A Precise Positioning Method for a Puncture Robot Based on a PSO-Optimized BP Neural Network Algorithm

Jiang, Guanwu; Luo, Minzhou; Bai, Keqiang; Chen, Saixuan

doi:10.3390/app7100969

Cited by 71 publications

(42 citation statements)

References 21 publications

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“…In control engineering, the multi-layer neural network, represented by back propagation (BP) [29,30] neural network and RBF neural network, are used to approximate nonlinear or unknown functions most widely [31]. Taking RBF neural network as an example, for any given continuous nonlinear function f (x) with f (0) = 0, it can be rewritten as…”

Section: Neural Network Minimum Parameter Learning Methodsmentioning

confidence: 99%

Adaptive Trajectory Tracking Control for Underactuated Unmanned Surface Vehicle Subject to Unknown Dynamics and Time-Varing Disturbances

Wang

Fan

et al. 2018

Applied Sciences

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This paper proposes an adaptive trajectory tracking control strategy for underactuated unmanned surface vehicles subject to unknown dynamics and time-varing external disturbances. In short, the goal of this paper is to provide a control strategy that allows an underactuated unmanned surface vehicle to track a time dependent trajectory. First, a first-order sliding surface is introduced into the design of surge control law to converge to surge tracking error, and then a second-order sliding surface is hired to design yaw control law to deal with sway motion tracking error. Meanwhile, neural network minimum learning parameter method, which has a smaller amount of computation than a multilayer neural network, is employed to preserve the control law robustness against unknown dynamics and time-varing disturbances induced by wind, waves and ocean currents. Furthermore, much effort is made to obtain uniform ultimate bounded stability for the closed-loop control system. Finally, the numerical simulation experiments of straight line and circle trajectory tracking have been given to prove the correctness and feasibility of the proposed control strategy.

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Section: Neural Network Minimum Parameter Learning Methodsmentioning

confidence: 99%

Adaptive Trajectory Tracking Control for Underactuated Unmanned Surface Vehicle Subject to Unknown Dynamics and Time-Varing Disturbances

Wang

Fan

et al. 2018

Applied Sciences

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“…The BP neural network (BPNN) is a representative neural network, which is widely used in many practical systems [24]. The training of neural network is a process that makes the neural network interactive to the external environment in a new way.…”

Section: Bpnn-psomentioning

confidence: 99%

Hybrid Optimal Kinematic Parameter Identification for an Industrial Robot Based on BPNN‐PSO

et al. 2018

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A novel hybrid algorithm that employs BP neural network (BPNN) and particle swarm optimization (PSO) algorithm is proposed for the kinematic parameter identification of industrial robots with an enhanced convergence response. The error model of the industrial robot is established based on a modified Denavit-Hartenberg method and Jacobian matrix. Then, the kinematic parameter identification of the industrial robot is transformed to a nonlinear optimization in which the unknown kinematic parameters are taken as optimal variables. A hybrid algorithm based on a BPNN and the PSO is applied to search for the optimal variables which are used to compensate for the error of the kinematic parameters and improve the positioning accuracy of the industrial robot. Simulations and experiments based on a realistic industrial robot are all provided to validate the efficacy of the proposed hybrid identification algorithm. The results show that the proposed parameter-identification method based on the BPNN and PSO has fewer iterations and faster convergence speed than the standard PSO algorithm.

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“…The pneumatic rotary actuator can realize the change of rotation angle, so it is widely used in the rotation of the mechanical arm, the rotation of the platform, the opening and closing of the valve, and so on [4]. At present, the "two points" control method is the most common method to control the rotation position of a pneumatic rotary actuator, but it is difficult to realize the pinpoint at any position [5]. The pneumatic servo control system can control the motion trajectory of the cylinder and make the positioning accurate and efficient, which has become a hot research topic in pneumatic technology [6].…”

Section: Introductionmentioning

confidence: 99%

Pneumatic Rotary Actuator Position Servo System Based on ADE-PD Control

Zhang

Wei

et al. 2018

Applied Sciences

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In order to accurately control the rotation position of a pneumatic rotary actuator, the flow state of the gas and the motion state of the pneumatic rotary actuator in the pneumatic rotary actuator position servo system are analyzed in this paper. The mathematical model of the system and the experiment platform are established after that. An Adaptive Differential Evolution (ADE) algorithm which adaptively ameliorates the scaling factor and crossover probability in the process of individual evolution is proposed and applied to the parameter optimization of PD controller. The experimental platform is used to compare the controller with Differential Evolution (DE) algorithm and NCD-PID controller. Finally, the characteristics of the system are tested by increasing the inertial load. The experimental results illustrate that system using ADE-PD control strategy has greater position precision and faster response than using DE-PD and NCD-PID strategies, and shows great robustness.

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A Precise Positioning Method for a Puncture Robot Based on a PSO-Optimized BP Neural Network Algorithm

Cited by 71 publications

References 21 publications

Adaptive Trajectory Tracking Control for Underactuated Unmanned Surface Vehicle Subject to Unknown Dynamics and Time-Varing Disturbances

Adaptive Trajectory Tracking Control for Underactuated Unmanned Surface Vehicle Subject to Unknown Dynamics and Time-Varing Disturbances

Hybrid Optimal Kinematic Parameter Identification for an Industrial Robot Based on BPNN‐PSO

Pneumatic Rotary Actuator Position Servo System Based on ADE-PD Control

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