5 research outputs found
Robust optimal control of a nonlinear surface vessel model with parametric uncertainties
This paper presents a fast alternative optimization method for developing a reliable optimal controller that can handle system model parameter uncertainties. The source of uncertainty in this study is identified as hydrodynamic coefficients, which are prone to errors due to the challenges involved in obtaining accurate values. The proposed optimization method utilizes a complex nonlinear ship model provided by Maneuver Modelling Group (MMG) as the reference for the ship motion model. The optimization process is divided into two stages: a blind search followed by bisection optimization, to obtain a robust optimal controller. To demonstrate the effectiveness of the proposed approach, system response analysis and practical tests were performed on Step, M-Turn, and Doublet maneuvers. The results show that the controller parameters obtained from the proposed optimization method are capable of achieving high success rates in controlling a system with uncertain parameters
Distributed robust control of linear multi-agent systems with parameter uncertainties
This article considers the distributed robust control problems of uncertain linear multi-agent systems with undirected communication topologies. It is assumed that the agents have identical nominal dynamics while subject to different norm-bounded parameter uncertainties, leading to weakly heterogeneous multi-agent systems. Distributed controllers are designed for both continuous- and discrete-time multi-agent systems, based on the relative states of neighbouring agents and a subset of absolute states of the agents. It is shown for both the continuous- and discrete-time cases that the distributed robust control problems under such controllers in the sense of quadratic stability are equivalent to the H ∞ control problems of a set of decoupled linear systems having the same dimensions as a single agent. A two-step algorithm is presented to construct the distributed controller for the continuous-time case, which does not involve any conservatism and meanwhile decouples the feedback gain design from the communication topology. Furthermore, a sufficient existence condition in terms of linear matrix inequalities is derived for the distributed discrete-time controller. Finally, the distributed robust H ∞ control problems of uncertain linear multi-agent systems subject to external disturbances are discussed
Cooperative Control Reconfiguration in Networked Multi-Agent Systems
Development of a network of autonomous cooperating vehicles has attracted significant
attention during the past few years due to its broad range of applications in areas
such as autonomous underwater vehicles for exploring deep sea oceans, satellite formations
for space missions, and mobile robots in industrial sites where human involvement
is impossible or restricted, to name a few. Motivated by the stringent specifications
and requirements for depth, speed, position or attitude of the team and the possibility
of having unexpected actuators and sensors faults in missions for these vehicles have
led to the proposed research in this thesis on cooperative fault-tolerant control design of
autonomous networked vehicles.
First, a multi-agent system under a fixed and undirected network topology and subject
to actuator faults is studied. A reconfigurable control law is proposed and the so-called
distributed Hamilton-Jacobi-Bellman equations for the faulty agents are derived. Then,
the reconfigured controller gains are designed by solving these equations subject to the
faulty agent dynamics as well as the network structural constraints to ensure that the
agents can reach a consensus even in presence of a fault while simultaneously the team
performance index is minimized.
Next, a multi-agent network subject to simultaneous as well as subsequent actuator
faults and under directed fixed topology and subject to bounded energy disturbances is considered. An H∞ performance fault recovery control strategy is proposed that guarantees:
the state consensus errors remain bounded, the output of the faulty system behaves
exactly the same as that of the healthy system, and the specified H∞ performance bound
is guaranteed to be minimized. Towards this end, the reconfigured control law gains
are selected first by employing a geometric control approach where a set of controllers
guarantees that the output of the faulty agent imitates that of the healthy agent and the
consensus achievement objectives are satisfied. Then, the remaining degrees of freedom
in the selection of the control law gains are used to minimize the bound on a specified
H∞ performance index.
Then, control reconfiguration problem in a team subject to directed switching topology
networks as well as actuator faults and their severity estimation uncertainties is considered.
The consensus achievement of the faulty network is transformed into two stability
problems, in which one can be solved offline while the other should be solved online
and by utilizing information that each agent has received from the fault detection and
identification module. Using quadratic and convex hull Lyapunov functions the control
gains are designed and selected such that the team consensus achievement is guaranteed
while the upper bound of the team cost performance index is minimized.
Finally, a team of non-identical agents subject to actuator faults is considered. A
distributed output feedback control strategy is proposed which guarantees that agents
outputs’ follow the outputs of the exo-system and the agents states remains stable even
when agents are subject to different actuator faults