Modeling flexible plate structure system with free-free-clamped-clamped (FFCC) edges using Particle Swarm Optimization
This paper presents the performance of system identification for rectangular flexible plate using conventional parametric modeling approach by Recursive Least Squares (RLS) and intelligent parametric modeling approach by Particle swarm Optimization (PSO). The experimental rig of rectangular flexible plate with free-free-clamped-clamped edges boundary condition designed and fabricated in this research. The experimental study was conducted to collect the input-output data using a vibration flexible plate complete with data acquisition and instrumentation system. The input-output data will be used during developed the system identification. The whole developed model using RLS and PSO were validated using mean squares error (MSE), one step ahead prediction (OSA) and correlation test. The estimated of developed models was found are comparable, acceptable and possible to be used as a platform of controller development later on. As a comparison between developed system, it was found that the Particle Swarm Optimization algorithm has perform better in term of the lowest mean squares error which is 0.00032719.
Genetic modeling of a rectangular flexible plate system with free-free-clamped-clamped (FFCC) edges
This paper presents the performance of system identification of a rectangular flexible plate structure using Least Squares, Recursive Least Squares and Genetic Algorithms techniques. The input - output data are collected through an experimental study using a vibrational flexible plate experimental rig complete with data acquisition and instrumentation system. The experimental rig of two clamped and two free edges (FFCC) flexible plate were fabricated and used throughout this research. All the models developed using Least Squares, Recursive Least Squares and Genetic Algorithms were validated using one step-ahead prediction (OSA), mean squared error (MSE) and correlation tests. It was found that the estimated models using all methods proposed are comparable, acceptable and possible to be used as a platform of controller development and verification to suppress the vibration of flexible plate structure in the future work. Amongst all, it was found that the Genetic Algorithm has performed the best as compared to other methods in estimating the first mode of vibration which is the dominant mode of the structure with 3.63% error. However, for overall estimation, the Least Squares algorithm has achieved the lowest mean squares error (0.00019725) as compared to the Recursive Least Squares and Genetic Algorithms performance.
Optimal placement of actuators for active vibration control of seismic excited tall buildings using a multiple start guided neighbourhood search (MSGNS) algorithm
Abstract Active control devices can be implemented on seismically excited high rise buildings using appropriate active control theory, to reduce structural responses to a desired level. Certain locations of the structure are advantageous for placement of actuators in the sense that these locations effectively reduce the structural responses. Hence, optimal placement of actuators at discrete locations is an important problem that will have significant impact on control of civil structures like high rise buildings, bridges, etc. This optimal placement problem leads to a combinatorial optimisation and is difficult to solve. This paper presents a multi-start meta-heuristic algorithm called multiple start guided neighbourhood search (MSGNS) algorithm, which makes use of the good features of guided local searches like simulated annealing (SA) and tabu search (TS). Four distinct design criteria which influence the active control design are considered in this paper to study the optimal actuator placement problem. The sensitivities of the four optimisation criteria with respect to different earthquake records are explored. Further, in this paper, we deviate from the usual practice of using shear building models (or simple lumped mass model) in active control research for finding optimal actuator locations. Instead, we use detailed finite element models and demonstrate through numerical examples their effectiveness in arriving at the optimal actuator locations. Finally, the superior performance of the proposed MSGNS algorithm over popular meta-heuristic algorithms like GA, SA and TS is demonstrated through numerical experiments.