlinear plant by using its input and output sample pairs. The method’s second stage, which constitutes the novel part of the presented study, builds up an online learning scheme for the linear auto-regressive moving average (ARMA) controller based on an already learned feedback-linearized model of the nonlinear plant. During online supervised learning, ARMA parameters of the feedback-linearized SISO plant model and the closed-loop ARMA model are computed by minimizing the plant identification and the closed-loop system tracking errors. Both errors are defined as $${{\ell}}_{1,{\varvec{\varepsilon}}}$$ ℓ 1 , ε , namely ε-insensitive loss functions that provide NARMA controller the robustness against noise and outliers. The proposed online learning control algorithm is applied to a rotary inverted pendulum model and to a real rotary inverted pendulum setup. The tracking performance of the developed controller is compared with those of the linear quadratic regulator and coupled sliding mode controller in terms of mean square error.

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Learning Stable Robust Adaptive NARMA Controller for UAV and Its Application to Twin Rotor MIMO Systems

Cited by 6 publications

References 23 publications

Parameter Identification with a New Recursive Framework for Wiener–Hammerstein-Like System and Its Application

Parameter Identification with a New Recursive Framework for Wiener–Hammerstein-Like System and Its Application

Control Strategies and Novel Techniques for Autonomous Rotorcraft Unmanned Aerial Vehicles: A Review

Online learning of stable robust adaptive controllers design based on data-dependent feedback linearization with application to rotary inverted pendulum

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