# Adaptive neural network based dynamic surface control for uncertain dual arm robots

## TL;DR

This paper introduces an adaptive control method combining dynamic surface control and radial basis function networks to robustly manage uncertain dual arm robot motions, ensuring stability and accurate trajectory tracking.

## Contribution

It presents a novel adaptive control scheme integrating RBFN with DSC for dual arm robots under uncertainties, with theoretical stability guarantees.

## Key findings

- Effective trajectory tracking under uncertainties
- Stable control system with Lyapunov-based adaptation
- Promising simulation results demonstrating robustness

## Abstract

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.