Fractional-order $$PI^{\lambda }D$$ sliding mode control for hypersonic vehicles with neural network disturbance compensator

Sheng, Yongzhi; Bai, Weijie; Xie, Yuwei

doi:10.1007/s11071-020-06046-y

Cited by 34 publications

(17 citation statements)

References 28 publications

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“…In addition, fractional order controllers have been found to obtain more control options and flexibility than integer order controllers. The fractional order proportional-integralderivative (PI λ D µ ) controller is derived from the conventional PID controller by extending the integral and derivative orders from 1 to real numbers [18,19]. It is reported that a PI λ D µ controller can provide more flexibility and better control performance than a PID controller [20,21].…”

Section: Introductionmentioning

confidence: 99%

A Look-Up Table Based Fractional Order Composite Controller Synthesis Method for the PMSM Speed Servo System

et al. 2022

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Considering the performance requirements in actual applications, a look-up table based fractional order composite control scheme for the permanent magnet synchronous motor speed servo system is proposed. Firstly, an extended state observer based compensation scheme was adopted to suppress the motor parametric uncertainties and convert the speed servo plant into a double-integrator model. Then, a fractional order proportional-derivative (PDμ) controller was adopted as the speed controller to provide the optimal step response performance for the servo system. A universal look-up table was established to estimate the derivative order of the PDμ controller, according to the optimal samples collected by an improved differential evolution algorithm. With the look-up table, the optimal PDμ controller can be tuned analytically. Simulation and experimental results show that the servo system using the composite control scheme can achieve optimal tracking performance and has robustness to the motor parametric uncertainties and disturbance torques.

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Section: Introductionmentioning

confidence: 99%

A Look-Up Table Based Fractional Order Composite Controller Synthesis Method for the PMSM Speed Servo System

et al. 2022

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“…For the external disturbances, when the upper bounds of the disturbances are known, a robust controller is designed in [9,10] to suppress the influence of disturbances. When the upper bounds of the disturbances exist but are unknown, the disturbance observer (DOB) based on Lyapunov method is designed in [11][12][13]. Furthermore, the active disturbance rejection control is pro-posed in [14,15], where the lumped disturbances including system uncertainties and external disturbances are set as a new state and further introduced to the extended state observers, such that the lumped disturbances are accurately estimated.…”

Section: Introductionmentioning

confidence: 99%

Recursive Integral Terminal Sliding Mode Control of MEMS Gyroscopes via Composite Neural Learning and Disturbance Observer

Zhang¹,

Wang

2022

Preprint

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The recursive integral terminal sliding mode control based on disturbance observer and composite neural learning is studied in this paper to control the dynamics of MEMS gyroscopes in the presence of system uncertainties and external disturbances. To achieve more accurate approximation accuracy of system uncertainties, the composite neural learning is employed, where the updating law of the weight vector is designed by the tracking error and the prediction error constructed by serial-parallel estimation model. To further improve the system robustness, the recursive integral terminal sliding mode controller is utilized, where faster convergence can be obtained by forcing the system state to start from the sliding mode manifold at the initial time. To deal with external disturbances with unknown upper bounds, the disturbance observer is designed. Furthermore, simulations results are demonstrated to verify that faster convergence and higher tracking accuracy can be achieved under the proposed method.

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“…A new variable fractional-order derivative, applied to the coronavirus epidemic phenomena, has been proposed in [38] where, by using the fixed point theory, the existence and uniqueness of the solution have been demonstrated. In [36] a novel fractional-order PID sliding mode controller with neural network observer is proposed and applied to hypersonic vehicles. Another application of fractional-order PID controllers has been presented in [16] where a particle swarm optimization algorithm is used to search for the optimal parameters of the controllers.…”

Section: Introductionmentioning

confidence: 99%

A new model of Hopfield network with fractional-order neurons for parameter estimation

2021

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In this work, we study an application of fractional-order Hopfield neural networks for optimization problem solving. The proposed network was simulated using a semi-analytical method based on Adomian decomposition,, and it was applied to the on-line estimation of time-varying parameters of nonlinear dynamical systems. Through simulations, it was demonstrated how fractional-order neurons influence the convergence of the Hopfield network, improving the performance of the parameter identification process if compared with integer-order implementations. Two different approaches for computing fractional derivatives were considered and compared as a function of the fractional-order of the derivatives: the Caputo and the Caputo–Fabrizio definitions. Simulation results related to different benchmarks commonly adopted in the literature are reported to demonstrate the suitability of the proposed architecture in the field of on-line parameter estimation.

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Fractional-order $$PI^{\lambda }D$$ sliding mode control for hypersonic vehicles with neural network disturbance compensator

Cited by 34 publications

References 28 publications

A Look-Up Table Based Fractional Order Composite Controller Synthesis Method for the PMSM Speed Servo System

A Look-Up Table Based Fractional Order Composite Controller Synthesis Method for the PMSM Speed Servo System

Recursive Integral Terminal Sliding Mode Control of MEMS Gyroscopes via Composite Neural Learning and Disturbance Observer

A new model of Hopfield network with fractional-order neurons for parameter estimation

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