Adaptive control of hydraulic position servo system using output feedback

Ren, Hang; Gong, Pei-Fen

doi:10.1177/0959651817708487

Cited by 20 publications

(13 citation statements)

References 24 publications

(23 reference statements)

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“…According to equation (12), Lemma 4, and the definition ofs, we can get where h = 2 1Àa 1 l 1 l a 1 + 2 1Àa 2 l 2 l a 2 + l. Then, we have…”

Section: Declaration Of Conflicting Interestsmentioning

confidence: 99%

“…Various control methods have been put forward for the trajectory tracking control of hydraulic actuators, such as adaptive control, 4,5 robust control, 6,7 adaptive robust control, 8,9 passivity-based control, 10 and output feedback control. 11,12 These approaches required a nominal system model, of which parameters were usually derived based on an offline identification.…”

Section: Introductionmentioning

confidence: 99%

“…However, for an excavator, it is difficult to obtain a useful nominal model for controller design due to the following reasons: 1,2,[12][13][14][15] (1) accurate characteristic parameters for proportional directional valves are hard to obtain due to the nonlinear flow gain, hysteresis, dead zone, saturation, and the complex dynamic flow coupling; (2) change in work temperature, aging of hydraulic components, and wear of the mechanical part will result in variations in system parameters; and (3) there is a complex interaction between the excavator and the environment, and even worse, the unknown interaction force often varies in a broad range. Therefore, all aforementioned nominal-model-based methods [4][5][6][7][8][9][10][11][12] are not suitable for hydraulic excavators. Approaches that are not based on a nominal model, such as neural-network-based control, may be a better choice.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Adaptive neural finite-time trajectory tracking control of hydraulic excavators

Wang

2018

Proceedings of the Institution of Mechanical Engineers, Part I:

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This article is focused on the high-performance trajectory tracking control of single actuator of a hydraulic excavator. A novel adaptive neural finite-time controller without tedious offline parameter identification and the complex backstepping scheme is put forward. By employing a coordinate transform, the original system can be represented in a canonical form. Consequently, the control objective is retained by controlling the transformed system, which allows a simple controller design without using backstepping. To estimate the immeasurable states of the transformed system, a high-order sliding mode observer is employed, of which observation error is guaranteed to be bounded in finite time. To guarantee finite-time trajectory tracking performance, an adaptive neural finite-time controller based on neural network approximation and terminal sliding mode theory is synthesized. During its synthesis, an echo state network is used to approximate the lumped uncertain system functions, and it guarantees an improved approximation with online-updated output weights. Besides, to handle the lumped uncertain nonlinearities resulting from observation error and neural approximation error, a robust term is employed. The influences of the uncertain nonlinearities are restrained with a novel parameter adaption law, which estimates and updates the upper bound of the lumped uncertain nonlinearities online. With this novel controller, the finite-time trajectory tracking error convergence is proved theoretically. The superior performance and the practical applicability of the proposed method are verified by comparative simulations and experiments.

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“…According to equation (12), Lemma 4, and the definition ofs, we can get where h = 2 1Àa 1 l 1 l a 1 + 2 1Àa 2 l 2 l a 2 + l. Then, we have…”

Section: Declaration Of Conflicting Interestsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive neural finite-time trajectory tracking control of hydraulic excavators

Wang

2018

Proceedings of the Institution of Mechanical Engineers, Part I:

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“…With growing interests of high-precision control systems, how to develop efficient control approaches to counteract the adverse effects, caused by various uncertainties, is an active topic in both the control theory and application [1], for example, robust control [2], sliding model control [3,4], adaptive control [5,6], backstepping control [7,8], and disturbance estimation-based compound control [8][9][10][11][12]. The effectiveness of these control methods has been proved by their applications in some industrial areas.…”

Section: Introductionmentioning

confidence: 99%

An RBFNN‐Based Direct Inverse Controller for PMSM with Disturbances

Shi

2018

Complexity

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Considering the system uncertainties, such as parameter changes, modeling error, and external uncertainties, a radial basis function neural network (RBFNN) controller using the direct inverse method with the satisfactory stability for improving universal function approximation ability, convergence, and disturbance attenuation capability is advanced in this paper. The weight adaptation rule of the RBFNN is obtained online by Lyapunov stability analysis method to guarantee the identification and tracking performances. The simulation example for the position tracking control of PMSM is studied to illustrate the effectiveness and the applicability of the proposed RBFNN-based direct inverse control method.

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“…32,[38][39][40] Indeed, the DABsC is successfully applied in improving the robustness of a variety of control techniques such as sensorless speed control of induction motor drive, electro hydraulic actuator and other systems. 31,36,[41][42][43][44] This technique is used to ensure a high accuracy control of the outputs' (pressure and flow) variations in transient and steady states of the water pumping station. Based on the highlighted results, the purpose of this article is the experimental validation of DABsC applied to the water pumping station using MATLAB-Simulink (R2011b ''7.13.0.564'') environment and the dSPACE DS1104 card based on real-time data acquisition control system.…”

Section: Introductionmentioning

confidence: 99%

Discrete-time adaptive backstepping control: Application to pumping station

Chakchouk

Chrouta

Regaya

et al. 2017

Proceedings of the Institution of Mechanical Engineers, Part I:

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This article proposes an application of a discrete-time adaptive backstepping control strategy for a hydraulic process pumping station. The proposed solution leads to improved control system performances in terms of pressure and flow tracking in transient and standstill operation and improvement of pressure response time. The proposed design methodology is based on accurate model for pumping station, which is developed in previous works using fuzzy-C means algorithm. The control law design is based on discrete-time adaptive backstepping control, which is developed in the sense of Lyapunov stability theory using sign function, in order to satisfy various objectives of a stable pumping station like the asymptotic stability of the tracking error. To validate the proposed solution, simulation and experimental tests are made and analyzed. Compared to the conventional proportional-integral approach, the results show that the discrete-time adaptive backstepping control allows exhibiting excellent transient response over a wide range of operating conditions and especially is easier to be implemented in practice.

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Adaptive control of hydraulic position servo system using output feedback

Cited by 20 publications

References 24 publications

Adaptive neural finite-time trajectory tracking control of hydraulic excavators

Adaptive neural finite-time trajectory tracking control of hydraulic excavators

An RBFNN‐Based Direct Inverse Controller for PMSM with Disturbances

Discrete-time adaptive backstepping control: Application to pumping station

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