[1]CHANG Guangyu,CHEN Zhifeng,GUO Chunyu,et al.Neural network-based nonsingular terminal sliding mode control of the Stewart platform[J].CAAI Transactions on Intelligent Systems,2024,19(2):353-359.[doi:10.11992/tis.202210004]
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Neural network-based nonsingular terminal sliding mode control of the Stewart platform

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 19 Number of periods: 2024 2 Page number: 353-359 Column: 学术论文—智能系统 Public date: 2024-03-05
Title:
Neural network-based nonsingular terminal sliding mode control of the Stewart platform
Author(s):
CHANG Guangyu1;  CHEN Zhifeng2;  GUO Chunyu3;  PANG Ming1
1. College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China;
2. College of Energy and Architectural Engineering, Harbin University of Commerce, Harbin 150028, China;
3. Qingdao Innovation and Develo
Keywords:
Stewart platform; parallel robot; dynamics; sliding mode control; adaptive control system; neural networks; Lyapunov methods; nonlinear control
CLC:
TP242.2
DOI:
10.11992/tis.202210004
Abstract:
This paper proposes a solution to the six degrees of freedom trajectory tracking problem of the Stewart platform using a nonsingular terminal sliding mode control method based on a neural network. This method is applied to the position and pose control of the Stewart platform. First, a kinematic equation is established by analyzing the position inverse solution and velocity inverse solution of the Stewart platform. Simultaneously, the dynamic equation is established based on the Newton-Euler equation. By integrating the acceleration inverse solution, we obtain the state–space representation of the platform. Subsequently, a nonsingular terminal sliding mode control law is designed using the nonsingular sliding surface function. Considering the approximation characteristics of the radial basis function (RBF) neural network, we employ this network to adaptively approximate the unknown term of the equation. An adaptive law is then designed based on the second method of Lyapunov. Finally, the effectiveness of the controller design is proved through simulations. The simulation results show that the proposed controller that uses an RBF neural network and nonsingular terminal sliding mode outperforms the proportional integral derivative controller in terms of trajectory tracking accuracy and dynamic characteristics.
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