Neuro-adaptive Cooperative Tracking Control with Prescribed Performance of Unknown Higher-order Nonlinear Multi-agent Systems
This paper is concerned with the design of a distributed cooperative synchronization controller for a class of higher-order nonlinear multi-agent systems. The objective is to achieve synchronization and satisfy a predefined time-based performance. Dynamics of the agents (also called the nodes) are assumed to be unknown to the controller and are estimated using Neural Networks. The proposed robust neuro-adaptive controller drives different states of nodes systematically to synchronize with the state of the leader node within the constraints of the prescribed performance. The nodes are connected through a weighted directed graph with a time-invariant topology. Only few nodes have access to the leader. Lyapunov-based stability proofs demonstrate that the multi-agent system is uniformly ultimately bounded stable. Highly nonlinear heterogeneous networked systems with uncertain parameters and external disturbances were used to validate the robustness and performance of the new novel approach. Simulation results considered two different examples: single-input single-output and multi-input multi-output, which demonstrate the effectiveness of the proposed controller. Keywords: Prescribed performance, Transformed error, Multi-agents, Neuro-Adaptive, Distributed adaptive control, Consensus, Transient, Steady-state error, Communication graph, Networked Systems, Synchronization, Robustness, Estimation, Estimator, Observer, Filter, operator, small, error, dynamics, kinematics, equilibrium, asymptotic, zero, unknown, time-varying, neighborhood, global, node, agent, Neural Networks, semi-global, stable, stability, uncertain, noise, bias, singular value, matrix, bounded, origin, comparison, rigid body, 3D, space, mapping, Laplacian matrix, directed graph, disturbance, Theory, undirected graph, Inertial measurement units, IMUs, single-input single-output, multi-input multi-output, SISO, MIMO.