Position and Posture Control of Planar Four-Link Underactuated Manipulator Based on Neural Network Model

Wu, Jundong; She, Jinhua; Wang, Yawu; Su, Chun‐Yi

doi:10.1109/tie.2019.2926050

Cited by 22 publications

(9 citation statements)

References 25 publications

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“…Wu et al proposed a fuzzy neural network method to design the vehicle height controller. According to the working principle of the vehicle height adjustment process, a mathematical model of the vehicle system was established based on vehicle system dynamics and the thermodynamic theory of variable mass charging and discharging gas systems [3]. Fuzzy theory and neural network promote the further development of vehicle structure and parameter identification.…”

Section: Introductionmentioning

confidence: 99%

Research on Finite Element Structure of Vehicle Suspension Control Arm Based on Neural Network Sensing Control

Zhu

Jiang

2022

Journal of Sensors

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To improve the adaptive control of the neural network under the influence of vehicle suspension control, the neural network control method is proposed. The specific content of the method analyzes the nonlinear properties of vehicle suspensions, proposes neural network-based adaptive control strategies, and develops neural network-based nonlinear algorithms and neural identifiers. Genetic algorithms perform predictive control of rear suspension through a compensation network. The experimental results show that the model structure is order n = m = 2 , the AN1 network node is 4-6-1, the AN2 network node is 5-4-1, the AN3 network node is 6-4-1, and the learning correction rate is α = 0.90 . In the actual simulation calculation, the number of nodes in the hidden layer of the network is increased, and the minimum number of nodes is chosen to determine the structure of the network, since the control effect obtained is not fundamentally changed. The suspension, which is controlled by the neural network’s adaptive control, has a vibration-reducing effect and is more effective by increasing the control of the rear suspension. The neural network has been shown to be able to effectively control the vehicle’s control arm.

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Section: Introductionmentioning

confidence: 99%

Research on Finite Element Structure of Vehicle Suspension Control Arm Based on Neural Network Sensing Control

Zhu

Jiang

2022

Journal of Sensors

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“…However, in the BPNN network's self-learning process, changes in thresholds or weights make BPNN training more prone to the situation of local minimal solutions and reduce the accuracy of risk values in tourism management systems [ 7 , 8 ]. At the same time, the BPNN network requires more training time, the corresponding slow convergence speed, and poor real-time control performance [ 9 ]. Particle swarm optimization (PSO) is used to improve BPNN to improve BPNN calculation accuracy.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Safety Risk Assessment of Tourism Management System Based on PSO‐BP Neural Network

Guo

2021

Computational Intelligence and Neuroscience

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With the development of science and technology, system management is gradually applied to tourism management. How to correctly assess the security risks of the tourism management system has become an important means to maintain passenger information. The security risk index of the travel management system is input into the PSO-BP network as a sample, and the corresponding risk value of the index is used as the network output. The results show that the error results, accuracy (96.53%), training time (216 s), number of iterations (275 times), and convergence speed are all better than traditional BP network. The relative error of PSO-BP network (0.32%) is better than that of BP network, with 300 iterations, and the error is close to 10–5. The average evaluation accuracy of S based on PSO-BP network is 99.72%, and the average time consumed is 2.512 s. It is superior to the evaluation model based on fuzzy set and entropy weight theory and the evaluation model based on gray correlation analysis and radial basis function neural network. In conclusion, the security risk assessment of the tourism management system based on PSO-BP network can effectively assess the security risk of the tourism management system.

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“…However, the strong nonlinearity of the MAS models may bring difficulties to the design of control systems, and these model-based control design approaches are prone to model errors due to parametric uncertainty and unmodelled dynamics [32], [33]. Therefore, researchers have used a more robust, fault-tolerant and design-friendly backpropagation neural network (BPNN) to capture the nonlinear relationship between the input and output [34], [35]. However, BPNN cannot directly identify systems with multivalued mapping models such as hysteresis.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Empowered Shape Memory Alloy Gripper With Displacement-Force-Stiffness Self-Sensing

Pei

Wang

et al. 2023

IEEE Trans. Ind. Electron.

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Position and Posture Control of Planar Four-Link Underactuated Manipulator Based on Neural Network Model

Cited by 22 publications

References 25 publications

Research on Finite Element Structure of Vehicle Suspension Control Arm Based on Neural Network Sensing Control

Research on Finite Element Structure of Vehicle Suspension Control Arm Based on Neural Network Sensing Control

[Retracted] Safety Risk Assessment of Tourism Management System Based on PSO‐BP Neural Network

A Machine Learning Empowered Shape Memory Alloy Gripper With Displacement-Force-Stiffness Self-Sensing

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