Trajectory tracking control of a manipulator based on an immune algorithm-optimized neural network in the presence of unknown backlash-like hysteresis.

A neural network control strategy based on the immune algorithm is proposed to address the problem of chattering and trajectory tracking performance degradation of multi-joint manipulators due to unknown backlash-like hysteresis and system uncertainty. The controller is designed based on backstepping sliding mode technology and is suitable for Euler-Lagrange nonlinear systems. Firstly, to deal with the uncertainty problem of the manipulator model, we consider using radial basis function neural networks (RBFNN) to approximate, and on this basis, we add immune algorithms to optimize the RBFNN parameters to improve the approximation ability. Secondly, to compensate for the unknown backlash-like hysteresis error and RBFNN approximation error, an adaptive reaching law is designed to improve the control accuracy and reduce chattering to a certain extent. Finally, taking a 3-DOF manipulator as the research object, based on comparative simulation results, the feasibility and superiority of this control method can be proven. [ABSTRACT FROM AUTHOR]