7 citations · 28 across the 8 of their papers we have counts for
9 papers
ToMPC: Task-oriented Model Predictive Control via ADMM for Safe Robotic Manipulation
Xinyu Jia, Wenxin Wang, Jun Yang +2
This paper proposes a task-oriented model predictive control (ToMPC) framework for safe and efficient robotic manipulation in open workspaces. The framework unifies collision-free…
Multi-layered Safety of Redundant Robot Manipulators via Task-oriented Planning and Control
Xinyu Jia, Wenxin Wang, Jun Yang +2
Ensuring safety is crucial to promote the application of robot manipulators in open workspaces. Factors such as sensor errors or unpredictable collisions make the environment full…
Composite learning control with modular backstepping and high-order tuners
Tian Shi, Shihua Li, Changyun Wen +1
Adaptive control of strict-feedback nonlinear systems with mismatched uncertainties remains challenging because high-order derivatives of parameter estimates can degrade transient…
Enhanced Robust Motion Control based on Unknown System Dynamics Estimator for Robot Manipulators
Xinyu Jia, Jun Yang, Kaixin Lu +2
To achieve high-accuracy manipulation in the presence of unknown disturbances, we propose two novel efficient and robust motion control schemes for high-dimensional robot manipulat…
Learning robot inverse dynamics using sparse online Gaussian process with forgetting mechanism
Wei Li, Zhiwen Li, Yiqi Liu +1
Online Gaussian processes (GPs), typically used for learning models from time-series data, are more flexible and robust than offline GPs. Both local and sparse approximations of GP…
Composite FORCE learning of chaotic echo state networks for time-series prediction
Yansong Li, Kai Hu, Kohei Nakajima +1
Echo state network (ESN), a kind of recurrent neural networks, consists of a fixed reservoir in which neurons are connected randomly and recursively and obtains the desired output…
Bioinspired composite learning control under discontinuous friction for industrial robots
Yongping Pan, Kai Guo, Tairen Sun +1
Adaptive control can be applied to robotic systems with parameter uncertainties, but improving its performance is usually difficult, especially under discontinuous friction. Inspir…
Generic Evolving Self-Organizing Neuro-Fuzzy Control of Bio-inspired Unmanned Aerial Vehicles
MD. Meftahul Ferdaus, Mahardhika Pratama, Sreenatha G Anavatti +2
At recent times, with the incremental demand of the fully autonomous system, a huge research interest is observed in learning machine based intelligent, self-organizing, and evolvi…
Composite learning control with application to inverted pendulums
Yongping Pan, Lin Pan, Haoyong Yu
Composite adaptive control (CAC) that integrates direct and indirect adaptive control techniques can achieve smaller tracking errors and faster parameter convergence compared with…