Adaptive neural network based dynamic surface control for uncertain dual arm robots
Autor: | Dung Tien Pham, Thai Van Nguyen, Hai Xuan Le, Linh Nguyen, Nguyen Huu Thai, Tuan Anh Phan, Hai Tuan Pham, Anh Hoai Duong, Lam Thanh Bui |
---|---|
Rok vydání: | 2019 |
Předmět: |
Lyapunov function
0209 industrial biotechnology Control and Optimization Radial basis function network Artificial neural network Computer science Mechanical Engineering Stability (learning theory) 02 engineering and technology 01 natural sciences symbols.namesake Nonlinear system 020901 industrial engineering & automation Control and Systems Engineering Control theory Modeling and Simulation Control system 0103 physical sciences symbols Robot Electrical and Electronic Engineering Actuator 010301 acoustics Civil and Structural Engineering |
Zdroj: | International Journal of Dynamics and Control. 8:824-834 |
ISSN: | 2195-2698 2195-268X |
Popis: | The paper discusses an adaptive strategy to effectively control nonlinear manipulation motions of a dual arm robot (DAR) under system uncertainties including parameter variations, actuator nonlinearities and external disturbances. It is proposed that the control scheme is first derived from the dynamic surface control (DSC) method, which allows the robot's end-effectors to robustly track the desired trajectories. Moreover, since exactly determining the DAR system's dynamics is impractical due to the system uncertainties, the uncertain system parameters are then proposed to be adaptively estimated by the use of the radial basis function network (RBFN). The adaptation mechanism is derived from the Lyapunov theory, which theoretically guarantees stability of the closed-loop control system. The effectiveness of the proposed RBFN-DSC approach is demonstrated by implementing the algorithm in a synthetic environment with realistic parameters, where the obtained results are highly promising. |
Databáze: | OpenAIRE |
Externí odkaz: |