Utilizing online learning based on echo-state networks for the control of a hydraulic excavator

Park, Jaemann; Cho, Dongsoo; Kim, Seung Hyun; Kim, Young–Bum; Kim, Pan Young; Kim, H. Jin

doi:10.1016/j.mechatronics.2014.10.004

Cited by 31 publications

(11 citation statements)

References 45 publications

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“…In order to continuously improve the operation efficiency and operation quality of excavator, and to expand the application areas of the excavator, autonomous operation has become an important research direction [1,2]. A key problem of the excavator's autonomous operation is how to establish the scientific effective model of the electrohydraulic servo system and improve the trajectory tracking accuracy of the robotic excavator [3,4]. Due to the existence of dead zone, saturation, nonlinear friction, and the nonsymmetry of hydraulic cylinder, the electrohydraulic servo system has strong nonlinear and time-varying characteristics [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Parameter Identification and Control Algorithm of Electrohydraulic Servo System for Robotic Excavator Based on Improved Hammerstein Model

Shen

Cui²,

Feng

et al. 2020

Mathematical Problems in Engineering

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In view of the nonlinearity and time-varying characteristics of the electrohydraulic servo system of the robotic excavator, a nonlinear adaptive identification and control algorithm based on improved Hammerstein model is proposed. The Hammerstein algorithm model can approximate the nonlinear system with enough precision, but for the time-varying systems is not satisfactory. In order to compensate for the influence of time-varying factors, the fuzzy control module is designed to adaptively update the forgetting factor. The experimental results show that the improved Hammerstein model error is about 40.11% less than the classical Hammerstein model error. This proves that the improved Hammerstein model is feasible and effective to describe the electrohydraulic servo system of the robotic excavator.

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Section: Introductionmentioning

confidence: 99%

Parameter Identification and Control Algorithm of Electrohydraulic Servo System for Robotic Excavator Based on Improved Hammerstein Model

Shen

Cui²,

Feng

et al. 2020

Mathematical Problems in Engineering

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“…Feng et al 9 reported a predictive control strategy based on a random model, which improved fuel economy by 44.2–61.9%. In addition, Park et al 10 reported a control strategy utilizing online learning based on echo-state networks. Wu et al 11 proposed a dynamic programming control strategy applied to transportation machinery.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy logic control for a hydraulic hybrid excavator based on torque prediction and genetic algorithm optimization

Zhou

Zhao

Chen

2017

Proceedings of the Institution of Mechanical Engineers, Part D:

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Optimization of the control strategy, whose primary mission is to solve the problem associated with energy management, is an effective way to minimize the fuel consumption of the hydraulic hybrid excavator. As a widely used control strategy, fuzzy logic control can be adopted to realize suboptimal power split with robustness and adaptation, which is one of the most logical approaches for multidomain, nonlinear and time-varying plant. However, the membership functions are difficult to determine according to manual experiences; meanwhile, the optimization-based membership functions are difficult to utilize in real time control. This paper aims to improve the fuel consumption of a hydraulic hybrid excavator by proposing a fuzzy control strategy whose membership functions are optimized by the genetic algorithm, which considers predicted torque of the internal combustion engine (ICE) as a known quantity to realize real time control. The needed torque of the ICE is predicted by superposition of the previous torque. A fuzzy logic control strategy is then designed with membership functions optimized by the genetic algorithm according to the predicted needed torque to achieve better performance. Finally, a numerical experiment is carried out to verify the proposed control strategy.

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“…Busquets and Ivantysynova 17 put forward an adaptive control based on back stepping. In addition, Kalmari et al 18 and Park et al 19 studied several kinds of nonlinear model-based predictive control algorithm. The common feature of the aforementioned algorithm is that the system nonlinearity was considered and the nonlinear models were used.…”

Section: Introductionmentioning

confidence: 99%

Research on hydraulic pump displacement control using PI and feed-forward compensation

Song

Wang

Ren³

et al. 2017

Advances in Mechanical Engineering

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The pump-displacement-controlled actuator offers a very promising alternative and energy-saving solution for many applications in construction machines. It has proven to be with high fuel economy by eliminating the metering losses associated with hydraulic valves. However, actuator oscillation is observed while working with fluctuated velocity or dynamic loads due to slow dynamic response and low tracking accuracy of the pump-controlled actuator. To improve control quality, a new proportional-integral algorithm combined with feed-forward compensation is put forward in this article. Based on the analysis of the swash plate adjustment mechanism, the state-space equation is established. And then, simulation and experimental studies were carried out. Both simulation and experimental results show that the new controller based on proportional-integral algorithm with feed-forward compensation achieves higher dynamic response and tracking accuracy than the conventional proportional-integral controller. And the actuator oscillation is eliminated.

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Utilizing online learning based on echo-state networks for the control of a hydraulic excavator

Cited by 31 publications

References 45 publications

Parameter Identification and Control Algorithm of Electrohydraulic Servo System for Robotic Excavator Based on Improved Hammerstein Model

Parameter Identification and Control Algorithm of Electrohydraulic Servo System for Robotic Excavator Based on Improved Hammerstein Model

Fuzzy logic control for a hydraulic hybrid excavator based on torque prediction and genetic algorithm optimization

Research on hydraulic pump displacement control using PI and feed-forward compensation

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