Control strategy of hydraulic cylinder based on Deep Reinforcement Learning

Wyrwał, Daniel; Lindner, Tymoteusz; Nowak, Patryk; Białek, Marcin

doi:10.1109/msm49833.2020.9202351

Cited by 7 publications

(3 citation statements)

References 9 publications

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“…study in [2,3] applied adaptive non-linear PI control and cross-coupled pre In hydraulic actuator control designs, [4,5] used fuzzy logic to auto-tun sliding mode parameters, respectively, while [6] employed deep reinforce However, the previously mentioned studies except [2,3] mainly focused on and position tracking while the studies of [2,3] adopting a nonlinear PID p with cross-coupled precompensation still struggled with achieving trackin to the uncertain dynamics of nonlinear EHAs. As a solution to this probl proposes an efficient tracking control strategy to combine fuzzy logic-b control and contour control that can handle the uncertain and nonlinear ch EHAs in excavators and minimize tracking errors for autonomous ex performance of the proposed control algorithms was evaluated by a co multi-physics domains using MATLAB Simulink and Amesim software.…”

Section: Hydraulic Circuit Modelingmentioning

confidence: 99%

“…The study in [2,3] applied adaptive non-linear PI control and cross-coupled precompensation. In hydraulic actuator control designs, [4,5] used fuzzy logic to auto-tune the PID and sliding mode parameters, respectively, while [6] employed deep reinforcement learning. However, the previously mentioned studies except [2,3] mainly focused on motion control and position tracking while the studies of [2,3] adopting a nonlinear PID position control with cross-coupled precompensation still struggled with achieving tracking accuracy due to the uncertain dynamics of nonlinear EHAs.…”

Section: Introductionmentioning

confidence: 99%

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Collaborative Tracking Control Strategy for Autonomous Excavation of a Hydraulic Excavator

Sheikhha

Afsalaghaeinaeini

Seo

2021

The 8th International Electronic Conference on Sensors and Applications

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A hydraulic excavator consists of multiple electrohydraulic actuators (EHA). Due to uncertainties and nonlinearities in EHAs, it is challenging to devise a proper control strategy. To tackle this issue, a major goal of our study is to provide an efficient control strategy to minimize tracking errors of the bucket tip position for autonomous excavation. To accomplish the goal, the study offers a collaboration of PID and fuzzy controllers that are used to compensate for contour errors and achieve accurate actuator position control, respectively. Co-simulation models including control algorithms and hydraulic components were created using Matlab and Amesim to validate the performance of the designed controllers. Simulations indicate that the proposed method enables achieving accurate tracking control for autonomous excavation with small tracking errors despite the nonlinear characteristics of the hydraulic excavator system.

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Section: Hydraulic Circuit Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Collaborative Tracking Control Strategy for Autonomous Excavation of a Hydraulic Excavator

Sheikhha

Afsalaghaeinaeini

Seo

2021

The 8th International Electronic Conference on Sensors and Applications

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“…Reinforcement learning methods are highly effective in path-following control problems, such as the continuous control systems used for online control of hydraulic cylinders [17], intelligent electric motor control [18], and deterministic promotion RL for vehicles [19].…”

Section: Introductionmentioning

confidence: 99%

Model Predictive Control of Permanent Magnet Synchronous Motor Based on State Transition Constraint Method

Gao

Zhang

Fan

et al. 2021

Mathematical Problems in Engineering

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Permanent magnet synchronous motors are widely used and have sufficient development prospects in the drive systems of electric vehicles. Traditional model predictive control (MPC) methods are shown to achieve good control performance by tracking the d- and q-axis current as well as limiting the current amplitude. However, the dynamic response performance and current harmonics during the switching process are not considered in the traditional MPC. Therefore, this paper proposes an MPC that can effectively improve control performance, where the switch transfer sequence in the switch constraint module is considered in the improved model. The state transition error is obtained from the switch constraint module according to the current switch state and the transition probability, after which, the integration into the cost function in which the driving error, tracking error, and constraint error are considered. A reinforcement learning (RL) algorithm is used to obtain the weight coefficient of the transition error term in the constraint module for automatically determining the best switch state for the next control period using the cost function. Simulation tests show that the total harmonic distortion of the phase current based on the improved MPC is 978.4%, less than 2843.0% of the traditional MPC method under 20 Nm at 1000 rpm. The torque response time of the motor is reduced by 0.026 s, whereas the simulation results indicate that the 100 km acceleration performance of an electric vehicle is improved by 9.9%.

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Multi-Objective Optimization of Brake Force Distribution for Enhanced Thermal Management in Hydraulic Brake Systems using PESA 2 Algorithm

Banik,

Nandi

2024

Lecture Notes in Mechanical Engineering

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Control strategy of hydraulic cylinder based on Deep Reinforcement Learning

Cited by 7 publications

References 9 publications

Collaborative Tracking Control Strategy for Autonomous Excavation of a Hydraulic Excavator

Collaborative Tracking Control Strategy for Autonomous Excavation of a Hydraulic Excavator

Model Predictive Control of Permanent Magnet Synchronous Motor Based on State Transition Constraint Method

Multi-Objective Optimization of Brake Force Distribution for Enhanced Thermal Management in Hydraulic Brake Systems using PESA 2 Algorithm

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