Fundamentals of Robotics: Analysis and Control
Fundamentals of Robotics: Analysis and Control
Neural Network Control of Robot Manipulators and Nonlinear Systems
Neural Network Control of Robot Manipulators and Nonlinear Systems
Adaptive Neural Network Control of Robotic Manipulators
Adaptive Neural Network Control of Robotic Manipulators
Adaptive Approximation Based Control: Unifying Neural, Fuzzy and Traditional Adaptive Approximation Approaches (Adaptive and Learning Systems for Signal Processing, Communications and Control Series)
Distributed average consensus with least-mean-square deviation
Journal of Parallel and Distributed Computing
Distributed algorithms for reaching consensus on general functions
Automatica (Journal of IFAC)
SIAM Journal on Control and Optimization
Tracking control for multi-agent consensus with an active leader and variable topology
Automatica (Journal of IFAC)
Adaptive neural control of uncertain MIMO nonlinear systems
IEEE Transactions on Neural Networks
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Adaptive backstepping fuzzy control for nonlinearly parameterized systems with periodic disturbances
IEEE Transactions on Fuzzy Systems
Neural-network-based adaptive leader-following control for multiagent systems with uncertainties
IEEE Transactions on Neural Networks
Brief paper: Distributed adaptive control for synchronization of unknown nonlinear networked systems
Automatica (Journal of IFAC)
Solving a modified consensus problem of linear multi-agent systems
Automatica (Journal of IFAC)
Adaptive cooperative tracking control of higher-order nonlinear systems with unknown dynamics
Automatica (Journal of IFAC)
Adaptive consensus of multi-agents in networks with jointly connected topologies
Automatica (Journal of IFAC)
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II
Distributed adaptive containment control of networked flexible-joint robots using neural networks
Expert Systems with Applications: An International Journal
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A robust adaptive control approach is proposed to solve the consensus problem of multiagent systems. Compared with the previous work, the agent's dynamics includes the uncertainties and external disturbances, which is more practical in real-world applications. Due to the approximation capability of neural networks, the uncertain dynamics is compensated by the adaptive neural network scheme. The effects of the approximation error and external disturbances are counteracted by employing the robustness signal. The proposed algorithm is decentralized because the controller for each agent only utilizes the information of its neighbor agents. By the theoretical analysis, it is proved that the consensus error can be reduced as small as desired. The proposed method is then extended to two cases: Agents form a prescribed formation, and agents have the higher order dynamics. Finally, simulation examples are given to demonstrate the satisfactory performance of the proposed method.