Adaptive neural control for an uncertain fractional‐order rotational mechanical system using disturbance observer

Shao, Shuyi; Chen, Mou; Chen, Shaodong; Wu, Qingxian

doi:10.1049/iet-cta.2015.1054

Cited by 41 publications

(26 citation statements)

References 51 publications

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“…where σ 2 , ρ 2 > 0. According to Based on frequency distributed model (34), (37) and (40). The derivative of (41) iṡ…”

Section: Volume 7 2019mentioning

confidence: 99%

“…In [35] and [36], a output feedback control scheme for a class of triangular commensurate fractional order nonlinear systems is given. For a class of a commensurate fractional order rotational mechanical system with disturbances and uncertainties, a robust adaptive NN control is presented in [37]. Based on dual radial basis function (RBF) NNs, an adaptive fractional sliding mode controller is proposed to enhance the performance of the system in [38].…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Control of a Class of Incommensurate Fractional Order Nonlinear Systems With Input Dead-Zone

2019

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This paper develops a new adaptive control scheme for a class of incommensurate fractional order nonlinear systems with external disturbances and input dead-zone. Based on the backstepping algorithm, the radial basis function neural network (RBF NN) is used to approximate the unknown nonlinear uncertainties in each step of the backstepping, and the fractional order parameters update laws for RBF NN are proposed as well as the fractional order nonlinear disturbance estimator to estimate the external disturbances. The adaptive RBF NN controller is designed based on the frequency distributed model of fractional integrator for the fractional order systems, and the stability of the closed loop system is proved. Finally, three simulation examples are given to verify the effectiveness and robustness of the proposed method. INDEX TERMS Fractional order systems, adaptive backstepping control, nonlinear system, neural networks (NNs), input dead-zone.

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“…where σ 2 , ρ 2 > 0. According to Based on frequency distributed model (34), (37) and (40). The derivative of (41) iṡ…”

Section: Volume 7 2019mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive Control of a Class of Incommensurate Fractional Order Nonlinear Systems With Input Dead-Zone

2019

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“…These controllers not only deal with the uncertainties in ABS, but also track the desired slip faster than conventional integer-order SMC (IOSMC). Additionally, FOSMC has been employed for speed control of permanent magnet synchronous motors, 19,20 for vibration suppression of uncertain structures, 21 for control design of uncertain nonlinear systems, 22,23 for control of fractional-order chaotic systems, 24,25 and so on and achieved better control capacities. To date, most of the research on FOSMC has focused on the fractional-order switching manifold, these FOSMC strategies improve the control capacities of conventional IOSMC by employing fractional-order switching manifold, but they cannot achieve optimal control performance without additional measures.…”

Section: 6mentioning

confidence: 99%

Fractional-order sliding mode control for a class of uncertain nonlinear systems based on LQR

Zhang

Cao

Tang

2017

International Journal of Advanced Robotic Systems

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This article presents a new fractional-order sliding mode control (FOSMC) strategy based on a linear-quadratic regulator (LQR) for a class of uncertain nonlinear systems. First, input/output feedback linearization is used to linearize the nonlinear system and decouple tracking error dynamics. Second, LQR is designed to ensure that the tracking error dynamics converges to the equilibrium point as soon as possible. Based on LQR, a novel fractional-order sliding surface is introduced. Subsequently, the FOSMC is designed to reject system uncertainties and reduce the magnitude of control chattering. Then, the global stability of the closed-loop control system is analytically proved using Lyapunov stability theory. Finally, a typical single-input single-output system and a typical multi-input multi-output system are simulated to illustrate the effectiveness and advantages of the proposed control strategy. The results of the simulation indicate that the proposed control strategy exhibits excellent performance and robustness with system uncertainties. Compared to conventional integer-order sliding mode control, the high-frequency chattering of the control input is drastically depressed.

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“…In [31,32], an output feedback control scheme for a class of triangular fractional order nonlinear systems is given. For a class of a fractional order rotational mechanical system with disturbances and uncertainties, a robust adaptive NN control is presented in [33]. Based on dual radial basis function (RBF) NNs, an adaptive fractional sliding mode controller is proposed to enhance the performance of the system in [34].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Neural Network Control of a Class of Fractional Order Uncertain Nonlinear MIMO Systems with Input Constraints

2019

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An adaptive backstepping control scheme for a class of incommensurate fractional order uncertain nonlinear multiple-input multiple-output (MIMO) systems subjected to constraints is discussed in this paper, which ensures the convergence of tracking errors even with dead-zone and saturation nonlinearities in the controller input. Combined with backstepping and adaptive technique, the unknown nonlinear uncertainties are approximated by the radial basis function neural network (RBF NN) in each step of the backstepping procedure. Frequency distributed model of a fractional integrator and Lyapunov stability theory are used for ensuring asymptotic stability of the overall closed-loop system under input dead-zone and saturation. Moreover, the parameter update laws with incommensurate fractional order are used in the controller to compensate unknown nonlinearities. Two simulation results are presented at the end to ensure the efficacy of the proposed scheme.

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Adaptive neural control for an uncertain fractional‐order rotational mechanical system using disturbance observer

Cited by 41 publications

References 51 publications

Adaptive Control of a Class of Incommensurate Fractional Order Nonlinear Systems With Input Dead-Zone

Adaptive Control of a Class of Incommensurate Fractional Order Nonlinear Systems With Input Dead-Zone

Fractional-order sliding mode control for a class of uncertain nonlinear systems based on LQR

Adaptive Neural Network Control of a Class of Fractional Order Uncertain Nonlinear MIMO Systems with Input Constraints

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