Towards non-linearly activated ZNN model for constrained manipulator trajectory tracking

As a powerful method for time-varying problems solving, the zeroing neural network (ZNN) is widely applied in many practical applications that can be modeled as time-varying linear matrix equations (TVLME). Generally, existing ZNN models solve these TVLME problems in the ideal no noise situation without inequality constraints, but the TVLME with noises and inequality constraints are rarely considered. Therefore, a non-linear activation function is designed, and based on the non-linear activation function, a non-linearly activated ZNN (NAZNN) model is proposed for solving constrained TVLME (CTVLME) problems. The convergence and robustness of the proposed NAZNN model are verified theoretically, and simulation results further demonstrate the effectiveness and superiority of the NAZNN model in dealing with CTVLME and the constrained robot manipulator trajectory tracking problems. In addition, the wheeled robot trajectory tracking fault problems with physical constraints are also analyzed theoretically, and the proposed NAZNN model is also applied to the manipulator trajectory tracking fault problem, and the experimental results prove that the NAZNN model also deal with the manipulator trajectory tracking fault problem effectively.

Automated summary

The zeroing neural network (ZNN) is a powerful method for solving time-varying problems, widely used in practical applications modeled as time-varying linear matrix equations (TVLME). However, existing ZNN models often neglect noises and inequality constraints in solving TVLME problems. To address this, a non-linearly activated ZNN (NAZNN) model with a designed non-linear activation function is proposed for solving constrained TVLME (CTVLME) problems. The proposed NAZNN model is theoretically verified for convergence and robustness, while simulation and experimental results demonstrate its effectiveness in dealing with CTVLME and trajectory tracking problems.