775 research outputs found
Distributed Optimal State Consensus for Multiple Circuit Systems with Disturbance Rejection
This paper investigates the distributed optimal state consensus problem for an electronic system with a group of circuit units, where the dynamics of each unit is modeled by a Chua's circuit in the presence of disturbance generated by an external system. By means of the internal model approach and feedback control, a compensator-based continuous-time algorithm is proposed to minimize the sum of all cost functions associated with each individual unit in a cooperative manner. Supported by convex analysis, graph theory and Lyapunov theory, it is proved that the proposed algorithm is exponentially convergent. Compared with the centralized algorithms, the proposed protocol possesses remarkable superiority in improving scalability and reliability of multiple circuit systems. Moreover, we also study the distributed uncertain optimal state consensus problem and a linear regret bound is obtained in this case. Finally, a state synchronization example is provided to validate the effectiveness of the proposed algorithms
Uncertain Multi-Agent Systems with Distributed Constrained Optimization Missions and Event-Triggered Communications: Application to Resource Allocation
This paper deals with solving distributed optimization problems with equality
constraints by a class of uncertain nonlinear heterogeneous dynamic multi-agent
systems. It is assumed that each agent with an uncertain dynamic model has
limited information about the main problem and limited access to the
information of the state variables of the other agents. A distributed algorithm
that guarantees cooperative solving of the constrained optimization problem by
the agents is proposed. Via this algorithm, the agents do not need to
continuously broadcast their data. It is shown that the proposed algorithm can
be useful in solving resource allocation problems
Fixed-time Distributed Optimization under Time-Varying Communication Topology
This paper presents a method to solve distributed optimization problem within
a fixed time over a time-varying communication topology. Each agent in the
network can access its private objective function, while exchange of local
information is permitted between the neighbors. This study investigates first
nonlinear protocol for achieving distributed optimization for time-varying
communication topology within a fixed time independent of the initial
conditions. For the case when the global objective function is strictly convex,
a second-order Hessian based approach is developed for achieving fixed-time
convergence. In the special case of strongly convex global objective function,
it is shown that the requirement to transmit Hessians can be relaxed and an
equivalent first-order method is developed for achieving fixed-time convergence
to global optimum. Results are further extended to the case where the
underlying team objective function, possibly non-convex, satisfies only the
Polyak-\L ojasiewicz (PL) inequality, which is a relaxation of strong
convexity.Comment: 25 page
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