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.

Studies about the atomic capabilities concept for linear control systems in physical multi-agent environments

This paper shows the impact of the atomic capabilities concept to include control-oriented knowledge of linear control systems in the decisions making structure of physical agents. These agents operate in a real environment managing physical objects (e.g. their physical bodies) in coordinated tasks. This approach is presented using an introspective reasoning approach and control theory based on the specific tasks of passing a ball and executing the offside manoeuvre between physical agents in the robotic soccer testbed. Experimental results and conclusions are presented, emphasising the advantages of our approach that improve the multi-agent performance in cooperative systems

Optimal kinematic control of an autonomous underwater vehicle

This note investigates the motion control of an autonomous underwater vehicle (AUV). The AUV is modeled as a nonholonomic system as any lateral motion of a conventional, slender AUV is quickly damped out. The problem is formulated as an optimal kinematic control problem on the Euclidean Group of Motions SE(3), where the cost function to be minimized is equal to the integral of a quadratic function of the velocity components. An application of the Maximum Principle to this optimal control problem yields the appropriate Hamiltonian and the corresponding vector fields give the necessary conditions for optimality. For a special case of the cost function, the necessary conditions for optimality can be characterized more easily and we proceed to investigate its solutions. Finally, it is shown that a particular set of optimal motions trace helical paths. Throughout this note we highlight a particular case where the quadratic cost function is weighted in such a way that it equates to the Lagrangian (kinetic energy) of the AUV. For this case, the regular extremal curves are constrained to equate to the AUV's components of momentum and the resulting vector fields are the d'Alembert-Lagrange equations in Hamiltonian form.

Neural networks for linear control: an analysis

This paper considers the use of radial basis function and multi-layer perceptron networks for linear or linearizable, adaptive feedback control schemes in a discrete-time environment. A close look is taken at the model structure selected and the extent of the resulting parameterization. A comparison is made with standard, nonneural network algorithms, e.g. self-tuning control.

A critique of neural networks for discrete-time linear control

This paper discusses the use of multi-layer perceptron networks for linear or linearizable, adaptive feedback.control schemes in a discrete-time environment. A close look is taken at the model structure selected and the extent of the resulting parametrization. A comparison is made with standard, non-perceptron algorithms, e.g. self-tuning control, and it is shown how gross over-parametrization can occur in the neural network case. Because of the resultant heavy computational burden and poor controller convergence, a strong case is made against the use of neural networks for discrete-time linear control.

H∞ robust control design for unsupported paraplegic standing: experimental evaluation

This paper is concerned with the design of robust feedback H~-control systems for the control of the upright posture of paraplegic persons standing. While the subject stands in a special apparatus, stabilising torque at the ankle joint is generated by electrical stimulation of the paralyzed calf muscles. Since the muscles acting as actuators in this setup show a significant degree of nonlinearity, a robust H~-control design is used. The design approach is implemented in experiments with a paraplegic subject. The results demonstrate good performance and closed loop stability over the whole range of operation.

Bifurcation Analysis of Eigenstructure Assignment Control in a Simple Nonlinear Aircraft Model

Aircraft systems are highly nonlinear and time varying. High-performance aircraft at high angles of incidence experience undesired coupling of the lateral and longitudinal variables, resulting in departure from normal controlled � ight. The construction of a robust closed-loop control that extends the stable and decoupled � ight envelope as far as possible is pursued. For the study of these systems, nonlinear analysis methods are needed. Previously, bifurcation techniques have been used mainly to analyze open-loop nonlinear aircraft models and to investigate control effects on dynamic behavior. Linear feedback control designs constructed by eigenstructure assignment methods at a � xed � ight condition are investigated for a simple nonlinear aircraft model. Bifurcation analysis, in conjunction with linear control design methods, is shown to aid control law design for the nonlinear system.

Closed loop design for a simple nonlinear aircraft model using eigenstructure assignment

Aircraft systems are highly nonlinear and time varying. High-performance aircraft at high angles of incidence experience undesired coupling of the lateral and longitudinal variables, resulting in departure from normal controlled flight. The aim of this work is to construct a robust closed-loop control that optimally extends the stable and decoupled flight envelope. For the study of these systems nonlinear analysis methods are needed. Previously, bifurcation techniques have been used mainly to analyze open-loop nonlinear aircraft models and investigate control effects on dynamic behavior. In this work linear feedback control designs calculated by eigenstructure assignment methods are investigated for a simple aircraft model at a fixed flight condition. Bifurcation analysis in conjunction with linear control design methods is shown to aid control law design for the nonlinear system.

Influencing energy efficient occupant behavior through improved building and control design

There is growing pressure on the construction industry to deliver energy efficient, sustainable buildings but there is evidence to suggest that, in practice, designs regularly fail to achieve the anticipated levels of in-use energy consumption. One of the key factors behind this discrepancy is the behavior of the building occupants. This paper explores how insights from experimental psychology could potentially be used to reduce the gap between the predicted and actual energy performance of buildings. It demonstrates why traditional methods to engage with the occupants are not always successful and proposes a model for a more holistic approach to this issue. The paper concludes that achieving energy efficiency in buildings is not solely a technological issue and that the construction industry needs to adopt a more user-centred approach.

A new social-family model for eating disorders: A European multicentre project using a case-control design

OBJECTIVE: To examine a new socio-family risk model of Eating Disorders (EDs) using path-analyses. METHOD: The sample comprised 1264 (ED patients = 653; Healthy Controls = 611) participants, recruited into a multicentre European project. Socio-family factors assessed included: perceived maternal and parental parenting styles, family, peer and media influences, and body dissatisfaction. Two types of path-analyses were run to assess the socio-family model: 1.) a multinomial logistic path-model including ED sub-types [Anorexia Nervosa-Restrictive (AN-R), AN-Binge-Purging (AN-BP), Bulimia Nervosa (BN) and EDNOS)] as the key polychotomous categorical outcome and 2.) a path-model assessing whether the socio-family model differed across ED sub-types and healthy controls using body dissatisfaction as the outcome variable. RESULTS: The first path-analyses suggested that family and media (but not peers) were directly and indirectly associated (through body dissatisfaction) with all ED sub-types. There was a weak effect of perceived parenting directly on ED sub-types and indirectly through family influences and body dissatisfaction. For the second path-analyses, the socio-family model varied substantially across ED sub-types. Family and media influences were related to body dissatisfaction in the EDNOS and control sample, whereas perceived abusive parenting was related to AN-BP and BN. DISCUSSION: This is the first study providing support for this new socio-family model, which differed across ED sub-types. This suggests that prevention and early intervention might need to be tailored to diagnosis-specific ED profiles.