An Anti-Noise Disturbance Fuzzy Neural Dynamics for Manipulability Optimization of Omnidirectional Mobile Redundant Manipulator

Manipulability optimization plays a crucial role in the motion control of omni-directional mobile redundant manipulator (OMRM), since it can reduce the risk of the OMRM to enter into singular postures. However, manipulability is a nonlinear nonconvex function with respect to the joint variables, and thus its efficient optimization is a challenge. In addition, existing manipulability optimization methods rarely consider obstacle avoidance. To solve these limitations, a time-varying quadratic programming problem (TVQP)-based manipulability-optimized obstacle avoidance (MOOA) scheme is proposed, which no longer approximates obstacles as a single point and avoids singularity in the motion control process. To address the problem that the traditional neural network with fixed convergence parameters is not accurate enough for system disturbance or external noise, this paper proposes an anti-noise disturbance fuzzy neural dynamics (AND-FND) model. The model adapts the fuzzy parameters and convergence rate based on error fluctuations, enhancing both robustness and adaptability. Notably, the AND-FND model utilizes membership functions and rules to describe controller parameter variations due to external disturbances and operational complexity. Theoretical analysis demonstrates that the AND-FND model possesses global convergence and strong robustness. Numerical results and physical experiments demonstrate the practicability and advantages of the proposed method compared to the existing techniques.