Swarm intelligence algorithms are applied for optimal control of flexible smart structures bonded with piezoelectric actuators and sensors. The optimal locations of actuators/sensors and feedback gain are obtained by maximizing the energy dissipated by the feedback control system. We provide a mathematical proof that this system is uncontrollable if the actuators and sensors are placed at the nodal points of the mode shapes. The optimal locations of actuators/sensors and feedback gain represent a constrained non-linear optimization problem. This problem is converted to an unconstrained optimization problem by using penalty functions. Two swarm intelligence algorithms, namely, Artificial bee colony (ABC) and glowworm swarm optimization (GSO) algorithms, are considered to obtain the optimal solution. In earlier published research, a cantilever beam with one and two collocated actuator(s)/sensor(s) was considered and the numerical results were obtained by using genetic algorithm and gradient based optimization methods. We consider the same problem and present the results obtained by using the swarm intelligence algorithms ABC and GSO. An extension of this cantilever beam problem with five collocated actuators/sensors is considered and the numerical results obtained by using the ABC and GSO algorithms are presented. The effect of increasing the number of design variables (locations of actuators and sensors and gain) on the optimization process is investigated. It is shown that the ABC and GSO algorithms are robust and are good choices for the optimization of smart structures.
This paper investigates in-line spring–mass systems (
A
n
), fixed at one end and free at the other, with
n
-degrees of freedom (d.f.). The objective is to find feasible in-line systems (
B
n
) that are isospectral to a given system. The spring–mass systems,
A
n
and
B
n
, are represented by Jacobi matrices. An error function is developed with the help of the Jacobi matrices
A
n
and
B
n
. The problem of finding the isospectral systems is posed as an optimization problem with the aim of minimizing the error function. The approach for creating isospectral systems uses the fact that the trace of two isospectral Jacobi matrices
A
n
and
B
n
should be identical. A modification is made to the diagonal elements of the given Jacobi matrix (
A
n
), to create the isospectral systems. The optimization problem is solved using the firefly algorithm augmented by a local search procedure. Numerical results are obtained and resulting isospectral systems are shown for 4 d.f. and 10 d.f. systems.
In this paper, we present a cooperative targetcentric formation control strategy that maintains the dynamic graph connectivity for a system of unmanned aerial vehicles. The connectivity of a graph plays a critical role in dynamic networks of multiple agents since it represents a level of information sharing capability of a system. The connectivity of a network of unmanned systems changes as the state dependent graph evolves over time, revealing the risk of the system being uncontrollable during null connectivity. We show that the existing formation controller fails to make the formation when the network connectivity is lost. We propose a novel formation control strategy which keeps the multi-agent graph connected throughout the system dynamic process. The convergence of the formation controller is shown with the help of the Lyapunov theory. Simulation results validate the effectiveness of the proposed control law.
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