Neural Network Control of Robot Manipulators and Nonlinear Systems
Neural Network Control of Robot Manipulators and Nonlinear Systems
Cooperative Control of Dynamical Systems: Applications to Autonomous Vehicles
Cooperative Control of Dynamical Systems: Applications to Autonomous Vehicles
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Consensus in leaderless networks of high-order-integrator agents
ACC'09 Proceedings of the 2009 conference on American Control Conference
Consensus of multiagent systems and synchronization of complex networks: a unified viewpoint
IEEE Transactions on Circuits and Systems Part I: Regular Papers
Brief paper: Distributed adaptive control for synchronization of unknown nonlinear networked systems
Automatica (Journal of IFAC)
Finite-time distributed consensus via binary control protocols
Automatica (Journal of IFAC)
Multilayer neural-net robot controller with guaranteed tracking performance
IEEE Transactions on Neural Networks
Neural-network control of nonaffine nonlinear system with zero dynamics by state and output feedback
IEEE Transactions on Neural Networks
Adaptive neural control of uncertain MIMO nonlinear systems
IEEE Transactions on Neural Networks
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II
Consensus of high-order multi-agent systems with large input and communication delays
Automatica (Journal of IFAC)
| Hi-index | 22.15 |
A practical design method is developed for cooperative tracking control of higher-order nonlinear systems with a dynamic leader. The communication network is a weighted directed graph with a fixed topology. Each follower node is modeled by a higher-order integrator incorporating with unknown nonlinear dynamics and an unknown disturbance. The leader node is modeled as a higher-order nonautonomous nonlinear system. It acts as a command generator giving commands only to a small portion of the networked group. A robust adaptive neural network controller is designed for each follower node such that all follower nodes ultimately synchronize to the leader node with bounded residual errors. Moreover, these controllers are distributed in the sense that the controller design for each follower node only requires relative state information between itself and its neighbors. A simulation example demonstrates the effectiveness of the algorithm.