Resonance control of piezolaminated structures

This paper deals with a third order shear deformation finite element model wich is applied on the active resonance control thin plate/shell laminated structures with integrated piezoelectric layers of patches, acting as sensors and actuators. The finite element model is a single layer tringular nonconforming plate/shell element with 24 degrees of freedom for he generalized displacements, and one electrical potential degree of freedom for each piezoelectric element layer, wich are surface bonded on the laminated. The newwork method is considered to calculate the dynamic response of the laminated sructures forced to vibrate in the first natural frequency. To achieve a mechanism of active control of the structure dynamic response, a feedback control algorithm is used, coupling the sensor and active piezoelectric layers. The model is applied to the solution of one illustrative case, and the results are presented and discussed.

Optimal dynamic control of laminated adaptive structures using a higher order model and a genetic algorithm

This paper deals with a finite element formulation based on the classical laminated plate theory, for active control of thin plate laminated structures with integrated piezoelectric layers, acting as sensors and actuators. The control is initialized through a previous optimization of the core of the laminated structure, in order to minimize the vibration amplitude. Also the optimization of the patches position is performed to maximize the piezoelectric actuator efficiency. The genetic algorithm is used for these purposes. The finite element model is a single layer triangular plate/shell element with 24 degrees of freedom for the generalized displacements, and one electrical potential degree of freedom for each piezoelectric element layer, which can be surface bonded or embedded on the laminate. To achieve a mechanism of active control of the structure dynamic response, a feedback control algorithm is used, coupling the sensor and active piezoelectric layers. To calculate the dynamic response of the laminated structures the Newmark method is considered. The model is applied in the solution of an illustrative case and the results are presented and discussed.

Finit element model for active control of adaptive laminated structures

A finite element formulation for active vibration control of thin plate laminated structures with integrated piezoelectric layers, acting as sensors and actuators in presented. The finite element model is a nonconforming single layer triangular plate/shell element with 18 degrees of freedom for the generalized displacements and one electrical potential degree of freedom for each piezoelectric element layer, and is based on the kirchhoff classical laminated theory. To achieve a mechanism of active control of the structure dynamic response, a feedback control algorithm is used, coupling the sensor and active piezoelectric layers, and Newmark method is used to calculate yhe dynamic response of the laminated structures. The model is applied in the solution of several illustrative cases, and the results are presented and discussed.

Core and patch position optimizations for vibration control of piezolaminated structures

This paper deals with a finite formulation baserd on the classical laminated plate tehory, for active control of thin late laminated structures with integrated piezoelectric layers, acting as sensors and actuators. The control is initialized through a previuos optimization of the core of the laminated structure, in order to minimize the vibration amplitude. Also the optimization of the patches position in performed to maximize the piezoelectric actuator efficiency. the simulating annealing mthod is used for these purposes. The finite element model is a single layer triangular nonconforming plate/shell element with 18 degrees of fredom for the generalized displacements, and one electrical potential degree of freedom for each piezoelectric element layer, wich can be surface bonded or imbedded on the laminate. To achieve a mechanism of active control of the structure dynamic response, a feedback control algorirhm is used, coupling the sensor and active piezoelectric layers. To calculate the dynamic response of the laminated structures the Newmark method is considered. The model is applied in the solution of an illustrative case and the results are presented and discussed.

Intelligent predictive control for thermal comfort and energy savings in public buildings

This talk addresses the problem of controlling a heating ventilating and air conditioning system with the purpose of achieving a desired thermal comfort level and energy savings. The formulation uses the thermal comfort, assessed using the predicted mean vote (PMV) index, as a restriction and minimises the energy spent to comply with it. This results in the maintenance of thermal comfort and on the minimisation of energy, which in most operating conditions are conflicting goals requiring some sort of optimisation method to find appropriate solutions over time. In this work a discrete model based predictive control methodology is applied to the problem. It consists of three major components: the predictive models, implemented by radial basis function neural networks identifed by means of a multi-objective genetic algorithm [1]; the cost function that will be optimised to minimise energy consumption and provide adequate thermal comfort; and finally the optimisation method, in this case a discrete branch and bound approach. Each component will be described, with a special emphasis on a fast and accurate computation of the PMV indices [2]. Experimental results obtained within different rooms in a building of the University of Algarve will be presented, both in summer [3] and winter [4] conditions, demonstrating the feasibility and performance of the approach. Energy savings resulting from the application of the method are estimated to be greater than 50%.

Learning control systems

Novel method of controller (PID) autotuning, involving neural networks and genetic algorithms: to employ neural networks to map the identification measures and controller parameters to objective functions, adapt these models on-line; to employ the genetic algorithm to perform on-line minimization.