Output feedback sliding mode control based on adaptive sliding mode disturbance observer

Chen, Yunjun; Chao, Jiang; Dong, Jiuzhi; Zhao, Zhanshan

doi:10.1177/00202940221114491

Cited by 3 publications

(1 citation statement)

References 37 publications

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“…The production process will suffer from the unavoidable output noise or input disturbance [19]. The input disturbance will affect the fast tracking performance of the inner loop controller.…”

Section: Introductionmentioning

confidence: 99%

Two-Degrees-of-Freedom PID Control with Kalman Filter for Engraving Machine System

Dong,

Hao,

Shao

et al. 2023

Actuators

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For an engraving machine system with input dynamic disturbance and output random measurement noise, a two-degrees-of-freedom proportional integral derivative (2-DOF PID) control method based on the Kalman filter is firstly proposed in this paper, which can effectively reject the input disturbance and ensure the set point tracking performance of the controller. The 2-DOF controller consists of a disturbance rejection controller and a set point tracking controller. The disturbance rejection controller is composed of a PID controller based on a disturbance observer and expectation model. The parameters of the set point tracking controller are tuned using a differential evolution algorithm (DE), and the cumulative absolute error value (IAE) is used as the fitness function of the DE algorithm, which can improve the rationality of intelligent parameter tuning. In addition, the Kalman filter is also applied to deal with the output noise to suppress the influence of the output measurement uncertainty. Finally, compared with existing algorithms, the feasibility and superiority of the proposed algorithm are verified using numerical simulation and an experimental test.

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Section: Introductionmentioning

confidence: 99%

Two-Degrees-of-Freedom PID Control with Kalman Filter for Engraving Machine System

Dong,

Hao,

Shao

et al. 2023

Actuators

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Research on Anti-swing of Container Gantry Crane as Hierarchical Sliding Mode Control

Dong

Sun²,

Jia³

et al. 2023

J Fail. Anal. and Preven.

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The Enhanced Wagner–Hagras OLS–BP Hybrid Algorithm for Training IT3 NSFLS-1 for Temperature Prediction in HSM Processes

Méndez,

López-Juárez,

Alcorta García

et al. 2023

Mathematics

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This paper presents (a) a novel hybrid learning method to train interval type-1 non-singleton type-3 fuzzy logic systems (IT3 NSFLS-1), (b) a novel method, named enhanced Wagner–Hagras (EWH) applied to IT3 NSFLS-1 fuzzy systems, which includes the level alpha 0 output to calculate the output y alpha using the average of the outputs y alpha k instead of their weighted average, and (c) the novel application of the proposed methodology to solve the problem of transfer bar surface temperature prediction in a hot strip mill. The development of the proposed methodology uses the orthogonal least square (OLS) method to train the consequent parameters and the backpropagation (BP) method to train the antecedent parameters. This methodology dynamically changes the parameters of only the level alpha 0, minimizing some criterion functions as new information becomes available to each level alpha k. The precursor sets are type-2 fuzzy sets, the consequent sets are fuzzy centroids, the inputs are type-1 non-singleton fuzzy numbers with uncertain standard deviations, and the secondary membership functions are modeled as two Gaussians with uncertain standard deviation and the same mean. Based on the firing set of the level alpha 0, the proposed methodology calculates each firing set of each level alpha k to dynamically construct and update the proposed EWH IT3 NSFLS-1 (OLS–BP) system. The proposed enhanced fuzzy system and the proposed hybrid learning algorithm were applied in a hot strip mill facility to predict the transfer bar surface temperature at the finishing mill entry zone using, as inputs, (1) the surface temperature measured by the pyrometer located at the roughing mill exit and (2) the time taken to translate the transfer bar from the exit of the roughing mill to the entry of the descale breaker of the finishing mill. Several fuzzy tools were used to make the benchmarking compositions: type-1 singleton fuzzy logic systems (T1 SFLS), type-1 adaptive network fuzzy inference systems (T1 ANFIS), type-1 radial basis function neural networks (T1 RBFNN), interval singleton type-2 fuzzy logic systems (IT2 SFLS), interval type-1 non-singleton type-2 fuzzy logic systems (IT2 NSFLS-1), type-2 ANFIS (IT2 ANFIS), IT2 RBFNN, general singleton type-2 fuzzy logic systems (GT2 SFLS), general type-1 non-singleton type-2 fuzzy logic systems (GT2 NSFLS-1), interval singleton type-3 fuzzy logic systems (IT3 SFLS), and interval type-1 non-singleton type-3 fuzzy systems (IT3 NSFLS-1). The experiments show that the proposed EWH IT3 NSFLS-1 (OLS–BP) system presented superior capability to learn the knowledge and to predict the surface temperature with the lower prediction error.

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Output feedback sliding mode control based on adaptive sliding mode disturbance observer

Cited by 3 publications

References 37 publications

Two-Degrees-of-Freedom PID Control with Kalman Filter for Engraving Machine System

Two-Degrees-of-Freedom PID Control with Kalman Filter for Engraving Machine System

Research on Anti-swing of Container Gantry Crane as Hierarchical Sliding Mode Control

The Enhanced Wagner–Hagras OLS–BP Hybrid Algorithm for Training IT3 NSFLS-1 for Temperature Prediction in HSM Processes

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