Humanoid robots are increasingly being deployed in complex and unstructured environments, where precise motion tracking and robustness against uncertainties are critical. Recent advances in tracking control of humanoid manipulators highlight the powerful integration of Sliding Mode Control (SMC), Neural Networks (NNs), and Disturbance Observers (DOs) to achieve high-performance and reliable control.
Sliding Mode Control remains a popular nonlinear control strategy due to its inherent robustness against model uncertainties and external disturbances. However, traditional SMC often suffers from the well-known chattering phenomenon, which can degrade performance and cause mechanical wear in humanoid manipulators. To address this issue, researchers are combining SMC with intelligent learning techniques.
Neural Networks are employed to approximate unknown nonlinear dynamics of the humanoid manipulator in real time. By learning system uncertainties and unmodeled dynamics online, NNs significantly reduce reliance on precise mathematical models while improving tracking accuracy. This adaptive capability is particularly valuable for humanoid manipulators operating in dynamic and unpredictable environments.
In parallel, a Disturbance Observer is integrated to estimate external disturbances such as payload variations, friction, and environmental interactions. The observer compensates for these disturbances in real time, allowing the controller to maintain smooth and accurate trajectory tracking even under unexpected conditions.
The hybrid SMC–NN–DO framework offers several advantages: improved robustness, reduced chattering, faster convergence, and enhanced tracking precision. Simulation and experimental studies consistently demonstrate superior performance compared to conventional control schemes, making this approach highly suitable for advanced humanoid manipulation tasks, including human–robot interaction, service robotics, and industrial automation.
As humanoid robots continue to evolve, intelligent control architectures that blend robustness, learning, and disturbance rejection are expected to play a key role in enabling safe, precise, and autonomous robotic systems.
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