The optimal design and placement of controllers at discrete locations on civil engineering structures is an important control problem that will have impact on the earthquake engineering community. Though algorithms exist for the placement of sensor/actuator systems on continuous structures, the placement of controllers on discrete civil structures is a very difficult problem. Because of the nature of civil structures, it is not possible to place sensors and actuators at any location in the structure. This usually creates a nonlinear constrained mixed integer problem that can be very difficult to solve. However, genetic algorithms have been found to be a powerful too in solving such problems. The introduction of algorithms based on genetic search procedures should increase the rate of convergence and thus reduce the computational time for solving the difficult control problem. In this research task, a real coded genetic algorithm will be used to simultaneously place and design a control system for a civil engineering structure. The proposed method of simultaneously placing and designing sensor/actuators will be compared to a similar work that used a hybrid method. The hybrid method involves using a genetic algorithm to place the sensor/actuators, followed by a gradient-based method to determine the optimal controller gains. The proposed method is more convenient, in that both placement and design is done in the same algorithm, and as such it has a better convergence rate than the hybrid method.
Genetic algorithms will be used for the optimization of feedback gains and controller placement for discrete building structures. The optimal design and placement of controllers at discrete locations is an important problem that will have impact on the control of civil engineering structures. Though algorithms exist for the placement of sensor/actuator systems on continuous structures, the placement of controllers on discrete civil structures is a very difficult problem. Because of the nature of civil structures, it is not possible to place sensors and actuators at any location in the structure. This usually creates a nonlinear constrained mixed integer problem that can be very difficult to solve. Using genetic algorithms in conjunction with gradient based optimization techniques will allow for the simultaneous placement and design of an effective structural control system. The introduction of genetic-based algorithms should increase the rate of convergence and thus reduce the computational time for solving the difficult control problem.
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