Output Feedback Control of Uncertain Hydraulic Servo Systems

Na, Jing; Liu, Yunpeng; Huang, Yingbo; Gao, Guanbin; Chen, Qiang

doi:10.1109/tie.2019.2897545

Cited by 134 publications

(92 citation statements)

References 35 publications

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“…Now, if we combine Equations (3), (5), (6), and (8), the bilinear-parameter system in (1) can be represented conveniently as the following regression-like model…”

Section: Statement Of the Problem And The Am-lsi Algorithmmentioning

confidence: 99%

“…In engineering practice, a great emphasis is placed on the reliability and accuracy of the system models since they are normally employed to design and analyze the behavior of the systems. [6][7][8] This means that good mathematical models must be able to accurately describe the dynamic characteristics of the systems. [9][10][11] Nonlinear systems are generally considered to be linearizable to some extent under given inputs.…”

Section: Introductionmentioning

confidence: 99%

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Maximum likelihood least squares‐based iterative methods for output‐error bilinear‐parameter models with colored noises

Chen

Ding

Lin

et al. 2020

Intl J Robust & Nonlinear

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This article is concerned with the parameter identification of output-error bilinear-parameter models with colored noises from measurement data. An auxiliary model least squares-based iterative method is developed through the overparameterization model. It examines the difficulty of estimating the overparameterized vector, which usually presents a heavy computational burden in the identification process. To overcome this drawback, a parameter separation technique is introduced and the nonlinear model is reformulated as a refined identification model through eliminating the crossmultiplying terms. In this regard, a parameter separation least squares-based iterative (PS-LSI) algorithm is derived by avoiding estimating the redundant parameters. On the basis of the PS-LSI algorithm, we derive a maximum likelihood least squares-based iterative method to further improve the numerical accuracy. The identification is dependent on the formulation of a pseudolinear regression relationship, which contains two linear prefilters constructed from the system and noise models. The performance of this proposed method is confirmed by the numerical simulations as well as direct comparisons with other existing algorithms.

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“…Now, if we combine Equations (3), (5), (6), and (8), the bilinear-parameter system in (1) can be represented conveniently as the following regression-like model…”

Section: Statement Of the Problem And The Am-lsi Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Maximum likelihood least squares‐based iterative methods for output‐error bilinear‐parameter models with colored noises

Chen

Ding

Lin

et al. 2020

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

show abstract

“…There are two kinds of control forms for synchronization drive: closed-loop control and open-loop control. Some closed-loop control methods in hydraulic systems are developed, such as backstepping control, [7][8][9] robust adaptive control, [10][11][12] unknown dynamics estimator control, 13,14 and achieve very high control precision by feedback and dynamics estimation. However, closedloop control for hydraulic system is very complex and costly, at least including servo valves or proportional valves, controller, various sensors, and is not suitable for severe environment (e.g.…”

Section: Introductionmentioning

confidence: 99%

A new hydraulic synchronous scheme in open-loop control: Load-sensing synchronous control

Ding

Liu

Zhao

2020

Measurement and Control

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At present, open-loop synchronous control for hydraulic systems widely using synchronous valves has low synchronous accuracy due to weak anti-bias capacity, and is hard to adjust synchronization velocity, so it could not meet the requirements of accurate synchronization under severe conditions. In this paper, a load-sensing synchronous control is developed to obtain accurate synchronization in open-loop control, which is made up of a load-sensing unit and a synchronous valve. In the load-sensing loop, a load-sensing pump supplies pressure and flow required by the system through pressure closed-loop control, and a load-sensing valve could improve the capability of anti-offset loads by pressure compensation. The synchronous valve is between the load-sensing pump and load-sensing valve, to achieve equal distribution of flow supplied by the pump, which could improve the divider accuracy of the load-sensing system. A test system within load-sensing synchronous control is established, and then comparison experiments under different partial loads are carried out. The experimental results show that, compared with the traditional synchronous valve control, the load-sensing synchronous control has the advantages of higher synchronous precision, higher energy efficiency and also the ability of velocity regulation. Load-sensing synchronous control has the potential of high precision synchronization control in server environments.

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“…Although there exist a few of adaptive finite control algorithms which have been developed for some special systems and bench tested with simulations and applications [33][34][35], it is very hard to extend the research results to the general systems or algorithms. The key issue is how to systematically obtain finite time adaptive law of neural networks [9,36] weight from finite time convergence of close loop systems [37]. In regard to neural network based such adaptive control to the authors' best knowledge, there are a few results about finite time adaptive neural networks control.…”

Section: Introductionmentioning

confidence: 99%

Homeomorphism Mapping Based Neural Networks for Finite Time Constraint Control of a Class of Nonaffine Pure‐Feedback Nonlinear Systems

Zhang

Zhu

et al. 2019

Complexity

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This paper proposes a new scheme for solving finite time neural networks adaptive tracking control issue for the nonaffine pure-feedback nonlinear system. The procedure, based on homeomorphism mapping and backstepping, effectively deals with constraint control and design difficulty induced by pure-feedback structure. The most outstanding novelty is that finite time adaptive law is proposed for training weights of neural networks. Furthermore, by combining finite time adaptive law and Lyapunov-based arguments, a valid finite time adaptive neural networks controller design algorithm is presented to ensure that system is practical finite stable (PFS) rather than uniformly ultimately bounded (UUB). Because of using the finite time adaptive law to training weights of neural networks, the closed-loop error system signals are in assurance of bounded in finite time. Benchmark simulations have well demonstrated effectiveness and efficiency of the proposed approach.

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Output Feedback Control of Uncertain Hydraulic Servo Systems

Cited by 134 publications

References 35 publications

Maximum likelihood least squares‐based iterative methods for output‐error bilinear‐parameter models with colored noises

Maximum likelihood least squares‐based iterative methods for output‐error bilinear‐parameter models with colored noises

A new hydraulic synchronous scheme in open-loop control: Load-sensing synchronous control

Homeomorphism Mapping Based Neural Networks for Finite Time Constraint Control of a Class of Nonaffine Pure‐Feedback Nonlinear Systems

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