Neurocontrol of a Cantilever Beam

نویسنده

  • Daniele Veneziano
چکیده

The civil engineering community is currently moving towards the continuous monitoring of civil structures in order to forecast their unavoidable failure with enough precision. So-called smart technologies seem to be well adapted to this specific task. For a civil structure, such as a bridge or a dam, a monitoring smart system often includes a set of sensors, whose data is passed onto a controller. The latter analyzes the data and outputs commands to a set of actuators that will modify the structure properties in response to the new sensors' environment. Therefore, the structure can continuously adapt to its surrounding environment. Artificial neural networks are electronic devices whose structure resembles the structure of the human brain. Such devices can be trained to output desired signals when fed with specific inputs. Consequently, neural networks can theoretically act as controllers in monitoring smart systems. This thesis first presents artificial neural networks in details, since this topic remains unfamiliar in the civil engineering literature. An entire chapter is also devoted to the training of these artificial neural networks that are likely to be used in civil engineering applications. The thesis then introduces the new concept of neurocontrol, i.e. control using neural networks. Finally, a simulation run under MATLAB applies this concept of neurocontrol to a cantilever beam supporting fluctuating loads. Thesis Supervisor: Jerome J.J. Connor Title: Professor, Department of Civil and Environmental Engineering

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تاریخ انتشار 2013