Acceleration-Layer Robotic Control Based on Neural Dynamics

Abstract

The evolution of robotic control strategies has progressed from velocity-level paradigms to acceleration-layer approaches, driven by the need for improved adaptability, precise force modulation, and efficient real-time execution. This article presents the acceleration layer continuous quad neural dynamics (ACQN) control method, designed to enhance adaptability and precision in both continuum and rigid robots. ACQN provides direct acceleration-layer control by advancing beyond velocity-based control, ensuring smoother operation and greater robustness. To enable implementation in digital environments, ACQN is discretized using two methods: the Verlet method for stability and time-reversibility and a modified implicit left-and-right 3-step (ILR3S) formula with simplified derivative computation using the Euler backward approach to form acceleration layer discrete quad neural dynamics (ADQN) method. Simulations in MATLAB and CoppeliaSim, alongside physical experiments, validate the proposed ADQN method, demonstrating its effectiveness in maintaining joint properties, improving control performance, and extending the robotic manipulator lifespan.