Sliding-mode observers for state and disturbance estimation in electro-hydraulic systems

Palli, Gianluca; Strano, Salvatore; Terzo, Mario

doi:10.1016/j.conengprac.2018.02.007

Cited by 58 publications

(51 citation statements)

References 32 publications

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“…Considering that the proposed scheme requires full‐state feedback, a second‐order sliding mode differentiator [5] is used to estimate both

\dot{x}

and

\ddot{x}

. It has recently been shown [6] that higher‐order sliding mode observers are among the best options for state estimation in the case of electrohydraulic systems. Regarding the adaptive neural network, Gaussian functions are adopted for the neurons:

φ false(σ; c_{i}, a_{i} false) = \exp falsefalse{- 0.5 false[false(σ - c_{i} false) / a_{i} false]^{2} falsefalse}

.…”

Section: Resultsmentioning

confidence: 99%

Intelligent control for accurate position tracking of electrohydraulic actuators

Santos

Bessa

2019

Electron. lett.

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This letter presents a novel intelligent control scheme for accurate position tracking of electrohydraulic servo actuators. The proposed control law is designed by means of a nonlinear control approach and includes an adaptive neural network to provide the basic intelligent features. Online learning, instead of off-line supervised training, is proposed to update the weight vector of the neural network. Moreover, the adoption of a composite error signal as the only input to the neural network allows a significant reduction in the computational complexity of the algorithm. Rigorous proofs for the boundedness and convergence properties of the closed-loop signals are provided. Experimental results obtained with an electrohydraulic system demonstrate the efficacy of the proposed controller, even considering the highly nonlinear and uncertain plant dynamics.

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“…Considering that the proposed scheme requires full‐state feedback, a second‐order sliding mode differentiator [5] is used to estimate both

\dot{x}

and

\ddot{x}

φ false(σ; c_{i}, a_{i} false) = \exp falsefalse{- 0.5 false[false(σ - c_{i} false) / a_{i} false]^{2} falsefalse}

.…”

Section: Resultsmentioning

confidence: 99%

Intelligent control for accurate position tracking of electrohydraulic actuators

Santos

Bessa

2019

Electron. lett.

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“…It can be seen in the figure that the system consists of three mathematical models, namely the hydraulic actuator model G akt , valve servo model G sk and static force model G P . The transfer function of each part is as Figure 2, Equation 1, Equation 2, and Equation 3 [15].…”

Section: Figure 1 Electro Hydraulic Actuator Modelmentioning

confidence: 99%

Design of a Fractional Order PID Controller for Electric Hydraulic Actuator

Atsari

Halim

2021

KINETIK

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Electric hydraulic actuators are more used especially in industries that demand high levels of accuracy. A common problem with this type of actuator is consistency in fluid flow control. PID controllers can accelerate the achievement of defined output values, eliminate offsets, and reduce maximum overshoots but result in considerable errors. Therefore, it is necessary to design controllers that can reduce errors significantly. In this research, a Fractional Order PID controller is developed to reduce maximum overshoots and steady state. Unlike conventional PID controllers that have three parameters, in the Fractional Order PID controller, there are extra two parameters of the λ and μ. The parameters were selected using the Ziegler Nichols method with a 1st order approach with a delay time. Meanwhile, the λ and μ parameters were selected the best value to make the system response better. The results of the design of the Fractional Order PID controller were evaluated using matlab simulation. The simulation results showed that the Fractional Order PID controller was able to reduce the steady state error response by 0.5 %, and the maximum overshoots by 17.4 %. From this result, it can be noted that the Fractional Order PID controller is better than conventional PID.

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“…The estimation of internal system parameters is an essential topic of research in several areas, from adaptive control methods to machine fault detection and diagnosis, 1‐9 with a significant amount of estimation techniques available in the literature. Usually, the classical parameter estimation techniques consist of three steps: (a) acquiring available information about the system (outputs); (b) comparing the obtained signal with the expected behavior resultant from a parameter‐dependent model; and (c) updating the parameter estimation.…”

Section: Introductionmentioning

confidence: 99%

Robust real‐time parameter estimation for linear systems affected by external noises and uncertainties

Agulhari

Neto

Lacerda

et al. 2020

Adaptive Control & Signal

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Summary A robust adaptive parameter estimation method, based on the application of a full‐order filter capable of rejecting exogenous disturbances, is proposed in this article. A linear matrix inequality condition is proposed to synthesize the desired robust filter, assuming the presence of a known input control with constraints. The filter uses the output of the system to estimate the desired signal that will be employed in the adaptive estimation procedure and, to assure robustness to exogenous noise and unstructured uncertainties, the ℋ∞ guaranteed cost is minimized in the synthesis condition. The filtered signals are then applied to an adaptive procedure to estimate the unknown system's internal parameters, which is also proposed in this article. It is shown that lower values for the ℋ∞ guaranteed cost from the disturbance input to the error output of the filter imply more accurate estimations of the parameters. The efficiency of the proposed estimation technique is illustrated through a simulated model and a physical system has been considered to validate real‐time estimation.

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Sliding-mode observers for state and disturbance estimation in electro-hydraulic systems

Cited by 58 publications

References 32 publications

Intelligent control for accurate position tracking of electrohydraulic actuators

Intelligent control for accurate position tracking of electrohydraulic actuators

Design of a Fractional Order PID Controller for Electric Hydraulic Actuator

Robust real‐time parameter estimation for linear systems affected by external noises and uncertainties

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