Path tracking control of electromechanical micro-positioner by considering control effort of the system

Gharib, Mohammad Reza; Koochi, Ali; Ghorbani, Mojtaba

doi:10.1177/0959651820953275

Cited by 7 publications

(6 citation statements)

References 26 publications

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“…The essence of regression network is dynamic system, and the most famous representative is Hapfield network. Compared with traditional algorithm-based identification methods, neural network system identification has the following characteristics [4][5].…”

Section: System Identification Based On Neural Networkmentioning

confidence: 99%

Application of neural network algorithm in electromechanical automatic control system

2023

Second International Conference on Electronic Information Technology (EIT 2023)

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In order to understand the application effect of electromechanical automatic control system, a neural network algorithm in the application of electromechanical automatic control system is proposed. This paper firstly analyzes the concept of electrical automation and neural network algorithm in detail, and then describes the advantages of neural network algorithm in electrical automatic control. Finally, the application of neural network algorithm in electrical automatic control is analyzed. In this paper, the application of neural network algorithm in electrical engineering automation is analyzed, and some suggestions are provided for the development of electrical engineering automation.

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Section: System Identification Based On Neural Networkmentioning

confidence: 99%

Application of neural network algorithm in electromechanical automatic control system

2023

Second International Conference on Electronic Information Technology (EIT 2023)

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“…The virtual model controller was proposed to control the hexapod robot, and the joint torque was calculated based on the dynamic model [9]. Several advanced control methods such as sliding mode control [10][11][12] and PID controller [13] can be used for robot control based on the model. CPGs are common neural elements for locomotion control in insects.…”

Section: Introductionmentioning

confidence: 99%

A Novel Double-Layered Central Pattern Generator-Based Motion Controller for the Hexapod Robot

et al. 2023

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To implement the various movement control of the hexapod robot, a motion controller based on the double-layered central pattern generator (CPG) is proposed in this paper. The novel CPG network is composed of a rhythm layer and a pattern layer. The CPG neurons are constructed based on Kuramoto nonlinear oscillator. The parameters including the frequency, coupling strength, and phase difference matrix of the CPG network for four typical gaits are planned. The mapping relationship between the signals of the CPG network and the joint trajectories of the hexapod robot is designed. The co-simulations and experiments have been conducted to verify the feasibility of the proposed CPG-based controller. The actual average velocities of the wave gait, the tetrapod gait, the tripod gait, and the self-turning gait are 10.8 mm/s, 25.5 mm/s, 37.8 mm/s and 26°/s, respectively. The results verify that the hexapod robot with the proposed double-layered CPG-based controller can perform stable and various movements.

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“…Based on the radial basis function neural network (RBFNN) and sliding mode control (SMC), Ruan et al developed a RBFNN-SMC for nonlinear electromechanical actuator systems [ 17 ]. Gharib et al designed a PID controller with a feedback linearization technique for path tracking control of a micropositioner [ 18 ]. Nevertheless, the performance and robustness of such model-based control strategies are still limited by the precision of the dynamics model.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Sliding Mode Disturbance Observer and Deep Reinforcement Learning Based Motion Control for Micropositioners

Liang

Xiao

et al. 2022

Micromachines

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The motion control of high-precision electromechanitcal systems, such as micropositioners, is challenging in terms of the inherent high nonlinearity, the sensitivity to external interference, and the complexity of accurate identification of the model parameters. To cope with these problems, this work investigates a disturbance observer-based deep reinforcement learning control strategy to realize high robustness and precise tracking performance. Reinforcement learning has shown great potential as optimal control scheme, however, its application in micropositioning systems is still rare. Therefore, embedded with the integral differential compensator (ID), deep deterministic policy gradient (DDPG) is utilized in this work with the ability to not only decrease the state error but also improve the transient response speed. In addition, an adaptive sliding mode disturbance observer (ASMDO) is proposed to further eliminate the collective effect caused by the lumped disturbances. The micropositioner controlled by the proposed algorithm can track the target path precisely with less than 1 μm error in simulations and actual experiments, which shows the sterling performance and the accuracy improvement of the controller.

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Path tracking control of electromechanical micro-positioner by considering control effort of the system

Cited by 7 publications

References 26 publications

Application of neural network algorithm in electromechanical automatic control system

Application of neural network algorithm in electromechanical automatic control system

A Novel Double-Layered Central Pattern Generator-Based Motion Controller for the Hexapod Robot

Adaptive Sliding Mode Disturbance Observer and Deep Reinforcement Learning Based Motion Control for Micropositioners

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