4 research outputs found
Task-space coordinated tracking of multiple heterogeneous manipulators via controller-estimator approaches
This paper studies the task-space coordinated tracking of a time-varying
leader for multiple heterogeneous manipulators (MHMs), containing redundant
manipulators and nonredundant ones. Different from the traditional coordinated
control, distributed controller-estimator algorithms (DCEA), which consist of
local algorithms and networked algorithms, are developed for MHMs with
parametric uncertainties and input disturbances. By invoking differential
inclusions, nonsmooth analysis, and input-to-state stability, some conditions
(including sufficient conditions, necessary and sufficient conditions) on the
asymptotic stability of the task-space tracking errors and the subtask errors
are developed. Simulation results are given to show the effectiveness of the
presented DCEA.Comment: 17 pages, 7 figures, Journal of the Franklin Institut
Neuro-adaptive distributed control with prescribed performance for the synchronization of unknown nonlinear networked systems
This paper proposes a neuro-adaptive distributive cooperative tracking
control with prescribed performance function (PPF) for highly nonlinear
multi-agent systems. PPF allows error tracking from a predefined large set to
be trapped into a predefined small set. The key idea is to transform the
constrained system into unconstrained one through transformation of the output
error. Agents' dynamics are assumed to be completely unknown, and the
controller is developed for strongly connected structured network. The proposed
controller allows all agents to follow the trajectory of the leader node, while
satisfying necessary dynamic requirements. The proposed approach guarantees
uniform ultimate boundedness of the transformed error and the adaptive neural
network weights. Simulations include two examples to validate the robustness
and smoothness of the proposed controller against highly nonlinear
heterogeneous networked system with time varying uncertain parameters and
external disturbances
Adaptive synchronisation of unknown nonlinear networked systems with prescribed performance
This paper proposes an adaptive tracking control with prescribed performance
function for distributive cooperative control of highly nonlinear multi-agent
systems. The use of such approach confines the tracking error within a large
predefined set to a predefined smaller set. The key idea is to transform the
constrained system into unconstrained one through the transformation of the
output error. Agents' dynamics are assumed unknown, and the controller is
developed for a strongly connected structured network. The proposed controller
allows all agents to follow the trajectory of the leader node, while satisfying
the necessary dynamic requirements. The proposed approach guarantees uniform
ultimate boundedness for the transformed error as well as a bounded adaptive
estimate of the unknown parameters and dynamics. Simulations include two
examples to validate the robustness and smoothness of the proposed controller
against highly nonlinear heterogeneous multi-agent system with uncertain
time-variant parameters and external disturbances. Keywords: Prescribed
performance, Transformed error, Multi-agents, Distributed adaptive control,
Adaptive Consensus, Transient, Steady-state error, Semi-global asymptotic
stability, uniformly ultimately bounded, Nonlinear Networked Systems,
Distributed Control, Robustness.Comment: arXiv admin note: text overlap with arXiv:1802.0725
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